pytorch_tensorflow2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyO/kPnRlngM2HxHKoiMn/ml",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wallisonferreira/machine-learning-pavic/blob/main/pytorch_tensorflow2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pytorch"
      ],
      "metadata": {
        "id": "1aahgKpBtbcI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importações"
      ],
      "metadata": {
        "id": "Bw_Wr1hxRbW6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "L7yKpqp-Ola0",
        "outputId": "3e073078-3def-4444-b507-b770c53ab21e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.1.0+cu118'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "torch.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dados"
      ],
      "metadata": {
        "id": "EBKzdP2ZTBzB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weight = 0.7\n",
        "bias = 0.3\n",
        "\n",
        "# create range\n",
        "start = 0\n",
        "end = 1\n",
        "step = 0.02\n",
        "\n",
        "# create X and y\n",
        "#X = torch.arange(start, end, step).unsqueeze(dim=1)\n",
        "X = tf.experimental.numpy.arange(start, end, step)\n",
        "\n",
        "y = weight * X + bias # gerando nossa saída y (output)\n",
        "\n",
        "X[:10], y[:10] #imprimir as 10 primeiras amostras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ECjHfZiTC3k",
        "outputId": "e2961ca1-6a86-4776-9125-8458d25d9894"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(10,), dtype=float64, numpy=\n",
              " array([0.        , 0.02      , 0.04      , 0.06      , 0.08      ,\n",
              "        0.09999999, 0.12      , 0.14      , 0.16      , 0.17999999])>,\n",
              " <tf.Tensor: shape=(10,), dtype=float64, numpy=\n",
              " array([0.3       , 0.314     , 0.328     , 0.342     , 0.356     ,\n",
              "        0.37      , 0.384     , 0.398     , 0.412     , 0.42599999])>)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#split\n",
        "train_split = int(0.8 * len(X))\n",
        "X_train, y_train = X[:train_split], y[:train_split]\n",
        "X_test, y_test = X[train_split:], y[train_split:]\n",
        "\n",
        "len(X_train), len(y_train), len(X_test), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZNtfjpNV6oR",
        "outputId": "c57ca2d8-25ad-40e3-d3a2-88591dccb9ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 40, 10, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_predictions(train_data=X_train,\n",
        "                     train_labels=y_train,\n",
        "                     test_data=X_test,\n",
        "                     test_labels=y_test,\n",
        "                     predictions=None):\n",
        "    plt.figure(figsize=(10,7))\n",
        "    #plot training data\n",
        "    plt.scatter(train_data, train_labels, c=\"b\", s=4, label=\"Training data\")\n",
        "    #plot test data\n",
        "    plt.scatter(test_data, test_labels, c=\"g\", s=4, label=\"Testing data\")\n",
        "\n",
        "    if predictions is not None:\n",
        "        plt.scatter(test_data,predictions, c=\"r\", s=4, label=\"Predictions\")\n",
        "\n",
        "    plt.legend(prop={\"size\":14});"
      ],
      "metadata": {
        "id": "FiAfdqYvWyFe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "NdcxQaGOYRqV",
        "outputId": "620100b0-6ddc-4ea2-e46b-d51784e1c793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.1 Construir o modelo"
      ],
      "metadata": {
        "id": "22RycNH8YyYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LinearRegressionModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        # a partir daqui teremos\n",
        "        self.linear_layer = nn.Linear(in_features=1, out_features=1)\n",
        "\n",
        "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
        "        return self.linear_layer(x)\n",
        "\n",
        "torch.manual_seed(42)\n",
        "model_1 = LinearRegressionModel()\n",
        "model_1, model_1.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZpzy5fUY2b-",
        "outputId": "2a3cc15f-c0b1-4b7c-cc92-b77b14b9f6fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(LinearRegressionModel(\n",
              "   (linear_layer): Linear(in_features=1, out_features=1, bias=True)\n",
              " ),\n",
              " OrderedDict([('linear_layer.weight', tensor([[0.7645]])),\n",
              "              ('linear_layer.bias', tensor([0.8300]))]))"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next(model_1.parameters()).device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lq1JDBSRbMGJ",
        "outputId": "9ebe395f-06c0-4e49-930d-3c3e7cd00ce8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "n3j4fBkvbW05",
        "outputId": "8cbc17fb-954a-4cad-d86a-bcf80b53c2bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set model to GPU\n",
        "model_1.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wORAkEjXby5S",
        "outputId": "e43e78df-2a92-413f-c86f-f71c0e100374"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegressionModel(\n",
              "  (linear_layer): Linear(in_features=1, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.2 Treinar o modelo"
      ],
      "metadata": {
        "id": "WuSUq1mhcLhz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.L1Loss() # seta a função de custo (MAE)\n",
        "optimizer = torch.optim.SGD(model_1.parameters(), lr=0.01) # SGD (Stochastic Gradient Descent)"
      ],
      "metadata": {
        "id": "pFfxjdcHcQLD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "epochs = 1000\n",
        "\n",
        "X_train = X_train.to(device)\n",
        "X_test = X_test.to(device)\n",
        "y_train = y_train.to(device)\n",
        "y_test = y_test.to(device)\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    model_1.train()\n",
        "\n",
        "    # 1 forward\n",
        "    y_pred = model_1(X_train)\n",
        "\n",
        "    # 2 calcular o loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "\n",
        "    # 3 Zero grad optimizer\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # 4 Loss backward\n",
        "    loss.backward()\n",
        "\n",
        "    # 5 Step Optimizer\n",
        "    optimizer.step()\n",
        "\n",
        "    ## Testing\n",
        "    model_1.eval()\n",
        "\n",
        "    with torch.inference_mode():\n",
        "        test_pred = model_1(X_test)\n",
        "        test_loss = loss_fn(test_pred, y_test)\n",
        "\n",
        "    if epoch % 100 == 0:\n",
        "        print(f\"Epoch: {epoch} | Train loss: {loss} | Teste loss: {test_loss}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xB93p9NYdhap",
        "outputId": "497e199c-2c95-43d6-bb3c-97692e57776f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Train loss: 0.5551779866218567 | Teste loss: 0.5739762187004089\n",
            "Epoch: 100 | Train loss: 0.006215683650225401 | Teste loss: 0.014086711220443249\n",
            "Epoch: 200 | Train loss: 0.0012645035749301314 | Teste loss: 0.013801801018416882\n",
            "Epoch: 300 | Train loss: 0.0012645035749301314 | Teste loss: 0.013801801018416882\n",
            "Epoch: 400 | Train loss: 0.0012645035749301314 | Teste loss: 0.013801801018416882\n",
            "Epoch: 500 | Train loss: 0.0012645035749301314 | Teste loss: 0.013801801018416882\n",
            "Epoch: 600 | Train loss: 0.0012645035749301314 | Teste loss: 0.013801801018416882\n",
            "Epoch: 700 | Train loss: 0.0012645035749301314 | Teste loss: 0.013801801018416882\n",
            "Epoch: 800 | Train loss: 0.0012645035749301314 | Teste loss: 0.013801801018416882\n",
            "Epoch: 900 | Train loss: 0.0012645035749301314 | Teste loss: 0.013801801018416882\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PED1yWCuiRxj",
        "outputId": "44fb7f91-e816-4a8a-d9bc-0868238f8e55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('linear_layer.weight', tensor([[0.6968]], device='cuda:0')),\n",
              "             ('linear_layer.bias', tensor([0.3025], device='cuda:0'))])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.eval()\n",
        "\n",
        "with torch.inference_mode():\n",
        "    y_preds = model_1(X_test)\n",
        "\n",
        "plot_predictions(predictions=y_preds.cpu())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "nwwrgQXZics6",
        "outputId": "9ee0205e-3650-49c8-ee3a-0e9dff7a7a9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SAVE\n",
        "from pathlib import Path\n",
        "MODEL_PATH = Path(\"models\")\n",
        "MODEL_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "MODEL_NAME = \"model_1.pth\"\n",
        "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
        "\n",
        "torch.save(obj=model_1.state_dict(), f=MODEL_SAVE_PATH)"
      ],
      "metadata": {
        "id": "Ywk__6Azpmze"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#LOAD\n",
        "loaded_model_1 = LinearRegressionModel()\n",
        "\n",
        "loaded_model_1.load_state_dict(torch.load(MODEL_SAVE_PATH))\n",
        "\n",
        "loaded_model_1.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvFACEKXqQ-k",
        "outputId": "96eee4dd-5792-4157-e20f-07a7da727220"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegressionModel(\n",
              "  (linear_layer): Linear(in_features=1, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model_1.state_dict(), model_1.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WpnlqmzVrk2x",
        "outputId": "82fbf3f4-33b1-4bf8-8a1c-5dd0f7e2446f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(OrderedDict([('linear_layer.weight', tensor([[0.6968]], device='cuda:0')),\n",
              "              ('linear_layer.bias', tensor([0.3025], device='cuda:0'))]),\n",
              " OrderedDict([('linear_layer.weight', tensor([[0.6968]], device='cuda:0')),\n",
              "              ('linear_layer.bias', tensor([0.3025], device='cuda:0'))]))"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model_1.eval()\n",
        "with torch.inference_mode():\n",
        "    loaded_model_1_preds = loaded_model_1(X_test)\n",
        "\n",
        "y_preds == loaded_model_1_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-LpMfTiqor_",
        "outputId": "106d9a8a-937c-4514-b9b9-92d4a2105b92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True],\n",
              "        [True]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tensorflow"
      ],
      "metadata": {
        "id": "PRDDgBvttWJw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# construa o modelo\n",
        "model_tf = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# compile o modelo\n",
        "model_tf.compile(loss=tf.keras.losses.mae,\n",
        "              optimizer=tf.keras.optimizers.SGD(),\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "# treine o modelo\n",
        "model_tf.fit(tf.expand_dims(X, axis=-1), y, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9IqttWptXwS",
        "outputId": "4df9eac6-888d-4634-a47d-989829ed8ad5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 1s 12ms/step - loss: 0.8226 - mae: 0.8226\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7979 - mae: 0.7979\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.7733 - mae: 0.7733\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7486 - mae: 0.7486\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7240 - mae: 0.7240\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.6993 - mae: 0.6993\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.6744 - mae: 0.6744\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.6494 - mae: 0.6494\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.6245 - mae: 0.6245\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.5997 - mae: 0.5997\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.5748 - mae: 0.5748\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.5499 - mae: 0.5499\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.5251 - mae: 0.5251\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.5004 - mae: 0.5004\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.4758 - mae: 0.4758\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.4513 - mae: 0.4513\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.4283 - mae: 0.4283\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.4071 - mae: 0.4071\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.3873 - mae: 0.3873\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.3696 - mae: 0.3696\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.3526 - mae: 0.3526\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.3355 - mae: 0.3355\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.3197 - mae: 0.3197\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.3049 - mae: 0.3049\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2921 - mae: 0.2921\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2803 - mae: 0.2803\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2690 - mae: 0.2690\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2592 - mae: 0.2592\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2505 - mae: 0.2505\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2424 - mae: 0.2424\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2346 - mae: 0.2346\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.2276 - mae: 0.2276\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2211 - mae: 0.2211\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.2156 - mae: 0.2156\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.2103 - mae: 0.2103\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2055 - mae: 0.2055\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2008 - mae: 0.2008\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1963 - mae: 0.1963\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1921 - mae: 0.1921\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1885 - mae: 0.1885\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1853 - mae: 0.1853\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1819 - mae: 0.1819\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.1794 - mae: 0.1794\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1765 - mae: 0.1765\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1746 - mae: 0.1746\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1724 - mae: 0.1724\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1705 - mae: 0.1705\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1687 - mae: 0.1687\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1668 - mae: 0.1668\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1648 - mae: 0.1648\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1635 - mae: 0.1635\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1620 - mae: 0.1620\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1605 - mae: 0.1605\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1589 - mae: 0.1589\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1578 - mae: 0.1578\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1564 - mae: 0.1564\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1557 - mae: 0.1557\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1543 - mae: 0.1543\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1532 - mae: 0.1532\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.1520 - mae: 0.1520\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1507 - mae: 0.1507\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1497 - mae: 0.1497\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.1485 - mae: 0.1485\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1472 - mae: 0.1472\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1464 - mae: 0.1464\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1451 - mae: 0.1451\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1441 - mae: 0.1441\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1432 - mae: 0.1432\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1420 - mae: 0.1420\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1413 - mae: 0.1413\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1401 - mae: 0.1401\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1391 - mae: 0.1391\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1382 - mae: 0.1382\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1371 - mae: 0.1371\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1359 - mae: 0.1359\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1351 - mae: 0.1351\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1340 - mae: 0.1340\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1330 - mae: 0.1330\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1320 - mae: 0.1320\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.1310 - mae: 0.1310\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1303 - mae: 0.1303\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.1292 - mae: 0.1292\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1280 - mae: 0.1280\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1270 - mae: 0.1270\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1260 - mae: 0.1260\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1251 - mae: 0.1251\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1241 - mae: 0.1241\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1231 - mae: 0.1231\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1222 - mae: 0.1222\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1211 - mae: 0.1211\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1201 - mae: 0.1201\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1192 - mae: 0.1192\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1182 - mae: 0.1182\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1171 - mae: 0.1171\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1162 - mae: 0.1162\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1152 - mae: 0.1152\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1142 - mae: 0.1142\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1132 - mae: 0.1132\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1122 - mae: 0.1122\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.1112 - mae: 0.1112\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x795ead34cc40>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_tf.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWBT0kNUvJgv",
        "outputId": "30e97da3-d11f-4653-c131-67d194445ae3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 1)                 2         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2 (8.00 Byte)\n",
            "Trainable params: 2 (8.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_tf.variables, \"#\"*50, model_tf.weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BcU6KP7vkWc",
        "outputId": "d694f019-6973-49d9-b897-6521260e2f88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<tf.Variable 'dense_3/kernel:0' shape=(1, 1) dtype=float32, numpy=array([[0.26267874]], dtype=float32)>,\n",
              "  <tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32, numpy=array([0.53645873], dtype=float32)>],\n",
              " '##################################################',\n",
              " [<tf.Variable 'dense_3/kernel:0' shape=(1, 1) dtype=float32, numpy=array([[0.26267874]], dtype=float32)>,\n",
              "  <tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32, numpy=array([0.53645873], dtype=float32)>])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Trabalhando com dados das aulas anteriores"
      ],
      "metadata": {
        "id": "Zy6UIAd6iuJ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.makedirs('utils_ex', exist_ok=True)\n",
        "!wget -q https://github.com/mafaldasalomao/pavic_treinamento_ml/raw/main/utils/plot.py -O utils_ex/plot.py\n",
        "!wget -q https://github.com/mafaldasalomao/pavic_treinamento_ml/raw/main/utils/samples_generator.py -O utils_ex/samples_generator.py"
      ],
      "metadata": {
        "id": "VV5oeZAKwtot"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import _pickle as pkl\n",
        "import matplotlib.pyplot as plt\n",
        "from utils_ex.samples_generator import make_cubic\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from utils_ex import plot\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "TXZxlOoajT2A"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "iXf-bBHmjBqx",
        "outputId": "440e7668-3353-47c1-e154-154cac61ecfb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### make data"
      ],
      "metadata": {
        "id": "RNuzMSdzj-Kw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x, y = make_cubic(n_samples=100, x_min=-4, x_max=4, a=1, b=0, c=-10, d=0, noise=3)\n",
        "\n",
        "print(x.shape, y.shape)\n",
        "plt.scatter(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "2GBxhgYCj2FK",
        "outputId": "fd01c068-255e-4b6a-d0a2-500b4d22ae0d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 1) (100, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7d0e1af38790>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### rescaling data"
      ],
      "metadata": {
        "id": "WpBRDuW6kVIu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "minmax = MinMaxScaler(feature_range=(-1, 1))\n",
        "x = minmax.fit_transform(x)\n",
        "\n",
        "print(x.min(axis=0), x.max(axis=0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTxgPCQPj6Iq",
        "outputId": "c757caa0-3fd3-4630-de73-f40dc0f2f39f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1.] [1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfKy8N6FkSfZ",
        "outputId": "6bef991d-089a-4ec5-e81e-65cb9fa9aa8f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####  x e y são convertidos do tensor do numpy para o tensor do torch"
      ],
      "metadata": {
        "id": "kAx_Fg7kkjUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x, y = torch.from_numpy(x).float(), torch.from_numpy(y).float()"
      ],
      "metadata": {
        "id": "XBtQkVk1kJ7n"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### NA AULA PASSADA USAMOS O L1LOSS QUE É EQUIVALENTE AO MAE"
      ],
      "metadata": {
        "id": "NUflc-H7lH_z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2 - BUILD MODEL"
      ],
      "metadata": {
        "id": "CyyB22bhktnl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class NeuralNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.dense0 = nn.Linear(in_features=1, out_features=30)\n",
        "        self.dense1 = nn.Linear(30, 30)\n",
        "        self.output = nn.Linear(30, 1)\n",
        "        self.relu   = nn.ReLU()\n",
        "        self.tanh   = nn.Tanh()\n",
        "\n",
        "    def forward(self, x):\n",
        "        # 1 camada\n",
        "        x = self.dense0(x) #  1 -> 30\n",
        "        x = self.relu(x)\n",
        "        # 2 camada\n",
        "        x = self.relu(self.dense1(x)) # 30 -> 30\n",
        "        # 3 camada\n",
        "        out = self.output(x)\n",
        "        return out\n",
        "\n",
        "model_2 = NeuralNetwork() # create instance\n",
        "model_2.to(device)        # set model to GPU"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abhD_K8tlYvj",
        "outputId": "c9e3595c-b0d5-4a70-a24e-cd04012ddfcc"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NeuralNetwork(\n",
              "  (dense0): Linear(in_features=1, out_features=30, bias=True)\n",
              "  (dense1): Linear(in_features=30, out_features=30, bias=True)\n",
              "  (output): Linear(in_features=30, out_features=1, bias=True)\n",
              "  (relu): ReLU()\n",
              "  (tanh): Tanh()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cost function and optimizer\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model_2.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "rRgDnhHIko_O"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "epochs = 5000\n",
        "x = x.to(device)\n",
        "y = y.to(device)\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    model_2.train()\n",
        "    # 1 forward\n",
        "    y_pred = model_2(x)\n",
        "    # 2 calc loss\n",
        "    loss = criterion(y_pred, y)\n",
        "    # 3 zero grad\n",
        "    optimizer.zero_grad()\n",
        "    # 4 loss backward\n",
        "    loss.backward()\n",
        "    # 5 step optim\n",
        "    optimizer.step()\n",
        "\n",
        "    if epoch % 500 == 0:\n",
        "        print( f\"Epoch: {epoch} Train_loss: {loss}\" )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isBXSoy0rMjd",
        "outputId": "64f0842e-cb21-4ee7-e483-84be5fed034d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 Train_loss: 101.60286712646484\n",
            "Epoch: 500 Train_loss: 97.64185333251953\n",
            "Epoch: 1000 Train_loss: 52.63125991821289\n",
            "Epoch: 1500 Train_loss: 4.922182083129883\n",
            "Epoch: 2000 Train_loss: 2.7527084350585938\n",
            "Epoch: 2500 Train_loss: 2.5920586585998535\n",
            "Epoch: 3000 Train_loss: 2.5741095542907715\n",
            "Epoch: 3500 Train_loss: 2.5667788982391357\n",
            "Epoch: 4000 Train_loss: 2.5615105628967285\n",
            "Epoch: 4500 Train_loss: 2.5569121837615967\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### temos que usar .cpu() para trazer o valor de x da GPU de volta para a CPU para que possamos fazer o plot"
      ],
      "metadata": {
        "id": "zGazBJG_tCjk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.scatter(x.cpu(), y.cpu())\n",
        "plt.plot(x.cpu(), model_2(x).cpu().detach().numpy(), c='green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "_zoRbLgYskz7",
        "outputId": "ecc38b76-86c1-42f7-922a-b2d6f41dd922"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7d0dedd3b730>]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regressão venda de vídeo games"
      ],
      "metadata": {
        "id": "e_G59LEswCBV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trabalhando com múltiplos previsores"
      ],
      "metadata": {
        "id": "JWYovLKpwadg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "EZ6eU_RMwBL-"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base = pd.read_csv('games.csv')"
      ],
      "metadata": {
        "id": "Lpnrv0ZlxT8U"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "huYq9YQNxUPK",
        "outputId": "0f4bad8a-7fbf-432b-c807-6ac6405bd45f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Name Platform  Year_of_Release         Genre Publisher  \\\n",
              "0                Wii Sports      Wii           2006.0        Sports  Nintendo   \n",
              "1         Super Mario Bros.      NES           1985.0      Platform  Nintendo   \n",
              "2            Mario Kart Wii      Wii           2008.0        Racing  Nintendo   \n",
              "3         Wii Sports Resort      Wii           2009.0        Sports  Nintendo   \n",
              "4  Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing  Nintendo   \n",
              "\n",
              "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
              "0     41.36     28.96      3.77         8.45         82.53          76.0   \n",
              "1     29.08      3.58      6.81         0.77         40.24           NaN   \n",
              "2     15.68     12.76      3.79         3.29         35.52          82.0   \n",
              "3     15.61     10.93      3.28         2.95         32.77          80.0   \n",
              "4     11.27      8.89     10.22         1.00         31.37           NaN   \n",
              "\n",
              "   Critic_Count User_Score  User_Count Developer Rating  \n",
              "0          51.0          8       322.0  Nintendo      E  \n",
              "1           NaN        NaN         NaN       NaN    NaN  \n",
              "2          73.0        8.3       709.0  Nintendo      E  \n",
              "3          73.0          8       192.0  Nintendo      E  \n",
              "4           NaN        NaN         NaN       NaN    NaN  "
            ],
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              "      <th></th>\n",
              "      <th>Name</th>\n",
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              "      <th>NA_Sales</th>\n",
              "      <th>EU_Sales</th>\n",
              "      <th>JP_Sales</th>\n",
              "      <th>Other_Sales</th>\n",
              "      <th>Global_Sales</th>\n",
              "      <th>Critic_Score</th>\n",
              "      <th>Critic_Count</th>\n",
              "      <th>User_Score</th>\n",
              "      <th>User_Count</th>\n",
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              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Wii Sports</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>41.36</td>\n",
              "      <td>28.96</td>\n",
              "      <td>3.77</td>\n",
              "      <td>8.45</td>\n",
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              "      <td>51.0</td>\n",
              "      <td>8</td>\n",
              "      <td>322.0</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>E</td>\n",
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              "    <tr>\n",
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              "      <td>Super Mario Bros.</td>\n",
              "      <td>NES</td>\n",
              "      <td>1985.0</td>\n",
              "      <td>Platform</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>29.08</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mario Kart Wii</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>15.68</td>\n",
              "      <td>12.76</td>\n",
              "      <td>3.79</td>\n",
              "      <td>3.29</td>\n",
              "      <td>35.52</td>\n",
              "      <td>82.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>709.0</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Wii Sports Resort</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>15.61</td>\n",
              "      <td>10.93</td>\n",
              "      <td>3.28</td>\n",
              "      <td>2.95</td>\n",
              "      <td>32.77</td>\n",
              "      <td>80.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>8</td>\n",
              "      <td>192.0</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pokemon Red/Pokemon Blue</td>\n",
              "      <td>GB</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>Role-Playing</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>11.27</td>\n",
              "      <td>8.89</td>\n",
              "      <td>10.22</td>\n",
              "      <td>1.00</td>\n",
              "      <td>31.37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-438dcb68-7415-4a83-8c89-d6f5a3f32b01 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-438dcb68-7415-4a83-8c89-d6f5a3f32b01');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-781134ab-13c2-46b4-b96f-6388543da770\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-781134ab-13c2-46b4-b96f-6388543da770')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-781134ab-13c2-46b4-b96f-6388543da770 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4H06-qZpx1-L",
        "outputId": "aff321e2-af06-4be4-9e83-f9907ead8e98"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16719, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base  = base.drop(\"Other_Sales\", axis = 1)\n",
        "base  = base.drop(\"Global_Sales\", axis = 1)\n",
        "base  = base.drop(\"Developer\", axis = 1)"
      ],
      "metadata": {
        "id": "6mwjIX_bzL6m"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SBweQr4EznMs",
        "outputId": "9334841f-33ff-4c35-b119-ac2d6a0d9b92"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16719, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(base.isnull());"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "Dn8Wnblnz1uB",
        "outputId": "a053d7cb-498b-4a3b-89cc-35f12755055e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dropa vazios em linha\n",
        "base = base.dropna(axis = 0)"
      ],
      "metadata": {
        "id": "baPsKlsU0UqS"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(base.isnull());"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "R8hhQA_Z0pnu",
        "outputId": "66b22ab7-1220-4f0b-f721-8c0c8868953e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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fH09ubi6xsbEAVK9eHRsbG73Bzd27dwGoXbt2oTdJCyGEEC8jQ/taDU0AwONrZKKjowkMDNSmmZiY4O3tTVRUlMHyo6KiCAgI0Enz8fHRub/vn5KTk9m7dy/r1q3Tezd//nw+/vhjKlWqxMCBA/H398fMTJm5GaOXeubMGdq3b6/9Oe8XZejQocyePZtdu3YB0LBhQ53vDh06RLt27QCYOXOmzi9Mo0aN9PIIIYQQLx0j3twcFBSkvR4mz6xZs5g9e7Ze3rt375Kbm4uzs7NOurOzM7/99pvB8pOSkgzmz5vQeNK6deuwtbWlV69eOunjx4+ncePGlC1blhMnThAYGEhiYiJLlix5Vhefi9EHPu3atUOj0eT7/mnv8oSGhj7zDp/C1CmEEEK8EIy41BUYGKg3I2NotqeorF27lkGDBmFpaamT/s821q9fH3Nzc9577z2CgoIUaW+x3+MjhBBCCOPLb1nLkPLly2NqakpycrJOenJycr57b1xcXAqc/9ixY8TFxbF58+ZntqV58+Y8evSIGzduUKtWrQK1vzAkOrsQQghRUhTTqS5zc3M8PT05ePCgNk2tVnPw4EG8vLwMfuPl5aWTHyAiIsJg/jVr1uDp6UmDBg2e2ZbY2FhMTExwcnIqVB8KSmZ8hBBCiJKiGE91BQQEMHToUJo0aUKzZs1YtmwZGRkZ2lNeQ4YMoUKFCgQFBQEwYcIE2rZty+LFi+natSubNm3izJkzrFq1Sqfc+/fvExYWxuLFi/XqjIqK4uTJk7Rv3x5bW1uioqLw9/fnnXfeoUyZMor0s0ijs+e5dOkS3bt3x97eHmtra5o2bUp8fLxePo1GQ+fOnQ2Wc/DgQVq2bKk9Ajd16lQePXpk7O4IIYQQr4R+/fqxaNEiZs6cScOGDYmNjSU8PFy7gTk+Pp7ExERt/pYtW7JhwwZWrVpFgwYN2Lp1Kzt27OD111/XKXfTpk1oNBoGDBigV6eFhQWbNm2ibdu21K1bl7lz5+Lv7683eDImo8/45EVnHzFihN7ObYBr167RunVr/Pz8mDNnDnZ2dly4cEFvsxPAsmXLUKlUeum//PILXbp04cMPP+Sbb77h9u3bjB49mtzcXBYtWmTsLgkhhBBFo5iDlI4dO5axY8cafHf48GG9tD59+tCnT5+nljlq1ChGjRpl8F3jxo35+eefC93Of6NIo7MDfPjhh3Tp0oVPP/1Um2bo0sHY2FgWL17MmTNncHV11Xm3efNm6tevz8yZM4HH9/t8+umn9O3bl1mzZmFra2uk3gghhBBFyIjH2YVhRbq5Wa1Ws3fvXmrWrImPjw9OTk40b95cbxnr4cOHDBw4kBUrVhjcHZ6VlaU3Q2RlZUVmZibR0dFKdkEIIYQQL7AiHfjcuXOH9PR05s+fj6+vLwcOHOCtt96iV69eHDlyRJvP39+fli1b0qNHD4Pl+Pj4cOLECTZu3Ehubi63b9/mo48+AtBZfxRCCCFeKMUcpPRVUKSnutT//19Ejx498Pf3Bx7f4HzixAmCg4Np27Ytu3btIjIykrNnz+ZbTqdOnVi4cCGjR49m8ODBWFhYMGPGDI4dO4aJSf5jOUPXd2drciVelxBCiJJBlroUV6QzPuXLl8fMzIw6deropNeuXVt7qisyMpJr167h4OCgjb4O0Lt3b51wFQEBAaSmphIfH8/du3e1s0NPBj77p6CgIOzt7XWe0AdXjNxLIYQQ4jnJjI/iinTGx9zcnKZNmxIXF6eTfvnyZSpXrgzAtGnTePfdd3Xe16tXj6VLl9KtWzeddJVKhZubGwAbN26kYsWKNG7cON/6DV3ffb72oOfujxBCCCFeLEUanb1SpUpMmTKFfv368cYbb9C+fXvCw8PZvXu39pici4uLwQ3NlSpVokqVKtqfFy5ciK+vLyYmJmzbto358+ezZcsWTE3zX7YydH23LHMJIYQoMWSpS3FFGp09NDSUt956i+DgYIKCghg/fjy1atXi+++/p3Xr1oWq54cffmDu3LlkZWXRoEEDdu7c+dRj9EIIIUSJJ0tUilNpXvGw5mde61ncTRBCCPGCaHJrh6Ll/731E6OVZfX2dKOV9TKRWF1CCCFESSEzPoqTgY8QQghRUrzaizBFokiPswshhBBCFCejD3yCgoJo2rQptra2ODk50bNnT73j65mZmYwZM4Zy5cphY2ND7969SU5O1r4PDQ1FpVIZfO7cuaPNt379eho0aEDp0qVxdXVlxIgR3Lt3z9hdEkIIIYqG3OOjOKMPfI4cOcKYMWP4+eefiYiIICcnh06dOpGRkaHN4+/vz+7duwkLC+PIkSMkJCToRHLv168fiYmJOo+Pjw9t27bFyckJgOPHjzNkyBD8/Py4cOECYWFhnDp1ipEjRxq7S0IIIUTRkIGP4oy+xyc8PFzn59DQUJycnIiOjuaNN94gLS2NNWvWsGHDBt58800AQkJCqF27Nj///DMtWrTAysoKKysrbRl//vknkZGRrFmzRpsWFRWFu7s748ePB6BKlSq89957LFiwwNhdEkIIIcRLQvE9PmlpaQCULVsWgOjoaHJycvD29tbm8fDwoFKlSkRFRRks45tvvqF06dK8/fbb2jQvLy/++OMP9u3bh0ajITk5ma1bt9KlSxcFeyOEEEIoSKM23iMMUnTgo1armThxIq1ateL1118HICkpCXNzcxwcHHTyOjs7k5SUZLCcNWvWMHDgQJ1ZoFatWrF+/Xr69euHubk5Li4u2Nvbs2LFCsX6I4QQQihKlroUp+jAZ8yYMZw/f55NmzY9dxlRUVFcunQJPz8/nfSLFy8yYcIEZs6cSXR0NOHh4dy4cYPRo0fnW1ZWVhb379/XebI1uc/dNiGEEMKoNBrjPcIgxQY+Y8eOZc+ePRw6dIjXXntNm+7i4kJ2djapqak6+ZOTkw3G6Fq9ejUNGzbE09NTJz0oKIhWrVoxZcoU6tevj4+PD19++SVr164lMTHRYJskOrsQQgjxajP6wEej0TB27Fi2b99OZGSkTmBRAE9PT0qVKsXBgwe1aXFxccTHx+Pl5aWTNz09nS1btujN9gA8fPgQExPd5ucFKM0vCkdgYCBpaWk6zzDbGs/VTyGEEMLoZKlLcUY/1TVmzBg2bNjAzp07sbW11e7bsbe3x8rKCnt7e/z8/AgICKBs2bLY2dkxbtw4vLy8aNGihU5Zmzdv5tGjR7zzzjt69XTr1o2RI0eycuVKfHx8SExMZOLEiTRr1gw3NzeDbZPo7EIIIUo0GbAozugDn5UrVwLQrl07nfSQkBCGDRsGwNKlSzExMaF3795kZWVpl6metGbNGnr16qW3ERpg2LBhPHjwgOXLlzNp0iQcHBx488035Ti7EEIIIfIl0dklOrsQQogCUjw6++oAo5Vl9e4So5X1MpEgpUIIIUQJoVG/0nMRRUKClAohhBDilSEzPkIIIURJIZubFVcs0dnzaDQaOnfujEqlYseOHTrvTp8+TYcOHXBwcKBMmTL4+Pjwyy+/aN9nZmYybNgw6tWrh5mZGT179jR2V4QQQoiiJSErFFcs0dnzLFu2DJVKpZeenp6Or68vlSpV4uTJk/z000/Y2tri4+NDTk4OALm5uVhZWTF+/HiduF9CCCGEEPkp8ujseWJjY1m8eDFnzpzB1dVV55vffvuNlJQUPvroIypWrAjArFmzqF+/Pjdv3qR69epYW1trj84fP35c7yZoIYQQ4oUjm5sVV+TR2eHxrcsDBw5kxYoVBsNU1KpVi3LlyrFmzRqys7P5+++/WbNmDbVr18bd3V3pJgshhBDFQ25uVlyRR2cH8Pf3p2XLlvTo0cPgd7a2thw+fJjvvvsOKysrbGxsCA8P54cffsDMTPZjCyGEeEnJwEdxio4i8qKz//TTT9q0Xbt2ERkZydmzZ/P97u+//8bPz49WrVqxceNGcnNzWbRoEV27duX06dNYWVk9V3uysrLIysrSScvW5ErYCiGEEOIVUeTR2SMjI7l27RoODg6YmZlpZ3B69+6tDXOxYcMGbty4QUhICE2bNqVFixZs2LCB69evs3Pnzuduk0RnF0IIUaJpNMZ7hEFGn/HRaDSMGzeO7du3c/jwYb3o7NOmTePdd9/VSatXrx5Lly6lW7duwP9FXv/nia+8n9X/YvouMDCQgADd68DP1x703OUJIYQQRiVLVIor8ujsLi4uBjc0V6pUSTtI6tixI1OmTGHMmDGMGzcOtVrN/PnzMTMzo3379tpvLl68SHZ2NikpKTx48IDY2FgAGjZsaLBtEp1dCCGEeLUZfalr5cqVpKWl0a5dO1xdXbXP5s2bC1yGh4cHu3fv5tdff8XLy4s2bdqQkJBAeHi4ztH3Ll260KhRI3bv3s3hw4dp1KgRjRo1MnaXhBBCiKKh1hjveQ4rVqzA3d0dS0tLmjdvzqlTp56aPywsDA8PDywtLalXrx779u3TeT9s2DBUKpXO4+vrq5MnJSWFQYMGYWdnh4ODA35+fqSnpz9X+wtCkaUuY3zTsWNHOnbs+NTvbty4Uei6hBBCiBKrGG9c3rx5MwEBAQQHB9O8eXOWLVuGj48PcXFxODk56eU/ceIEAwYMICgoiP/85z9s2LCBnj17EhMTo3OS29fXl5CQEO3PT668DBo0iMTERO2lx8OHD2fUqFFs2LBBkX6qNM8zUnmJnHmtZ3E3QQghxAuiya0dipb/cOEIo5VVesraQuVv3rw5TZs2Zfny5cDjK2kqVqzIuHHjmDZtml7+fv36kZGRwZ49e7RpLVq0oGHDhgQHBwOPZ3xSU1P1wlLluXTpEnXq1OH06dM0adIEeHwRcpcuXbh16xZubm6F6kNBSHR2IYQQoqQw4lJXVlYW9+/f13mevNIlT3Z2NtHR0TohoExMTPD29iYqKsrgN1FRUXoho3x8fPTyHz58GCcnJ2rVqsX777/PvXv3dMpwcHDQDnoAvL29MTEx4eTJk4X+5SsIGfgIIYQQJYRGrTbaY+gKl6CgIIP13r17l9zcXJydnXXSnZ2dtYeUnpSUlPTM/L6+vnzzzTccPHiQBQsWcOTIETp37kxubq62jCeX0czMzChbtmy+9f5bcg2yEEII8RIydIXLk/trlNa/f3/tP9erV4/69etTrVo1Dh8+TIcOHYq0LXmMPuMTFBRE06ZNsbW1xcnJiZ49exIXF6eT59q1a7z11ls4OjpiZ2dH3759SU5O1r6/ceMGfn5+VKlSBSsrK6pVq8asWbPIzs7W5pk9e7beTnGVSoW1tbWxuySEEEIUDSMudVlYWGBnZ6fz5DfwKV++PKampjp/FgMkJycbvIIGwMXFpVD5AapWrUr58uW5evWqtow7d+7o5Hn06BEpKSlPLeffMPrA58iRI4wZM4aff/5Zu0O7U6dOZGRkAJCRkUGnTp1QqVRERkZy/PhxsrOz6datm/Zywt9++w21Ws1XX33FhQsXWLp0KcHBwfzvf//T1jN58mQSExN1njp16tCnTx9jd0kIIYQoGhq18Z5CMDc3x9PTk4MHD2rT1Go1Bw8exMvLy+A3Xl5eOvkBIiIi8s0PcOvWLe7du6e9msbLy4vU1FSio6O1eSIjI1Gr1TRv3rxQfSgoxU91/fnnnzg5OXHkyBHeeOMNDhw4QOfOnfnrr7+ws7MDHkdwL1OmDAcOHNDbKJVn4cKFrFy5kt9//93g+19++YWGDRty9OhR2rRpU+D2yakuIYQQBaX0qa6Mj4wXTcB65vpC5d+8eTNDhw7lq6++olmzZixbtowtW7bw22+/4ezszJAhQ6hQoYJ2n9CJEydo27Yt8+fPp2vXrmzatIl58+Zpj7Onp6czZ84cevfujYuLC9euXeODDz7gwYMHnDt3Tjv71LlzZ5KTkwkODtYeZ2/SpIlix9kV39yclpYGQNmyZYHHgUJVKpXOdJulpSUmJiY6wUwNlZNXhiGrV6+mZs2ahRr0CCGEEOKxfv36sWjRImbOnEnDhg2JjY0lPDxcu4E5Pj6exMREbf6WLVuyYcMGVq1aRYMGDdi6dSs7duzQ3uFjamrKr7/+Svfu3alZsyZ+fn54enpy7NgxnTHA+vXr8fDwoEOHDnTp0oXWrVuzatUqxfqp6IyPWq2me/fupKamagc1f/75J9WrV2f48OHMmzcPjUbDtGnTWL58OaNGjeKrr77SK+fq1at4enqyaNEiRo4cqfc+MzMTNzc3pk2bxgcffJBvewxFZz9fe5CErRBCCFEgis/4zB5gtLKsZ280WlkvE0VnfMaMGcP58+fZtGmTNs3R0ZGwsDB2796NjY0N9vb2pKam0rhxY0xM9Jtz+/ZtfH196dOnj8FBD8D27dt58OABQ4cOfWp7JDq7EEKIEq2YQ1a8ChQ7zj527Fj27NnD0aNHee2113TederUiWvXrnH37l3MzMxwcHDAxcWFqlWr6uRLSEigffv2tGzZ8qnTXqtXr+Y///mP3n0CT5Lo7EIIIcSrTZFYXePGjWP79u0cPnxYG3HdkPLlywOPd3DfuXOH7t27a9/dvn2b9u3b4+npSUhIiMHZIIDr169z6NAhdu3a9cy2SXR2IYQQJVoxxup6VRh94DNmzBg2bNjAzp07sbW11d68aG9vj5WVFQAhISHUrl0bR0dHoqKimDBhAv7+/tSqVQt4POhp164dlStXZtGiRfz555/a8p8817927VpcXV3p3LmzsbsihBBCFC1ZolKc0Qc+K1euBKBdu3Y66SEhIQwbNgyAuLg4AgMDSUlJwd3dnQ8//BB/f39t3oiICK5evcrVq1f1lsn+uRdbrVYTGhrKsGHDMDWVmRshhBBCPJ1EZ5d7fIQQQhSQ0qe60gN7G60sm6DvjVbWy0RidQkhhBAlhSx1KU6iswshhBDilSEzPkIIIURJITM+ijP6jM/KlSupX7++NhKsl5cXP/zwg14+jUZD586dUalU7NixQ+ddfHw8Xbt2pXTp0jg5OTFlyhQePXqkk2f9+vU0aNCA0qVL4+rqyogRI7h3756xuyOEEEIUnWIKUvoqMfrA57XXXmP+/PlER0dz5swZ3nzzTXr06MGFCxd08i1btgyVSqX3fW5uLl27diU7O5sTJ06wbt06QkNDmTlzpjbP8ePHGTJkCH5+fly4cIGwsDBOnTqV783OQgghxAtBbm5WnNEHPt26daNLly7UqFGDmjVrMnfuXGxsbPj555+1eWJjY1m8eDFr167V+/7AgQNcvHiR7777joYNG9K5c2c+/vhjVqxYQXZ2NgBRUVG4u7szfvx4qlSpQuvWrXnvvfc4deqUsbsjhBBCiJeIopubc3Nz2bRpExkZGXh5eQHw8OFDBg4cyIoVK/QuI4THg5p69erphJ/w8fHh/v372lkjLy8v/vjjD/bt24dGoyE5OZmtW7fSpUsXJbsjhBBCKEqj1hjtEYYpsrn53LlzeHl5kZmZiY2NDdu3b6dOnToA+Pv707JlS3r06GHw26SkJL2YW3k/590C3apVK9avX0+/fv3IzMzk0aNHdOvWjRUrVijRHSGEEKJoyIBFcYoMfGrVqkVsbCxpaWls3bqVoUOHcuTIEa5evUpkZCRnz579V+VfvHiRCRMmMHPmTHx8fEhMTGTKlCmMHj2aNWvW5PtdVlYWWVlZOmnZmlyJ1yWEEEK8IhQZ+Jibm1O9enUAPD09OX36NJ999hlWVlZcu3YNBwcHnfy9e/emTZs2HD58GBcXF729OsnJycD/xekKCgqiVatWTJkyBYD69etjbW1NmzZt+OSTT3B1dTXYrqCgIObMmaOTNtK2FqPsPP51n4UQQoh/TS2nsZRWJBcYqtVqsrKymDZtGr/++iuxsbHaB2Dp0qWEhIQAj/fvnDt3jjt37mi/j4iIwM7OTrtc9vDhQ71o7Xmxup4WgSMwMJC0tDSdZ5htDWN2VQghhHh+cqpLcUaf8QkMDKRz585UqlSJBw8esGHDBg4fPsz+/ftxcXExuKG5UqVKVKlSBYBOnTpRp04dBg8ezKeffkpSUhLTp09nzJgxWFhYAI9Pjo0cOZKVK1dql7omTpxIs2bNcHNzy7dtFhYW2jLyyDKXEEII8eow+sDnzp07DBkyhMTEROzt7alfvz779++nY8eOBfre1NSUPXv28P777+Pl5YW1tTVDhw7lo48+0uYZNmwYDx48YPny5UyaNAkHBwfefPNNFixYYOzuCCGEEEVHZmoUJ9HZJTq7EEKIAlI6Ovv993yMVpbdV/uNVtbLRIKUCiGEEOKVIUFKhRBCiJJClroUJwMfIYQQoqSQgY/iijw6e1JSEoMHD8bFxQVra2saN27M999/r1OGu7s7KpVK55k/f772fWZmJsOGDaNevXqYmZnRs2dPY3dDCCGEKHISskJ5Rp/xyYvOXqNGDTQaDevWraNHjx6cPXuWunXrMmTIEFJTU9m1axfly5dnw4YN9O3blzNnztCoUSNtOR999JFOtHVbW1vtP+fm5mJlZcX48eP1Bk1CCCGEEPkx+sCnW7duOj/PnTuXlStX8vPPP1O3bl1OnDjBypUradasGQDTp09n6dKlREdH6wx8bG1tDd75A2Btbc3KlSsBOH78OKmpqcbuhhBCCFH0ZKZGcUUenb1ly5Zs3ryZlJQU1Go1mzZtIjMzk3bt2ul8O3/+fMqVK0ejRo1YuHAhjx49UrKpQgghRPFTG/ERBhV5dPYtW7bQr18/ypUrh5mZGaVLl2b79u3a2F4A48ePp3HjxpQtW5YTJ04QGBhIYmIiS5YsUaK5QgghhHhFFGl09jp16jBjxgxSU1P58ccfKV++PDt27KBv374cO3aMevXqARAQEKAtq379+pibm/Pee+8RFBSkF3KiMCQ6uxBCiJJMNiUrT5Glrrzo7J6engQFBdGgQQM+++wzrl27xvLly1m7di0dOnSgQYMGzJo1iyZNmrBixYp8y2vevDmPHj3ixo0b/6pdQUFB2Nvb6zyhD678qzKFEEIIo5EgpYor0ujsDx8+fFypgcjqanX+C5KxsbGYmJjg5OT0r9oh0dmFEEKIV1uRRmf38PCgevXqvPfeeyxatIhy5cqxY8cOIiIi2LNnDwBRUVGcPHmS9u3bY2trS1RUFP7+/rzzzjuUKVNGW8/FixfJzs4mJSWFBw8eEBsbC0DDhg3zbZtEZxdCCFGiyaZkxRV5dPZ9+/Yxbdo0unXrRnp6OtWrV2fdunV06dIFeDw42bRpE7NnzyYrK4sqVarg7++vs+8HoEuXLty8eVP7c95R+Fc85qoQQogXmOzxUZ5EZ5fo7EIIIQpI6ejsf/VpZ7SyyoQdNlpZLxOJ1SWEEEKUFLLUpbgi2dwshBBCiGcr7lhdK1aswN3dHUtLS5o3b86pU6eemj8sLAwPDw8sLS2pV68e+/bt077Lyclh6tSp1KtXD2tra9zc3BgyZAgJCQk6ZTwrPqexycBHCCGEKCmK8ebmzZs3ExAQwKxZs4iJiaFBgwb4+Phw584dg/lPnDjBgAED8PPz4+zZs/Ts2ZOePXty/vx5AB4+fEhMTAwzZswgJiaGbdu2ERcXR/fu3fXK+uijj0hMTNQ+48aNK3wHCkjxgc/8+fNRqVRMnDhRm/bee+9RrVo1rKyscHR0pEePHvz222863z05+lOpVGzatMlgHcePH8fMzOypJ7qEEEIIkb8lS5YwcuRIhg8fTp06dQgODqZ06dKsXbvWYP7PPvsMX19fpkyZQu3atfn4449p3Lgxy5cvB8De3p6IiAj69u1LrVq1aNGiBcuXLyc6Opr4+HidsvLic+Y91tbWivVT0YHP6dOn+eqrr6hfv75OuqenJyEhIVy6dIn9+/ej0Wjo1KkTubm5OvlCQkJ0RoA9e/bUqyM1NZUhQ4bQoUMHJbsihBBCKE6jNt6TlZXF/fv3dZ4noxfkyc7OJjo6Gm9vb22aiYkJ3t7eREVFGfwmKipKJz+Aj49PvvkB0tLSUKlUODg46KQXZXxOxQY+6enpDBo0iK+//lrn/h2AUaNG8cYbb+Du7k7jxo355JNP+OOPP/RuZnZwcNAZAVpaWurVM3r0aAYOHKgNgiqEEEK8sIy41GUoWkFQUJDBau/evUtubi7Ozs466c7OziQlJRn8JikpqVD5MzMzmTp1KgMGDMDOzk6bPn78eDZt2sShQ4d47733mDdvHh988EH+v0b/kmIDnzFjxtC1a1e90eCTMjIyCAkJoUqVKlSsWFGvjPLly9OsWTPWrl2rd0dPSEgIv//+O7NmzTJ6+4UQQogXmaFoBYGBgcXSlpycHPr27YtGo2HlypU67wICAmjXrh3169dn9OjRLF68mC+++CLf2al/S5Hj7Js2bSImJobTp0/nm+fLL7/kgw8+ICMjg1q1ahEREYG5ubn2/UcffcSbb75J6dKlOXDgAP/9739JT09n/PjxAFy5coVp06Zx7NgxzMzkVL4QQogXn8aIx9kNRSvIT/ny5TE1NSU5OVknPTk5GRcXF4PfuLi4FCh/3qDn5s2bREZG6sz2GPLP+Jy1atUqUPsLw+gzPn/88QcTJkxg/fr1Bpem8gwaNIizZ89y5MgRatasSd++fcnMzNS+nzFjBq1ataJRo0ZMnTqVDz74gIULFwKQm5vLwIEDmTNnDjVr1ixw2wytd2Zrcp/9oRBCCFEUiulUl7m5OZ6enhw8ePD/mqJWc/DgwXy3knh5eenkB4iIiNDJnzfouXLlCj/++CPlypV7ZluMFZ8zP0a/uXnHjh289dZbmJr+Xwys3NxcVCoVJiYmZGVl6byDx5uqypQpw+rVqxkwYIDBcvfu3ct//vMfMjMz+fvvvylTpoxOOWq1Go1Gg6mpKQcOHODNN9/UK2P27NnMmTNHJ22kbS1G2Xn8my4LIYR4RSh9c/Ndn7ZGK6v8/iOFyr9582aGDh3KV199RbNmzVi2bBlbtmzht99+w9nZmSFDhlChQgXtPqETJ07Qtm1b5s+fT9euXdm0aRPz5s0jJiaG119/nZycHN5++21iYmLYs2ePzn6gsmXLYm5unm98zs6dO7Nu3Tqj/Vr8k9HXiDp06MC5c+d00oYPH46HhwdTp07VG/TA4/haGo3mqet5sbGxlClTBgsLC0qVKqVXx5dffklkZCRbt26lSpUqBssIDAzUi/l1vvaggnZNCCGEUJQxl7oKq1+/fvz555/MnDmTpKQkGjZsSHh4uHbAEh8fj4nJ/y0UtWzZkg0bNjB9+nT+97//UaNGDXbs2MHrr78OwO3bt9m1axegH0D80KFDtGvXrsDxOY2pSGJ1tWvXjoYNG7Js2TJ+//13Nm/eTKdOnXB0dOTWrVvMnz+f48ePc+nSJZycnNi9ezfJycm0aNECS0tLIiIimDx5MpMnT9absckze/ZsduzYoY3SXlASq0sIIURBKT3jc6eD8WZ8nA4WbsbnVVHku4ItLS05duwYy5Yt46+//sLZ2Zk33niDEydOaNfzSpUqxYoVK/D390ej0VC9enXtxUpCCCHEy6o4Z3xeFRKdXWZ8hBBCFJDSMz7J7Y034+N8SGZ8DJFz4EIIIURJoVEVdwteejLwEUIIIUoIWepSnkRnF0IIIcQrQ2Z8hBBCiBJCo5alLqUpPuMzf/58VCoVEydO1KZdu3aNt956C0dHR+zs7Ojbt6/etdcpKSkMGjQIOzs7HBwc8PPzIz093WAdV69exdbWVi/aqxBCCPEiMWZ0dmGYogOf06dP89VXX1G/fn1tWkZGBp06dUKlUhEZGcnx48fJzs6mW7duqNX/929q0KBBXLhwgYiICPbs2cPRo0cZNWqUXh05OTkMGDCANm3aKNkVIYQQQrwEFBv4pKenM2jQIL7++mvKlCmjTT9+/Dg3btwgNDSUevXqUa9ePdatW8eZM2eIjIwE4NKlS4SHh7N69WqaN29O69at+eKLL9i0aRMJCQk69UyfPh0PDw/69u2rVFeEEEKIIqHRqIz2CMMUG/iMGTOGrl274u3trZOelZWFSqXSiRhraWmJiYkJP/30EwBRUVE4ODjQpEkTbR5vb29MTEw4efKkNi0yMpKwsDBWrFihVDeEEEKIIiNLXcpTZHPzpk2biImJ4fTp03rvWrRogbW1NVOnTmXevHloNBqmTZtGbm4uiYmJACQlJelFZTUzM6Ns2bIkJSUBcO/ePYYNG8Z33333zBD3ebKysvTigWVrcjFX6ccPE0IIIcTLx+gzPn/88QcTJkxg/fr1WFpa6r13dHQkLCyM3bt3Y2Njg729PampqTRu3Fgn+NmzjBw5koEDB/LGG28U+JugoCDs7e11ntAHVwr8vRBCCKEkjVpltEcYZvSQFTt27OCtt97SicKem5uLSqXCxMSErKws7bu7d+9iZmaGg4MDLi4uTJo0iSlTprB27VomTZrEX3/9pS3j0aNHWFpaEhYWxltvvYWDg4POKS+NRoNarcbU1JRVq1YxYsQIvbYZmvE5X3uQzPgIIYQoEKVDVsQ36WC0siqdOWi0sl4mRl/q6tChA+fOndNJGz58OB4eHkydOlVnQFS+fHng8V6dO3fu0L17dwC8vLxITU0lOjoaT09PbR61Wk3z5s2Bx/uAcnNztWXt3LmTBQsWcOLECSpUqGCwbRYWFjp7iwAZ9AghhCgxZKZGeUYf+Nja2vL666/rpFlbW1OuXDltekhICLVr18bR0ZGoqCgmTJiAv78/tWrVAqB27dr4+voycuRIgoODycnJYezYsfTv3x83Nzdtnn86c+YMJiYmenULIYQQQuQplpub4+LiCAwMJCUlBXd3dz788EP8/f118qxfv56xY8fSoUMHTExM6N27N59//nlxNFcIIYQoEjLjozyj7/F50Zx5rWdxN0EIIcQLQuk9PtcbdDRaWVV+iTBaWS8TCVIqhBBCiFeGBCkVQgghSghZ6lKeDHyEEEKIEkJCTSjP6Etds2fPRqVS6TweHh56+TQaDZ07d0alUrFjxw5t+r179/D19cXNzQ0LCwsqVqzI2LFjuX//vs7369evp0GDBpQuXRpXV1dGjBjBvXv3jN0dIYQQQrxEFNnjU7duXRITE7VPXgyuf1q2bBkqlf7I1sTEhB49erBr1y4uX75MaGgoP/74I6NHj9bmOX78OEOGDMHPz48LFy4QFhbGqVOnGDlypBLdEUIIIYqExOpSniJLXWZmZri4uOT7PjY2lsWLF3PmzBlcXV113pUpU4b3339f+3PlypX573//y8KFC7VpUVFRuLu7M378eACqVKnCe++9x4IFC4zcEyGEEKLoqGWpS3GKzPhcuXIFNzc3qlatyqBBg4iPj9e+e/jwIQMHDmTFihVPHRzlSUhIYNu2bbRt21ab5uXlxR9//MG+ffvQaDQkJyezdetWunTpokR3hBBCCPGSMPrAp3nz5oSGhhIeHs7KlSu5fv06bdq04cGDBwD4+/vTsmVLevTo8dRyBgwYQOnSpalQoQJ2dnasXr1a+65Vq1asX7+efv36YW5ujouLC/b29qxYscLY3RFCCCGKjEajMtojDFP8AsPU1FQqV67MkiVLcHR0ZNKkSZw9exYbG5vHDVCp2L59Oz179tT5LikpidTUVC5fvkxgYCBt27blyy+/BODixYt4e3vj7++Pj48PiYmJTJkyhaZNm7JmzZp82yJBSoUQQvwbSl9g+FtN461ceFzeZ7SyXiZFcnNz06ZN8fb25u+//+bzzz/HxOT/Jppyc3MxMTGhTZs2HD582OD3P/30E23atCEhIQFXV1cGDx5MZmYmYWFh+eYxZPbs2cyZM0cnbaRtLUbZ6Z86E0IIIZ6k9MDnUg3jDXxqX5GBjyGK39ycnp7OtWvXcHV1Zdq0afz666/ExsZqH4ClS5cSEhKSbxlq9ePt6XmzNQ8fPtQZPAHaqO9PG8cFBgaSlpam8wyzrfFvuieEEEKIF4jRT3VNnjyZbt26UblyZRISEpg1axampqYMGDAAR0dHgxuaK1WqRJUqVQDYt28fycnJNG3aFBsbGy5cuMCUKVNo1aoV7u7uAHTr1o2RI0eycuVK7VLXxIkTadasmTZ6uyEWFhZYWFjopMkylxBCiJJCbm5WntEHPrdu3WLAgAHcu3cPR0dHWrduzc8//4yjo2OBvreysuLrr7/G39+frKwsKlasSK9evZg2bZo2z7Bhw3jw4AHLly9n0qRJODg48Oabb8pxdiGEEC80Oc6uPInOLtHZhRBCFJDSe3zOV/2P0cp6/fc9RivrZSKxuoQQQogSQo6hK08GPkIIIUQJ8WqvwRQNxU91CSGEEEKUFEUenf3atWu89dZbODo6YmdnR9++fUlOTtYpIyYmho4dO+Lg4EC5cuUYNWoU6enp2vcFjeAuhBBCvEjUGpXRHmFYkUZnz8jIoFOnTqhUKiIjIzl+/DjZ2dl069ZNe1dPQkIC3t7eVK9enZMnTxIeHs6FCxcYNmzY/zW6ABHchRBCiBdNcYesWLFiBe7u7lhaWtK8eXNOnTr11PxhYWF4eHhgaWlJvXr12LdP99JEjUbDzJkzcXV1xcrKCm9vb65cuaKTJyUlhUGDBmFnZ4eDgwN+fn46kx3GpsjAJy86e95Tvnx5AI4fP86NGzcIDQ2lXr161KtXj3Xr1nHmzBkiIyMB2LNnD6VKlWLFihXUqlWLpk2bEhwczPfff8/Vq1eB/4vg3qRJEypXrkyHDh3473//y7Fjx5TojhBCCPHS27x5MwEBAcyaNYuYmBgaNGiAj48Pd+7cMZj/xIkTDBgwAD8/P86ePUvPnj3p2bMn58+f1+b59NNP+fzzzwkODubkyZNYW1vj4+NDZmamNs+gQYO4cOECERER7Nmzh6NHjzJq1CjF+lmk0dmzsrJQqVQ6lwhaWlpiYmKinRXKysrC3Nxc52ZmKysrAG2eJxmK4C6EEEK8aDQa4z2FtWTJEkaOHMnw4cOpU6cOwcHBlC5dmrVr1xrM/9lnn+Hr68uUKVOoXbs2H3/8MY0bN2b58uX/vy8ali1bxvTp0+nRowf169fnm2++ISEhgR07dgBw6dIlwsPDWb16Nc2bN6d169Z88cUXbNq0iYSEhOf9ZXyqIo3O3qJFC6ytrZk6dSoPHz4kIyODyZMnk5ubS2JiIgBvvvkmSUlJLFy4kOzsbP766y/t5YV5efI8LYK7EEII8aIx5h6frKws7t+/r/M8Gag7T3Z2NtHR0Xh7e2vTTExM8Pb2JioqyuA3UVFROvkBfHx8tPmvX79OUlKSTh57e3uaN2+uzRMVFYWDgwNNmjTR5vH29sbExISTJ08+3y/iMxh94NO5c2f69OlD/fr18fHxYd++faSmprJlyxYcHR0JCwtj9+7d2NjYYG9vT2pqKo0bN9bO8NStW5d169axePFiSpcujYuLC1WqVMHZ2VkvPtfSpUuJiYlh586dXLt2jYCAgKe2zdD/CLI1ucb+JRBCCCGeizH3+AQFBWFvb6/zBAUFGaz37t275Obm4uzsrJPu7OxMUlKSwW+SkpKemj/vv5+Vx8nJSee9mZkZZcuWzbfef0vxe3wcHByoWbOmdn9Op06duHbtGnfv3sXMzAwHBwdcXFyoWrWq9puBAwcycOBAkpOTsba2RqVSsWTJEp08gHYPkYeHB2XLlqVNmzbMmDEj3+jsQUFBEp1dCCHEKyEwMFBvQuDJeJWvoiKNzv5P5cuXx8HBgcjISO7cuUP37t31vnV2dsbGxobNmzdjaWlJx44d863nyQjuhkh0diGEECWZMZe6LCwssLOz03nyG/iUL18eU1NTvetlkpOTDQYXh8eTD0/Ln/ffz8rz5ObpR48ekZKSkm+9/5bRBz6TJ0/myJEj3LhxgxMnTvDWW29po7MDhISE8PPPP3Pt2jW+++47+vTpg7+/P7Vq1dKWsXz5cmJiYrh8+TIrVqxg7NixBAUF4eDgADyO4B4SEsL58+e5ceMGe/fuZfTo0ToR3A0x9D8Cic4uhBCipNAY8SkMc3NzPD09OXjwoDZNrVZz8OBBvLy8DH7j5eWlkx8gIiJCm79KlSq4uLjo5Ll//z4nT57U5vHy8iI1NZXo6GhtnsjISNRqNc2bNy9kLwqmyKOzx8XFERgYSEpKCu7u7nz44Yf4+/vrlHHq1ClmzZpFeno6Hh4efPXVVwwePFj7viAR3IUQQghRcAEBAQwdOpQmTZrQrFkzli1bRkZGBsOHDwdgyJAhVKhQQbtPaMKECbRt25bFixfTtWtXNm3axJkzZ1i1ahUAKpWKiRMn8sknn1CjRg2qVKnCjBkzcHNzo2fPngDUrl0bX19fRo4cSXBwMDk5OYwdO5b+/fvj5uamSD8lOrtEZxdCCFFASkdnP+Ha22hltUz8vtDfLF++nIULF5KUlETDhg35/PPPtTMv7dq1w93dndDQUG3+sLAwpk+fzo0bN6hRowaffvopXbp00b7XaDTMmjWLVatWkZqaSuvWrfnyyy+pWbOmNk9KSgpjx45l9+7dmJiY0Lt3bz7//HNsbGyev/NPIQMfGfgIIYQoIKUHPsdd3jZaWa2SthqtrJeJBCkVQgghxCtD8ePsQgghhCgYdXE34BWgyIzP7du3eeeddyhXrhxWVlbUq1ePM2fO6OS5dOkS3bt3x97eHmtra5o2baoNbQHw3nvvUa1aNaysrHB0dKRHjx789ttvOmWMHz8eT09PLCwsaNiwoRJdEUIIIYqMBpXRHmGY0Qc+f/31F61ataJUqVL88MMPXLx4kcWLF1OmTBltnmvXrtG6dWs8PDw4fPgwv/76KzNmzMDS0lKbx9PTk5CQEC5dusT+/fvRaDR06tSJ3Fzdm5ZHjBhBv379jN0NIYQQQryEjL65edq0aRw/fvypkdL79+9PqVKl+Pbbbwtc7q+//kqDBg24evUq1apV03k3e/ZsduzYQWxsbKHbK5ubhRBCFJTSm5sPO/cxWlntksOMVtbLxOgzPrt27aJJkyb06dMHJycnGjVqxNdff619r1ar2bt3LzVr1sTHxwcnJyeaN2+ujdRqSEZGBiEhIVSpUoWKFSsau8lCCCFEiaBGZbRHGGb0gc/vv//OypUrqVGjBvv37+f9999n/PjxrFu3DoA7d+6Qnp7O/Pnz8fX15cCBA7z11lv06tWLI0eO6JT15ZdfYmNjg42NDT/88AMRERGYm5sbu8lCCCFEiSB7fJRn9FNdarWaJk2aMG/ePAAaNWrE+fPnCQ4OZujQodqYWj169NDe2NywYUNOnDhBcHAwbdu21ZY1aNAgOnbsSGJiIosWLaJv374cP35cZy9QYWRlZenF8srW5ErYCiGEEOIVYfQZH1dXV+rUqaOTVrt2be2JrfLly2NmZvbUPHns7e2pUaMGb7zxBlu3buW3335j+/btz922oKAg7O3tdZ7QB1eeuzwhhBDCmNRGfIRhRh/4tGrViri4OJ20y5cvU7lyZeBxILSmTZs+NY8hGo0GjUbz1OjrzyLR2YUQQpRkstSlPKMvdfn7+9OyZUvmzZtH3759OXXqFKtWrdIGLQOYMmUK/fr144033qB9+/aEh4eze/duDh8+DDzeJ7R582Y6deqEo6Mjt27dYv78+VhZWenEALl69Srp6ekkJSXx999/a0911alTx+BeIAsLCywsLHTSZJlLCCGEeHUoEqtrz549BAYGcuXKFapUqUJAQAAjR47UybN27VqCgoK4desWtWrVYs6cOfTo0QOAhIQE3n33XaKjo/nrr79wdnbmjTfeYObMmdSqVUtbRrt27fQ2RANcv34dd3f3ArVVjrMLIYQoKKWPs4c79zdaWb7Jm4xW1stEgpTKwEcIIUQBKT3w2WfEgU8XGfgYJEFKhRBCCPHKkCClQgghRAkhm5KVJwMfIYQQooRQy7hHcbLUJYQQQohXhtEHPu7u7qhUKr1nzJgxpKSkMG7cOGrVqoWVlRWVKlVi/PjxpKWlab+/d+8evr6+uLm5YWFhQcWKFRk7diz379/X5tm2bRsdO3bE0dEROzs7vLy82L9/v7G7IoQQQhQpidWlPKMPfE6fPk1iYqL2iYiIAKBPnz4kJCSQkJDAokWLOH/+PKGhoYSHh+Pn5/d/DTIxoUePHuzatYvLly8TGhrKjz/+yOjRo7V5jh49SseOHdm3bx/R0dG0b9+ebt26cfbsWWN3RwghhCgyGiM+wjDFj7NPnDiRPXv2cOXKFVQq/RFoWFgY77zzDhkZGZiZGd5y9Pnnn7Nw4UL++OOPfOupW7cu/fr1Y+bMmYVqnxxnF0IIUVBKH2ff5jLQaGX1StpgtLJeJoru8cnOzua7775jxIgRBgc9AGlpadjZ2eU76ElISGDbtm06wUufpFarefDgAWXLljVKu4UQQgjxclJ04LNjxw5SU1MZNmyYwfd3797l448/ZtSoUXrvBgwYQOnSpalQoQJ2dnasXr0633oWLVpEeno6ffv2fWp7srKyuH//vs6TrcktVJ+EEEIIpahVKqM9wjBFBz5r1qyhc+fOuLm56b27f/8+Xbt2pU6dOsyePVvv/dKlS4mJiWHnzp1cu3aNgIAAg3Vs2LCBOXPmsGXLFpycnJ7aHonOLoQQoiSTPT7KU2yPz82bN6latSrbtm3TxuDK8+DBA3x8fChdujR79uzB0tLyqWX99NNPtGnThoSEBFxdXbXpmzZtYsSIEYSFhdG1a9dntikrK0svuvv52oMkUKkQQogCUXqPT5jrIKOV1SdxvdHKepkodoFhSEgITk5OegOS+/fv4+Pjg4WFBbt27XrmoAce7+EBdAYtGzduZMSIEWzatKlAgx6Q6OxCCCFKNnVxN+AVoMjAR61WExISwtChQ3U2Ld+/f59OnTrx8OFDvvvuO+0+GwBHR0dMTU3Zt28fycnJNG3aFBsbGy5cuMCUKVNo1aqVNuL6hg0bGDp0KJ999hnNmzcnKSkJACsrK+zt7ZXokhBCCKE4ublZeYrs8fnxxx+Jj49nxIgROukxMTGcPHmSc+fOUb16dVxdXbVP3lF1Kysrvv76a1q3bk3t2rXx9/ene/fu7NmzR1vOqlWrePToEWPGjNEpY8KECUp0RwghhBAvCcXv8Snp5B4fIYQQBaX0Hp/1bu8YraxBCd8ZrayXiQQpFUIIIUqIV3omoohIkFIhhBBCvDJkxkcIIYQoIWRzs/KKNDr7jRs3DL5TqVSEhYVpyzh48CAtW7bE1tYWFxcXpk6dyqNHj7TvZ8+ebbAMa2trY3dHCCGEKDJqIz7CsCKNzl6xYkWdd4mJicyZMwcbGxs6d+4MwC+//EKXLl3w9fXl7NmzbN68mV27djFt2jRtHZMnT9Yrp06dOvTp08fY3RFCCCGKjNzcrDyjL3U5Ojrq/Dx//nyqVatG27ZtUalUuLi46Lzfvn07ffv2xcbGBoDNmzdTv359bZT16tWr8+mnn9K3b19mzZqFra0tNjY22vzweLB08eJFgoODjd0dIYQQQrxEijU6e3R0NLGxsfj5+WnTsrKy9G5ztrKyIjMzk+joaIP1rF69mpo1a9KmTRvjdkAIIYQoQmqV8R5hWLFGZ1+zZg21a9emZcuW2jQfHx9OnDjBxo0byc3N5fbt23z00UcAJCYm6pWRmZnJ+vXrdQZPQgghxIvoRdjjk5KSwqBBg7Czs8PBwQE/Pz/S09Of+k1mZiZjxoyhXLly2NjY0Lt3b5KTk7Xvf/nlFwYMGEDFihWxsrKidu3afPbZZzplHD582OD+3rzoDQVVbNHZ//77bzZs2KA3YOnUqRMLFy5k9OjRWFhYULNmTbp06fK4sSb6zd2+fTsPHjxg6NChz2xPVlaWNkxG3pOtyX3O3gkhhBCvnkGDBnHhwgUiIiLYs2cPR48eZdSoUU/9xt/fn927dxMWFsaRI0dISEigV69e2vfR0dE4OTnx3XffceHCBT788EMCAwNZvny5XllxcXE6e3ydnJwK1f5iic4O8O233+Ln58ft27f19gUBaDQaEhMTKVOmDDdu3KBOnTqcOnWKpk2b6uTr0KEDdnZ2bN++/Zltmj17NnPmzNFJG2lbi1F2HoXsnRBCiFeR0jc3f/Wa8W5ufu+W8W9uvnTpEnXq1OH06dM0adIEgPDwcLp06cKtW7cMTnSkpaXh6OjIhg0bePvttwH47bffqF27NlFRUbRo0cJgXWPGjOHSpUtERkYCj2d82rdvz19//YWDg8Nz90GxGZ/8orPnWbNmDd27dzc46AFQqVS4ublhZWXFxo0bqVixIo0bN9bJc/36dQ4dOlTgZa7AwEDS0tJ0nmG2NQrXMSGEEEIhGpXxHiVERUXh4OCgHfQAeHt7Y2JiwsmTJw1+Ex0dTU5ODt7e3to0Dw8PKlWqRFRUVL51paWlUbZsWb30hg0b4urqSseOHTl+/Hih+1Ck0dnzXL16laNHj7Jv3z6D3y9cuBBfX19MTEzYtm0b8+fPZ8uWLZiamurkW7t2La6urtqj8M9iYWGBhYWFTpq5yjSf3EIIIcSLKysri6ysLJ00Q38OFkZSUpLe0pKZmRlly5bNd69NUlIS5ubmerM0zs7O+X5z4sQJNm/ezN69e7Vprq6uBAcH06RJE7Kysli9ejXt2rXj5MmTehMjT1Ok0dnzrF27ltdee41OnToZfP/DDz/Qpk0bmjRpwt69e9m5cyc9e/bUyaNWqwkNDWXYsGF6AyIhhBDiRWTMzc1BQUHY29vrPEFBQQbrnTZtWr4XDOc9v/32m5Jd1zp//jw9evRg1qxZOuOEWrVq8d577+Hp6UnLli1Zu3YtLVu2ZOnSpYUqX5EZn06dOvG0rUPz5s1j3rx5+b7PW897GhMTE/7444/nap8QQghREhnzNFZgYCABAQE6afnN9kyaNCnfE9h5qlatiouLC3fu3NFJf/ToESkpKXr39OVxcXEhOzub1NRUnVmf5ORkvW8uXrxIhw4dGDVqFNOnT39qewCaNWvGTz/99Mx8/ySxuoQQQoiXUGGWtRwdHfPdc/tPXl5epKamEh0djaenJ/B4skKtVtO8eXOD33h6elKqVCkOHjxI7969gccns+Lj4/Hy8tLmu3DhAm+++SZDhw5l7ty5BWp3bGwsrq6uBcqbRwY+QgghRAlR0kNN1K5dG19fX0aOHElwcDA5OTmMHTuW/v37a0903b59mw4dOvDNN9/QrFkz7O3t8fPzIyAggLJly2JnZ8e4cePw8vLSnug6f/48b775Jj4+PgQEBGj3/piammoHZMuWLaNKlSrUrVuXzMxMVq9eTWRkJAcOHChUH2TgI4QQQpQQL8KNy+vXr2fs2LF06NABExMTevfuzeeff659n5OTQ1xcHA8fPtSmLV26VJs3KysLHx8fvvzyS+37rVu38ueff/Ldd9/x3Xf/dwy/cuXK3LhxA3gcDWLSpEncvn2b0qVLU79+fX788Ufat29fqPYb/R6f3NxcZs+ezXfffUdSUhJubm4MGzaM6dOnGwxbMXr0aL766iuWLl3KxIkTtelz585l7969xMbGYm5uTmpqqs53v/zyC/Pnz+enn37i7t27uLu7M3r0aCZMmFCo9p55redz9FIIIcSrSOl7fJZWMt49Pv7xxr/H52Vg9BmfBQsWsHLlStatW0fdunU5c+YMw4cPx97envHjx+vk3b59Oz///LPBC4+ys7Pp06cPXl5erFmzRu/9P295rFixIidOnGDUqFGYmpoyduxYY3dLCCGEEC8Bow98Tpw4QY8ePbQXF7q7u7Nx40ZOnTqlk+/27duMGzeO/fv3G7zkMO+G5dDQUIP1PHlUvmrVqkRFRbFt2zYZ+AghhHghKRljSzxm9Ht8WrZsycGDB7l8+TLweEnqp59+0rlkUK1WM3jwYKZMmULdunWNVnd+tzwKIYQQLwKNER9hmNFnfKZNm8b9+/fx8PDA1NSU3Nxc5s6dy6BBg7R5FixYgJmZmd7S179h6JZHIYQQQoh/MvrAZ8uWLaxfv54NGzZQt25dYmNjmThxIm5ubgwdOpTo6Gg+++wzYmJiDG52fh753fL4JEPXd2drciVshRBCiBLhRTjV9aIz+lLXlClTmDZtGv3796devXoMHjwYf39/7TXZx44d486dO1SqVAkzMzPMzMy4efMmkyZNwt3dvdD1FeaWR0PXd4c+uPI83RRCCCGMzpghK4RhRp/xefjwISYmuuMpU1NT1OrH/xoGDx6sE6EVwMfHh8GDBzN8+PBC1VXYWx4NXd99vvagfHILIYQQ4mVj9IFPt27dmDt3LpUqVaJu3bqcPXuWJUuWaE9hlStXjnLlyul8U6pUKVxcXKhVq5Y2LT4+npSUFOLj48nNzSU2NhaA6tWrY2NjU6BbHp8k0dmFEEKUZLIpWXlGH/h88cUXzJgxg//+97/cuXMHNzc33nvvPWbOnFmocmbOnMm6deu0Pzdq1AiAQ4cO0a5duwLd8iiEEEK8SNQy9FGc0W9uftHIzc1CCCEKSumbm+dWNt72iw9vrjdaWS8TidUlhBBClBCyKVl5MvARQgghSohXegmmiMjARwghhCghZMZHeUa/xwfgwYMHTJw4kcqVK2NlZUXLli05ffq09r1Go2HmzJm4urpiZWWFt7c3V67o36ezd+9emjdvjpWVFWXKlKFnz5467+Pj4+natSulS5fGycmJKVOm8OjRIyW6JIQQQoiXgCIDn3fffZeIiAi+/fZbzp07R6dOnfD29ub27dsAfPrpp3z++ecEBwdz8uRJrK2t8fHxITMzU1vG999/r73b55dffuH48eMMHDhQ+z43N5euXbuSnZ3NiRMnWLduHaGhoYU+PSaEEEKUFGqV8R5hmNFPdf3999/Y2tqyc+dOnajrnp6edO7cmY8//hg3NzcmTZrE5MmTgcfBRZ2dnQkNDaV///48evQId3d35syZg5+fn8F6fvjhB/7zn/+QkJCAs7MzAMHBwUydOpU///wTc3PzArVXTnUJIYQoKKVPdU13H/jsTAX0yY0NRivrZWL0GZ9Hjx6Rm5uLpaWlTrqVlRU//fQT169fJykpSef2Znt7e5o3b05UVBQAMTEx3L59GxMTExo1aoSrqyudO3fm/Pnz2m+ioqKoV6+edtADj2+Avn//PhcuXDB2t4QQQgjxEjD6wMfW1hYvLy8+/vhjEhISyM3N5bvvviMqKorExETtDcv/HLDk/Zz37vfffwdg9uzZTJ8+nT179lCmTBnatWtHSkoKAElJSQbLyHsnhBBCvGg0RnyEYYrs8fn222/RaDRUqFABCwsLPv/8cwYMGKAXwys/eXG9PvzwQ3r37o2npychISGoVCrCwsKeu11ZWVncv39f58nW5D53eUIIIYQxSZBS5Sky8KlWrRpHjhwhPT2dP/74g1OnTpGTk0PVqlVxcXEBIDk5Weeb5ORk7TtXV1cA6tSpo31vYWFB1apViY+PB8DFxcVgGXnvDJHo7EIIIcSrTZGBTx5ra2tcXV3566+/2L9/Pz169KBKlSq4uLhw8OBBbb779+9z8uRJvLy8gMcboS0sLIiLi9PmycnJ4caNG1SuXBkALy8vzp07x507d7R5IiIisLOz0xkw/VNgYCBpaWk6zzDbGkp0XQghhCg0NRqjPcIwRS4w3L9/PxqNhlq1anH16lWmTJmCh4cHw4cPR6VSMXHiRD755BNq1KhBlSpVmDFjBm5ubtp7euzs7Bg9ejSzZs2iYsWKVK5cmYULFwLQp08fADp16kSdOnUYPHgwn376KUlJSUyfPp0xY8boRWDPI9HZhRBClGQyXFGeIgOftLQ0AgMDuXXrFmXLlqV3797MnTuXUqVKAfDBBx+QkZHBqFGjSE1NpXXr1oSHh+ucBFu4cCFmZmYMHjyYv//+m+bNmxMZGUmZMmUAMDU1Zc+ePbz//vt4eXlhbW3N0KFD+eijj5TokhBCCCFeAhKdXe7xEUIIUUBK3+Mz2X2A0cpadGOj0cp6mUisLiGEEKKEkL05ypOBjxBCCFFCyLBHeYqe6hJCCCGEKElkxkcIIYQoIeTiQeUpMuPz4MEDJk6cSOXKlbGysqJly5acPn1a+16lUhl88o6s59m7dy/NmzfHysqKMmXKaI+7A4SGhuZbzj/v9hFCCCFeFBoj/kcYpsiMz7vvvsv58+f59ttvcXNz47vvvsPb25uLFy9SoUIFEhMTdfL/8MMP+Pn50bt3b23a999/z8iRI5k3bx5vvvkmjx490glS2q9fP3x9fXXKGTZsGJmZmTg5OSnRLSGEEEK84Ix+nP3vv//G1taWnTt30rVrV226p6cnnTt35pNPPtH7pmfPnjx48EB7m/OjR49wd3dnzpw5+Pn5FajeP//8kwoVKrBmzRoGDx5c4PbKcXYhhBAFpfRx9rHu/YxW1vIbm41W1svE6Etdjx49Ijc3V+cyQgArKyt++uknvfzJycns3btXZ4ATExPD7du3MTExoVGjRri6utK5c2edGZ8nffPNN5QuXZq3337beJ0RQgghipCErFCe0Qc+tra2eHl58fHHH5OQkEBubi7fffcdUVFRektcAOvWrcPW1pZevXpp037//XcAZs+ezfTp09mzZw9lypShXbt2pKSkGKx3zZo1DBw4ECsrq3zbJtHZhRBCiFebIpubv/32WzQaDRUqVMDCwoLPP/+cAQMGYGKiX93atWsZNGiQzgyRWv14X/uHH35I79698fT0JCQkBJVKRVhYmF4ZUVFRXLp06ZnLYhKdXQghREmmMeIjDFNk4FOtWjWOHDlCeno6f/zxB6dOnSInJ4eqVavq5Dt27BhxcXG8++67Oumurq4AOlHWLSwsqFq1KvHx8Xr1rV69moYNG+Lp6fnUdkl0diGEECWZLHUpT9ELDK2trXF1deWvv/5i//799OjRQ+f9mjVr8PT0pEGDBjrpnp6eWFhYEBcXp03Lycnhxo0bVK5cWSdveno6W7ZsKdAmaAsLC+zs7HQeic4uhBBCvDoUGfjs37+f8PBwrl+/TkREBO3bt8fDw4Phw4dr89y/f5+wsDC92R4AOzs7Ro8ezaxZszhw4ABxcXG8//77APTp00cn7+bNm3n06BHvvPOOEl0RQgghiozaiI9SUlJSGDRoEHZ2djg4OODn50d6evpTv8nMzGTMmDGUK1cOGxsbevfuTXJysk4eQ/fybdq0SSfP4cOHady4MRYWFlSvXp3Q0NBCt1+RgU9aWhpjxozBw8ODIUOG0Lp1a/bv30+pUqW0eTZt2oRGo2HAAMORaBcuXEj//v0ZPHgwTZs25ebNm0RGRlKmTBmdfGvWrKFXr144ODgo0RUhhBCiyLwIFxgOGjSICxcuEBERwZ49ezh69CijRo166jf+/v7s3r2bsLAwjhw5QkJCgs6hpjwhISEkJiZqn39eXHz9+nW6du1K+/btiY2NZeLEibz77rvs37+/UO03+j0+Lxq5x0cIIURBKX2Pzwh3413JsvbGVqOVlefSpUvUqVOH06dP06RJEwDCw8Pp0qULt27dws3NTe+btLQ0HB0d2bBhg/bKmd9++43atWsTFRVFixYtgMczPtu3b9cZ7PzT1KlT2bt3r87VNv379yc1NZXw8PAC90GClAohhBAvIUNXuGRlZf2rMqOionBwcNAOegC8vb0xMTHh5MmTBr+Jjo4mJycHb29vbZqHhweVKlUiKipKJ++YMWMoX748zZo1Y+3atfxzbiYqKkqnDAAfHx+9Mp5FBj5CCCFECWHMpS5DV7gEBQX9q/YlJSXphYUyMzOjbNmyJCUl5fuNubm53pYUZ2dnnW8++ugjtmzZQkREBL179+a///0vX3zxhU45zs7OemXcv3+fv//+u8B9kOjsQgghRAlhzE3JgYGBBAQE6KRZWFgYzDtt2jQWLFjw1PIuXbpktLYZMmPGDO0/N2rUiIyMDBYuXMj48eONWk+hZ3yOHj1Kt27dcHNzQ6VSsWPHDp33Go2GmTNn4urqipWVFd7e3ly5YviSwKysLBo2bIhKpSI2Nlbn3ZYtW2jYsCGlS5emcuXKepHbExMTGThwIDVr1sTExISJEycWtitCCCHES8vQFS75DXwmTZrEpUuXnvpUrVoVFxcX7ty5o/Pto0ePSElJwcXFxWDZLi4uZGdnk5qaqpOenJyc7zcAzZs359atW9rlORcXF72TYMnJydjZ2T01asOTCj3wycjIoEGDBqxYscLg+08//ZTPP/+c4OBgTp48ibW1NT4+PmRmZurl/eCDDwxuhPrhhx8YNGgQo0eP5vz583z55ZcsXbqU5cuXa/NkZWXh6OjI9OnT9e4BEkIIIV5Eao3GaE9hODo64uHh8dTH3NwcLy8vUlNTiY6O1n4bGRmJWq2mefPmBsv29PSkVKlS2kDkAHFxccTHx+Pl5ZVvm2JjYylTpox2sObl5aVTBkBERMRTyzDkX53qenIHtkajwc3NjUmTJjF58mTg8W5uZ2dnQkND6d+/v/bbH374gYCAAL7//nvq1q3L2bNnadiwIQADBw4kJydHJzzFF198waeffkp8fDwqlUqnHe3ataNhw4YsW7as0H2QU11CCCEKSulTXe9U1j/i/by+u7nNaGX9U+fOnUlOTiY4OJicnByGDx9OkyZN2LBhAwC3b9+mQ4cOfPPNNzRr1gyA999/n3379hEaGoqdnR3jxo0D4MSJEwDs3r2b5ORkWrRogaWlJREREUyePJnJkyczZ84c4PFx9tdff50xY8YwYsQIIiMjGT9+PHv37sXHx6fA7Tfq5ubr16+TlJSks+va3t6e5s2b6+y6Tk5OZuTIkXz77beULl1ar5ysrCyD0d1v3brFzZs3jdlkIYQQQhTC+vXr8fDwoEOHDnTp0oXWrVuzatUq7fucnBzi4uJ4+PChNm3p0qX85z//oXfv3rzxxhu4uLiwbdv/DcxKlSrFihUr8PLyomHDhnz11VcsWbKEWbNmafNUqVKFvXv3EhERQYMGDVi8eDGrV68u1KAHjLy5OW93tqFd13nvNBoNw4YNY/To0TRp0oQbN27olePj44O/vz/Dhg2jffv2XL16lcWLFwOP9/a4u7sbs9lCCCFEifAixNgqW7asdnbHEHd3d55cTLK0tGTFihX5bpPx9fXF19f3mXW3a9eOs2fPFq7BTyjyU11ffPEFDx48IDAwMN88I0eO5Nq1a/znP/8hJycHOzs7JkyYwOzZsw1GeC+orKwsvTsMsjW5Eq9LCCFEiaDkjcviMaMudeXtzja06zrvXWRkJFFRUVhYWGBmZkb16tUBaNKkCUOHDgUe7x1asGAB6enp3Lx5k6SkJO064ZMR3gvD0J0GoQ8MnzgTQgghxMvHqAOfKlWq4OLiorPr+v79+5w8eVK76/rzzz/nl19+ITY2ltjYWPbt2wc8DjY6d+5cnfJMTU2pUKEC5ubmbNy4ES8vLxwdHZ+7fYGBgaSlpek8w2xrPHd5QgghhDG9CEFKX3SFXupKT0/n6tWr2p+vX79ObGwsZcuWpVKlSkycOJFPPvmEGjVqUKVKFWbMmIGbm5v25FelSpV0yrOxsQGgWrVqvPbaawDcvXuXrVu30q5dOzIzMwkJCdEGNvunvLt/0tPT+fPPP4mNjcXc3Jw6deoYbLuFhYXeHQayzCWEEKKkeBH2+LzoCj3wOXPmDO3bt9f+nHcr5NChQwkNDeWDDz4gIyODUaNGkZqaSuvWrQkPD9c7pfUs69atY/LkyWg0Gry8vDh8+LB2uStPo0aNtP8cHR3Nhg0bqFy5ssEN00IIIURJJ3t8lCfR2eUeHyGEEAWk9D0+b1fubrSytt7cZbSyXiYSq0sIIYQoIWRvjvJk4COEEEKUEK/4IkyRMOqpLiGEEEKIkszo0dm3bdtGp06dKFeunMGo6ykpKYwbN45atWphZWVFpUqVGD9+PGlpaXp1hYaGUr9+fSwtLXFycmLMmDHad3FxcbRv3x5nZ2csLS2pWrUq06dPJycnp7BdEkIIIUoENRqjPcKwQi915UVnHzFiBL166QdTy8jIoHXr1vTt25eRI0fqvU9ISCAhIYFFixZRp04dbt68yejRo0lISGDr1q3afEuWLGHx4sUsXLiQ5s2bk5GRoXNaq1SpUgwZMoTGjRvj4ODAL7/8wsiRI1Gr1cybN6+w3RJCCCGKnezxUZ5Ro7P/040bN6hSpYpO1PX8hIWF8c4775CRkYGZmRl//fUXFSpUYPfu3XTo0KHA7QkICOD06dMcO3aswN/IqS4hhBAFpfSprm6V/mO0snbH7zFaWS+TErHHJy0tDTs7O8zMHk9ARUREoFaruX37NrVr1+a1116jb9++/PHHH/mWcfXqVcLDw2nbtm1RNVsIIYQwKo0R/yMMK/aBz927d/n4448ZNWqUNu3333/XLlktW7aMrVu3kpKSQseOHcnOztb5vmXLllhaWlKjRg3atGnDRx99VNRdEEIIIYxC9vgor1gHPvfv36dr167UqVOH2bNna9PVajU5OTl8/vnn+Pj40KJFCzZu3MiVK1c4dOiQThmbN28mJiaGDRs2sHfvXhYtWpRvfVlZWdy/f1/nydbkKtU9IYQQQpQwxXaPz4MHD/D19cXW1pbt27dTqlQp7TtXV1cAnZhbjo6OlC9fnvj4eJ1yKlasqM2bm5vLqFGjmDRpEqam+jG4goKCmDNnjk7aSNtajLLzMFq/hBBCiOcl9/gor1hmfO7fv0+nTp0wNzdn165denG8WrVqBTw+sp4nJSWFu3fvUrly5XzLzZspUqsN74uX6OxCCCFKMonOrjyjR2dPSUkhPj6ehIQE4P8GLy4uLri4uGgHPQ8fPuS7777TLjnB41kdU1NTatasSY8ePZgwYQKrVq3Czs6OwMBAPDw8tAFS169fT6lSpahXrx4WFhacOXOGwMBA+vXrpzN79E8SnV0IIURJJpuSlWf06Oy7du1i+PDh2vf9+/cHYNasWcyePZuYmBhOnjwJQPXq1XXKvn79Ou7u7gB88803+Pv707VrV0xMTGjbti3h4eHaQY2ZmRkLFizg8uXLaDQaKleuzNixY/H39y9sl4QQQgjxipDo7HKPjxBCiAJS+h4f74o+Rivrxz/2G62sl4kEKRVCCCFKiFd8LqJIFPs9PkIIIYQQRUVmfIQQQogSQi4eVJ5Ro7Pn5OQwdepU6tWrh7W1NW5ubgwZMkR7witPSkoKgwYNws7ODgcHB/z8/EhPT9fJs2XLFho2bEjp0qWpXLkyCxcu1GvL4cOHady4MRYWFlSvXp3Q0NDCdkcIIYQoMSRkhfIKPfDJi86+YsUKvXcPHz4kJiaGGTNmEBMTw7Zt24iLi6N79+46+QYNGsSFCxeIiIhgz549HD16VCdkxQ8//MCgQYMYPXo058+f58svv2Tp0qUsX75cm+f69et07dqV9u3bExsby8SJE3n33XfZv182cwkhhBDCMMWis+c5ffo0zZo14+bNm1SqVIlLly5Rp04dTp8+TZMmTQAIDw+nS5cu3Lp1Czc3NwYOHEhOTg5hYWHacr744gs+/fRT4uPjUalUTJ06lb1793L+/Hltnv79+5Oamkp4eHiB+yCnuoQQQhSU0qe63qjQwWhlHb190GhlvUwU39yclpaGSqXCwcEBgKioKBwcHLSDHgBvb29MTEy09/tkZWXp3eZsZWXFrVu3uHnzprYcb29vnTw+Pj5ERUUp2BshhBBCORojPsIwRQc+mZmZTJ06lQEDBmBnZwdAUlISTk5OOvnMzMwoW7YsSUlJwOMBzLZt2zh48CBqtZrLly+zePFiABITE7XlODs765Tj7OzM/fv3+fvvv5XslhBCCCFeUIqd6srJyaFv375oNBpWrlxZqG9HjhzJtWvX+M9//kNOTg52dnZMmDCB2bNnY2Ly/GO1rKwssrKydNKyNbkStkIIIUSJIKe6lKfIjE/eoOfmzZtERERoZ3vgccyuO3fu6OR/9OgRKSkpuLi4AI/3Di1YsID09HRu3rxJUlISzZo1A6Bq1aracpKTk3XKSU5Oxs7ODisrK4PtCgoKwt7eXucJfXDFaP0WQggh/g01GqM9wjCjD3zyBj1Xrlzhxx9/pFy5cjrvvby8SE1NJTo6WpsWGRmJWq2mefPmOnlNTU2pUKEC5ubmbNy4ES8vLxwdHbXlHDyou3ErIiICLy+vfNsm0dmFEEKUZBqNxmiPMMyo0dldXV15++23iYmJYc+ePeTm5mr37ZQtWxZzc3Nq166Nr68vI0eOJDg4mJycHMaOHUv//v1xc3MD4O7du2zdupV27dqRmZlJSEgIYWFhHDlyRFvv6NGjWb58OR988AEjRowgMjKSLVu2sHfv3nzbLtHZhRBCiFdboY+zHz58WCc6e56hQ4cye/ZsqlSpYvC7Q4cO0a5dO+DxBYZjx45l9+7dmJiY0Lt3bz7//HNsbGyAxwOfbt26ce7cOTQaDV5eXsydO1dvRujw4cP4+/tz8eJFXnvtNWbMmMGwYcMK0x05zi6EEKLAlD7O3sytrdHKOpVw5NmZXkESnV0GPkIIIQpI6YFPU7c3jFbW6YSjRivrZSJBSoUQQgjxypCBjxBCCFFCvAibmwsSb/NJmZmZjBkzhnLlymFjY0Pv3r11TmaHhoaiUqkMPnknwQ8fPmzwfd5e4oKS6OxCCCFECfEiHEMfNGgQiYmJREREkJOTw/Dhwxk1ahQbNmzI9xt/f3/27t1LWFgY9vb2jB07ll69enH8+HEA+vXrh6+vr843w4YNIzMzU+/S47i4OJ1rcp58/ywy8BFCCCFEgVy6dInw8HCdeJtffPEFXbp0YdGiRdrT2f+UlpbGmjVr2LBhA2+++SYAISEh1K5dm59//pkWLVpgZWWlcwffn3/+SWRkJGvWrNErz8nJSRsG63kUeqnr6NGjdOvWDTc3N1QqFTt27NB5P3v2bDw8PLC2tqZMmTJ4e3trY3DledY0WVxcHO3bt8fZ2RlLS0uqVq3K9OnTycnJ0SknLCwMDw8PLC0tqVevHvv27Stsd4QQQogSw5hLXVlZWdy/f1/neTJ6QWEVJN7mk6Kjo8nJydGJr+nh4UGlSpXyja/5zTffULp0ad5++229dw0bNsTV1ZWOHTtqZ4wKo9ADn4yMDBo0aMCKFSsMvq9ZsybLly/n3Llz/PTTT7i7u9OpUyf+/PNPbZ5BgwZx4cIFIiIi2LNnD0ePHmXUqFHa96VKlWLIkCEcOHCAuLg4li1bxtdff82sWbO0eU6cOMGAAQPw8/Pj7Nmz9OzZk549e+pEaxdCCCFeJMa8udlQtIKgoKB/1b6CxNs09I25ubneLI2zs3O+36xZs4aBAwfqzAK5uroSHBzM999/z/fff0/FihVp164dMTExhepDoZe6OnfuTOfOnfN9P3DgQJ2flyxZwpo1a/j111/p0KFDgabJqlatqg1NAVC5cmUOHz7MsWPHtGmfffYZvr6+TJkyBYCPP/6YiIgIli9fTnBwcGG7JYQQQrxUAgMDCQgI0El78hLfPNOmTWPBggVPLe/SpUtGa9vTREVFcenSJb799lud9Fq1alGrVi3tzy1btuTatWssXbpUL+/TKLrHJzs7m1WrVmFvb0+DBg2AZ0+TvfXWW3rlXL16lfDwcHr16qVNi4qK0vsX6uPjo7f0JoQQQrwoNEbc3GwoWkF+Jk2a9MwLgKtWrVqgeJtPcnFxITs7m9TUVJ1Zn+TkZIPfrF69moYNG+Lp6fnMdjdr1oyffvrpmfn+SZGBz549e+jfvz8PHz7E1dWViIgIypcvDxRumqxly5bExMSQlZXFqFGj+Oijj7TvkpKScHZ21sn/tGkzkOjsQgghSjZ1Md0p7OjoqI2F+TT/jLeZNzDJL95mHk9PT0qVKsXBgwfp3bs38Hgvb3x8vF58zfT0dLZs2VLgJbnY2FhcXV0LlDePIvf4tG/fntjYWE6cOIGvry99+/bVGyEWxObNm4mJiWHDhg3s3buXRYsW/at2SXR2IYQQJZnGiP9Rwj/jbZ46dYrjx4/rxdu8ffs2Hh4enDp1CgB7e3v8/PwICAjg0KFDREdHM3z4cLy8vGjRooVO+Zs3b+bRo0e88847enUvW7aMnTt3cvXqVc6fP8/EiROJjIxkzJgxheqDIjM+1tbWVK9enerVq9OiRQtq1KjBmjVrCAwMLNQ0WcWKFQGoU6cOubm5jBo1ikmTJmFqaoqLi4vO5UeQ/7RZHkPrnedrD/o3XRVCCCFeKevXr2fs2LF06NBBJ95mnpycHOLi4nj48KE2benSpdq8WVlZ+Pj48OWXX+qVvWbNGnr16mXwuHp2djaTJk3i9u3blC5dmvr16/Pjjz8ajB/6NEVyj49ardYuMT3PNFleGTk5OajVakxNTfHy8uLgwYNMnDhRmyciIkJv2uyfJDq7EEKIkqy4lroKo2zZsk+9rNDd3V3v5mhLS0tWrFiR74nwPCdOnMj33QcffMAHH3xQuMYaUOiBT3p6OlevXtX+fP36dWJjYylbtizlypVj7ty5dO/eHVdXV+7evcuKFSu4ffs2ffr0AXSnyYKDg8nJydGbJlu/fj2lSpWiXr16WFhYcObMGQIDA+nXrx+lSpUCYMKECbRt25bFixfTtWtXNm3axJkzZ1i1atW//kURQgghioNSS1Ti/xR64HPmzBmdaaW8paOhQ4cSHBzMb7/9xrp167h79y7lypWjadOmHDt2jLp162q/edY0mZmZGQsWLODy5ctoNBoqV67M2LFj8ff31+Zp2bIlGzZsYPr06fzvf/+jRo0a7Nixg9dff/25fiGEEEII8fJTaZSMZPYCOPNaz+JughBCiBdEk1s7FC2/pmOTZ2cqoMt/njFaWS8TidUlhBBClBCy1KU8RY6zCyGEEEKURDLjI4QQQpQQL8Kprhed0aOz/9Po0aNRqVQsW7ZMJ/1Z0dkzMzMZNmwY9erVw8zMjJ49e+qVvW3bNjp27IijoyN2dnZ4eXmxf//+wnZHCCGEKDFK+gWGLwOjR2fPs337dn7++WftEfV/elZ09tzcXKysrBg/frxOGPt/Onr0KB07dmTfvn1ER0fTvn17unXrxtmzZwvbJSGEEEK8IowenR0eX1c9btw49u/fT9euXXXeFSQ6u7W1NStXrgTg+PHjpKam6tXx5CzSvHnz2LlzJ7t376ZRo0aF7ZYQQghR7DQadXE34aVn9M3NarWawYMHM2XKFJ27e/I8Kzr7v6n3wYMHlC1b9rnLEEIIIYqTGo3RHmGY0Tc3L1iwADMzM8aPH2/wfWGisxfGokWLSE9Pp2/fvs9dhhBCCFGcXvGr9YqEUQc+0dHRfPbZZ8TExKBSqYxZ9FNt2LCBOXPmsHPnTr1B1T9lZWVpY4blydbkSrwuIYQQ4hVh1KWuY8eOcefOHSpVqoSZmRlmZmbcvHmTSZMm4e7uDlCo6OwFsWnTJt599122bNmS70boPEFBQdjb2+s8oQ+uFLpOIYQQQgmy1KU8ow58Bg8ezK+//kpsbKz2cXNzY8qUKdqj5v+Mzp6nINHZDdm4cSPDhw9n48aNepuoDQkMDCQtLU3nGWZbo3CdFEIIIRSi0WiM9gjDjBqdvVKlSpQrV04nf6lSpXBxcaFWrVpAwaKzA1y8eJHs7GxSUlJ48OABsbGxADRs2BB4vLw1dOhQPvvsM5o3b67dH2RlZYW9vb3BtltYWGBhYaGTJstcQgghxKuj0EFKDx8+rBOdPc/QoUMJDQ3VS3d3d2fixIlMnDhRm5aSksLYsWPZvXu3TnR2Gxsbne9u3rypV15ec9u1a8eRI0cK3I78SJBSIYQQBaV0kFJXhzpGKysx9aLRynqZSHR2GfgIIYQoIKUHPi4OtY1WVlLqJaOV9TKRIKVCCCGEeGVIkFIhhBCihHjFF2GKhAx8hBBCiBJCjqErz+jR2YcNG4ZKpdJ5fH19te9v3LiBn58fVapUwcrKimrVqjFr1iyys7O1eWbPnq1XhkqlwtraWqeuZcuWUatWLaysrKhYsSL+/v5kZmYWtktCCCGEeEUUesYnLzr7iBEj6NWrl8E8vr6+hISEaH/+5xHy3377DbVazVdffUX16tU5f/48I0eOJCMjg0WLFgEwefJkRo8erVNmhw4daNq0qfbnDRs2MG3aNNauXUvLli25fPmydtC1ZMmSwnZLCCGEKHay1KU8RaKzW1hY5HsLs6+vr84MUNWqVYmLi2PlypXagY+NjY3O0fZffvmFixcvEhwcrE07ceIErVq1YuDAgcDj4+8DBgz4V4FOhRBCiOKkloGP4hQ51XX48GGcnJyoVasW77//Pvfu3Xtq/rS0tKdGVV+9ejU1a9akTZs22rSWLVsSHR3NqVOnAPj999/Zt28fXbp0MU4nhBBCiCImNzcrz+ibm319fenVqxdVqlTh2rVr/O9//6Nz585ERUVhaqp/S/LVq1f54osvtLM9T8rMzGT9+vVMmzZNJ33gwIHcvXuX1q1bo9FoePToEaNHj+Z///ufsbskhBBCiJeE0Qc+/fv31/5zvXr1qF+/PtWqVePw4cN06NBBJ+/t27fx9fWlT58+jBw50mB527dv58GDBwwdOlQn/fDhw8ybN48vv/yS5s2bc/XqVSZMmMDHH3/MjBkzDJYl0dmFEEKUZHKqS3mKX2BYtWpVypcvrxPfCyAhIYH27dvTsmVLVq1ale/3q1ev5j//+Q/Ozs466TNmzGDw4MG8++671KtXj7feeot58+YRFBSEWq02WJZEZxdCCFGSyVKX8hQf+Ny6dYt79+7h6uqqTbt9+zbt2rXD09OTkJAQTEwMN+P69escOnQIPz8/vXcPHz7U+y5vKS2/f+ESnV0IIYR4tRk1OnvZsmWZM2cOvXv3xsXFhWvXrvHBBx9QvXp1fHx8gP8b9FSuXJlFixbx559/ast68iTY2rVrcXV1NXiKrFu3bixZsoRGjRppl7pmzJhBt27dDO4lAonOLoQQomSTU13KK/TA58yZMzrR2QMCAoDHUdFXrlzJr7/+yrp160hNTcXNzY1OnTrx8ccfawccERERXL16latXr/Laa6/plP3PmRq1Wk1oaCjDhg0zOJCZPn06KpWK6dOnc/v2bRwdHenWrRtz584tbJeEEEKIEkEje3wUJ9HZJTq7EEKIAlI6Ort1aXejlZXx8IbRynqZSKwuIYQQooSQpS7lycBHCCGEKCFe8UWYIqH4qS4hhBBCiJJCZnyEEEKIEkI2NytPZnyEEEKIEuJFuMAwJSWFQYMGYWdnh4ODA35+fqSnpz/1m1WrVtGuXTvs7OxQqVSkpqY+V7m//vorbdq0wdLSkooVK/Lpp58Wuv0y8BFCCCFKiBdh4DNo0CAuXLhAREQEe/bs4ejRo4waNeqp3zx8+BBfX9+nxtN8Vrn379+nU6dOVK5cmejoaBYuXMjs2bOfGv3BEDnOLsfZhRBCFJDSx9lLmVcwWlk52beNVlaeS5cuUadOHU6fPk2TJk0ACA8Pp0uXLty6dQs3N7enfn/48GHat2/PX3/9hYODQ6HKXblyJR9++CFJSUmYm5sDMG3aNHbs2MFvv/1W4D7IjI8QQghRQmiM+GRlZXH//n2d58lA3YUVFRWFg4ODdnAC4O3tjYmJCSdPnlS03KioKN544w3toAfAx8eHuLg4/vrrr4JXphHPJTMzUzNr1ixNZmbmK1O39LloSZ9f/nqLs27p88tv1qxZeuOhWbNm/asy586dq6lZs6ZeuqOjo+bLL7985veHDh3SAJq//vqr0OV27NhRM2rUKJ33Fy5c0ACaixcvFrgPMvB5TmlpaRpAk5aW9srULX0uWtLnl7/e4qxb+vzyy8zM1KSlpek8+Q36pk6d+sxJpEuXLr0UAx85zi6EEEK8hAwF5s7PpEmTGDZs2FPzVK1aFRcXF+7cuaOT/ujRI1JSUvQCjRdGQcp1cXEhOTlZJ0/ez4WpWwY+QgghxCvO0dERR0fHZ+bz8vIiNTWV6OhoPD09AYiMjEStVtO8efPnrr8g5Xp5efHhhx+Sk5NDqVKlgMeBz2vVqkWZMmUKXJdsbhZCCCFEgdSuXRtfX19GjhzJqVOnOH78OGPHjqV///7aE123b9/Gw8ODU6dOab9LSkoiNjaWq1evAnDu3DliY2NJSUkpcLkDBw7E3NwcPz8/Lly4wObNm/nss88ICAgoXCcKvCgmdMjGwKIlfX756y3OuqXPRetV7PPL5N69e5oBAwZobGxsNHZ2dprhw4drHjx4oH1//fp1DaA5dOiQNs3QRmtAExISUuByNRqN5pdfftG0bt1aY2FhoalQoYJm/vz5hW7/K3+PjxBCCCFeHbLUJYQQQohXhgx8hBBCCPHKkIGPEEIIIV4ZMvARQgghxCtDBj7ima5evcr+/fv5+++/ARSN+iuEEEIoSQY+Il/37t3D29ubmjVr0qVLFxITEwHw8/Nj0qRJRdIGGXS9vP744w9u3bql/fnUqVNMnDiRVatWFWOrisbRo0d59OiRXvqjR484evRoMbRIiFeHDHyew6vyh7G/vz9mZmbEx8dTunRpbXq/fv0IDw9XtO7iHHTl5OQwYsQIrl+/rmg9BZWamvpS1j1w4EAOHToEPL7crGPHjpw6dYoPP/yQjz76SLF6/yk1NZXVq1cTGBiovUgtJiaG27dvK1pv+/bttfX9U1paGu3bt1e07uLyzTffGIwMnp2dzTfffFMMLRKvKrnHpxDu3btHv379iIyMRKVSceXKFapWrcqIESMoU6YMixcvVqxujUbD1q1bOXToEHfu3EGtVuu837Ztm9HrdHFxYf/+/TRo0ABbW1t++eUXqlatyu+//079+vVJT083ep15hgwZwp07d1i9ejW1a9fW1r1//34CAgK4cOGCYnUD2NvbExsbS5UqVRSt50kLFizA3d2dfv36AdC3b1++//57XFxc2LdvHw0aNHhp6i5Tpgw///wztWrV4vPPP2fz5s0cP36cAwcOMHr0aH7//Xej1vekX3/9FW9vb+zt7blx4wZxcXFUrVqV6dOnEx8fr+gfxiYmJiQnJ+uFCLh8+TJNmjTh/v37itUNcO3aNUJCQrh27RqfffYZTk5O/PDDD1SqVIm6desqUqepqSmJiYk4OTnppN+7dw8nJydyc3MVqRfgrbfeQqVS6aWrVCosLS2pXr06AwcOpFatWoq1QZQcMuNTCMU5AzJx4kQGDx7M9evXsbGxwd7eXudRQkZGhk4/86SkpBQ48N3zOnDgAAsWLOC1117TSa9RowY3b95UtG6Anj17smPHDsXreVJwcDAVK1YEHsegiYiI4IcffqBz585MmTLlpao7JydH+7+jH3/8ke7duwPg4eGhneFTUkBAAMOGDePKlStYWlpq07t06aLYclOvXr3o1asXKpWKYcOGaX/u1asXPXr0wMfHh5YtWypSd54jR45Qr149Tp48ybZt27R/gfnll1+YNWuWYvVqNBqDg49bt24p9v9heezt7YmMjCQmJgaVSoVKpeLs2bNERkby6NEjNm/eTIMGDTh+/Lii7RAlgwQpLYQDBw6wf//+YvnD+Ntvv2Xbtm106dJF0Xr+qU2bNnzzzTd8/PHHwOO/HanVaj799FPFp+OLc9AFj/+dfvTRRxw/fhxPT0+sra113o8fP16RepOSkrSDjz179tC3b186deqEu7v7vwoAWBLrrlu3LsHBwXTt2pWIiAjt/84SEhIoV66c0et70unTp/nqq6/00itUqEBSUpIideb9Aa/RaLC1tcXKykr7ztzcnBYtWjBy5EhF6s4zbdo0PvnkEwICArC1tdWmv/nmmyxfvtzo9TVq1Eg72OjQoQNmZv/3x05ubi7Xr1/H19fX6PX+k4uLCwMHDmT58uWYmDz++75arWbChAnY2tqyadMmRo8ezdSpU/npp58UbYsofjLwKYTi/MPY3t6eqlWrKlrHkz799FM6dOjAmTNnyM7O5oMPPuDChQukpKQo/jej4hx0AaxZswYHBweio6OJjo7WeadSqRQb+JQpU4Y//viDihUrEh4ezieffAI8/oNSyaWA4qh7wYIFvPXWWyxcuJChQ4dql9J27dpFs2bNjF7fkywsLAwuKV2+fLlAUaqfR0hICADu7u5MnjxZb0BdFM6dO8eGDRv00p2cnLh7967R6+vZsycAsbGx+Pj4YGNjo31nbm6Ou7s7vXv3Nnq9/7RmzRqOHz+uHfTA4+XGcePG0bJlS+bNm8fYsWNp06aNou0QJUSho3u9wjp37qyZPn26RqPRaGxsbDS///67Jjc3V9OnTx9N7969Fa07NDRU079/f83Dhw8VredJqampmk8++UTTp08fTefOnTUffvihJiEhQfF6z507p3FyctL4+vpqzM3NNW+//bamdu3aGmdnZ83Vq1cVr7+4jBkzRlO5cmWNt7e3ply5ctoAfRs3btQ0atTopav70aNHmpSUFJ2069eva5KTkxWp75/8/Pw0PXv21GRnZ2t/P9+8eVPTqFEjzYQJExSvv7hUqFBBc/z4cY1G8/j/x65du6bRaDSabdu2aapWrapYvaGhoZq///5bsfKfxsHBQbNz50699J07d2ocHBw0Go1Gc/nyZe0/i5ebzPgUQnHOgPTt25eNGzfi5OSEu7s7pUqV0nkfExOjSL329vZ8+OGHipT9NK+//jqXL19m+fLl2Nrakp6eTq9evRgzZgyurq5F1o7s7GyuX79OtWrVdKbolbJ06VLc3d35448/+PTTT7V/O05MTOS///3vS1e3RqMhOjqaa9euMXDgQGxtbTE3Nzc4s2psixcv5u2338bJyYm///6btm3bkpSUhJeXF3PnzlW07uTkZCZPnszBgwe5c+eO3slQJWf3+vfvz9SpUwkLC9POpB4/fpzJkyczZMgQxeodOnQo8Pj3lKEDGpUqVVKs7sGDB+Pn58f//vc/mjZtCjxe6pw3b562z0eOHFFsY7coWeRUVyGlpaWxfPlyfvnlF9LT02ncuHGR/GHct29fDh06xNtvv42zs7PeJkElNiWGh4djY2ND69atAVixYgVff/01derUYcWKFZQpU8bodcLjTa++vr4EBwdTo0YNRep4locPHzJu3DjWrVsHPF7+qFq1KuPGjaNChQpMmzatWNr1Mrl58ya+vr7Ex8eTlZWl/TWeMGECWVlZBAcHF0k7jh8/rvP72dvbW/E6O3fuTHx8PGPHjsXV1VXv93OPHj0Uqzs7O5sxY8YQGhpKbm4uZmZm5ObmMnDgQEJDQzE1NVWk3itXrjBixAhOnDihk675/5uelRzs5ebmMn/+fJYvX05ycjIAzs7OjBs3jqlTp2Jqakp8fDwmJiZ6ezjFS6hY55tEgZUuXVpz7NixIq3z9ddf1+zdu1ej0Wg0v/76q8bc3FwTGBioadGihWbYsGGK1l2+fHnN5cuXFa3jacaPH6/x9PTUHDt2TGNtba1dDtixY4emYcOGitb9zTffaFq1aqVxdXXV3LhxQ6PRaDRLly7V7NixQ9F6i7ruHj16aN555x1NVlaWzpLLoUOHNNWrVzd6ff+UnZ2tMTU11Zw7d07RevJjY2OjOXv2bJHXq1arNTdv3tQ8fPhQEx8fr9m7d69m8+bNRfJ7rWXLlpo33nhDs2/fPs3Zs2c1sbGxOk9RSUtL06SlpRVZfaLkkaWuQsrMzOTXX381OFWbdxxXCRUrVsTOzk6x8g25fv06derUAeD777+nW7duzJs3j5iYGMVPl73zzjusWbOG+fPnK1pPfnbs2MHmzZtp0aKFzt/G69aty7Vr1xSrd+XKlcycOZOJEycyd+5c7d+CHRwcWLZsmaIzAUVd97Fjxzhx4gTm5uY66e7u7opfIFiqVCkqVaqk+Ibx/FSsWLFYLj7VaDRUr16dCxcuUKNGDe0pvqIQGxtLdHQ0Hh4eRVanIUX9/6Oi5JGBTyGEh4czZMgQgycflJ6qXbx4MR988AHBwcG4u7srVs8/mZub8/DhQ+DxPSt5a+Fly5ZV/IK1R48esXbtWn788UeDx8mXLFmiaP1//vmn3kVr8Phkn6G7SIzliy++4Ouvv6Znz546g74mTZowefJkxeotjrrVarXB3zO3bt3SOWatlA8//JD//e9/fPvtt5QtW1bx+v5p2bJlTJs2ja+++qrIfj/D45NMNWrU4N69e0W+jFynTh1FTo0VRHHuqRIljwx8CmHcuHH06dOHmTNn4uzsXKR1v/POOzx8+JBq1apRunRpvc3Nhq6//7dat25NQEAArVq14tSpU2zevBl4vN9F6XXw8+fP07hxY219/6TkwCNPkyZN2Lt3L+PGjdOpc/Xq1Xh5eSlW7/Xr12nUqJFeuoWFBRkZGYrVWxx1d+rUiWXLlmljc6lUKtLT05k1a1aR3Fe1fPlyrl69ipubG5UrV9YbXCt1YAAeX3pa1L+f88yfP58pU6awcuVKXn/9dcXqedKCBQv44IMPmDdvHvXq1dPrs5IzMcOGDSM+Pp4ZM2YY3FMlXi0y8CmE5ORkAgICinzQA4//hljUli9fzn//+1+2bt3KypUrqVChAgA//PCD4heO5cVwKi7z5s2jc+fOXLx4kUePHvHZZ59x8eJFTpw4wZEjRxSrt0qVKsTGxlK5cmWd9PDwcGrXrq1YvcVR9+LFi/Hx8aFOnTpkZmYycOBArly5Qvny5dm4caPR63tS3v0yxaE4fj/nGTJkCA8fPqRBgwaYm5vrXKIIyg268jaNd+jQQSddUwSbm3/66SeOHTtGw4YNFatDvDhk4FMIb7/9NocPH6ZatWpFWm9OTg5HjhxhxowZRRo7qlKlSuzZs0cvfenSpUXWhuLSunVrYmNjmT9/PvXq1ePAgQM0btyYqKgo6tWrp1i9AQEBjBkzhszMTDQaDadOnWLjxo0EBQWxevVqxeotjrpfe+01fvnlFzZt2sSvv/5Keno6fn5+DBo0SO8PYyUoGZ7hWfKOdheH4hp0FedfZoprT5UomeQ4eyE8fPiQPn364OjoaHCqVqnbfKH4gmbmyczMJDs7WydNyanpjIwM5s+fr12Tf3IjudIBLIvT+vXrmT17tnYTtZubG3PmzMHPz++lrru4REdHc+nSJeDx5nVDy33GFh8f/9T3St5p8yo6cOAAixcvLvI9VaJkkoFPIaxZs4bRo0djaWlJuXLldNaJVSqVon8YDx06lIYNG+Lv769YHU/KyMhg6tSpbNmyhXv37um9V3JqesCAARw5coTBgwcbXJOfMGGCYnXnUavVXL161eDA64033lC8/ocPH5Kenm5wk/WLWveuXbsKnFfJU5IAd+7coX///hw+fBgHBwcAUlNTad++PZs2bVIsbAU83mT8tH0mSm+2zc3NZceOHToDvu7duyt2hw/wzMCvSv6eKlOmDA8fPuTRo0dFvqdKlDwy8CkEFxcXxo8fz7Rp03RivhSFTz75hMWLF9OhQ4ciC5o5ZswYDh06xMcff8zgwYNZsWIFt2/f5quvvmL+/PkMGjTI6HXmcXBwYO/evbRq1UqxOp7m559/ZuDAgdy8eVNvilzp/Qgvs4L+vimKX+N+/frx+++/880332j3MF28eJGhQ4dSvXp1RfcZ/fLLLzo/5+TkcPbsWZYsWcLcuXPp1auXYnVfvXqVLl26cPv2bWrVqgVAXFwcFStWZO/evYot5Rv6d//PwZ+S/77zLiLNT3EuPYqiJwOfQihbtiynT58u8j0+wFOXuJSabapUqRLffPMN7dq1w87OjpiYGKpXr863337Lxo0b2bdvn9HrzFOlShX27dun+Ibe/DRs2JCaNWsyZ84cgzNOeVG2jSEvenVBGPukUXHWXdzs7e358ccftSEM8pw6dYpOnTrx/9q797gc7/8P4K/77qCjDiiHkkgphPadQ5gkp5aMbbY55etYc2iJaaOmiDBnWfZlzqe+luOYU99ySAxbis6izcihlSGjev/+8Otad/d9O8x93Vfq/Xw8ejxc1916fRj1vt/X51BUVKT1Mf3www9YtGgREhISRMvw9vYGEWHr1q3CMv579+5h+PDhkMvl+OGHH0TJLS4uVriuKPZCQ0MRGRmpNOmZMbHw5OZX4Ofnh507d+LLL7/UenZeXp7WMwsLC4UT4evWrSu0g7t164aAgABRs+fMmYOwsDBs3LhRK+c2VZWdnY1du3bBwcFB9CwpVxdJmS218vJypUcewLPNDas+2tQWJycn/PTTT6JmJCYmIjk5WWHvonr16iEqKkrUDquqNwu9e/eGvr4+pk6digsXLmg07/79+8I8xBftO8abGtYuXPi8grKyMixcuBCHDx+Gq6ur0jdNsTfVq1DRpBN7L4rmzZsjLy8PTZs2RatWrRAbG4uOHTti//79wpwIsSxevBi5ubmwtrbW6qGsFTp16oScnBytFD5Sri6SMht4No8sMTER+fn5SpPnxVwsAACenp4IDAzE9u3b0bhxYwDAjRs3EBQUJHr3oeoPYiLCzZs3MXv2bNE3FqxTpw7+/PNPpfsPHjxQ2kVbG6ytrZGZmanxr2thYYGbN2/CysoK5ubmKr9famMpPat+uPB5BampqcKKj7S0NIXXtLEh1qZNm7Bo0SJkZ2cDABwdHTF9+nSMGDFClLx///vfSElJQY8ePRASEoIBAwZg1apVePr0qehFnhSdiEuXLgm/njx5MoKDg3Hr1i2VK/hcXV21Pbwa5+eff4a3tzcePXqEhw8fwtLSEnfv3oWRkRGsrKxEL3xWrVoFX19fNGvWTDi64ddff0WbNm2wZcsWUbNV/SAmItja2mLHjh2iZvv4+GD8+PFYt24dOnbsCAA4e/Ys/P39RZ1QXvnfF/B3sRcVFSXK/jrx8fFCV0vqfcFY9cJzfN4QS5YsQWhoKCZNmiS0o0+dOoXo6GjMnTtXK6u9rl+/jgsXLsDBwaFG/uCvWGmj7p9ExWtivkMsKyvD0qVLERsbq7ILIubqE21ne3h4wNHRETExMTAzM0NKSgr09PQwfPhwBAYGijrBtwIR4dixY8jIyAAAODs7a+V09qqbYMrlcjRo0AAODg7Q1RX3/WhRURH8/Pywf/9+oaAvLS2Fr68vNmzYoNH5a5Wp+/fVuXNnfPfdd6Ke4ZWfnw9bW1uVxeavv/7K2wfUMlz4vCHs7e0RHh4unJdVYePGjZg9e7boc4AeP34MAwMDUTOqKioqwq5du5Cbm4vp06fD0tISFy9ehLW1tbCLtCZdv379pT+36u7GmhIWFoa1a9ciODgYs2bNwsyZM3Ht2jXs2bMHYWFhonZBtJ1tbm6Os2fPwsnJCebm5jhz5gycnZ1x9uxZ+Pn5CcUIE0dOTo6wnN3Z2Vn0x7pV/31VFHva+L6io6MjPPaq7N69e7CysuJHXbWNFk6Ar1F++uknmj59On300Uc0aNAghQ8x1alTh7Kzs5XuZ2VlUZ06dUTJLC0tpYiICGrcuDHp6OhQbm4uERHNmjWL1q5dK0pmhZSUFGrQoAE5ODiQrq6ukD1z5kwaMWKEqNlSat68OR04cICIiExMTCgnJ4eIiJYvX06ffPJJjcquX78+ZWVlERFRy5Yt6ccffyQiovT0dDIyMtJ4XlWTJ0+m5cuXK91fuXIlBQYGip6fk5NDkyZNol69elGvXr1o8uTJwp850yyZTEa3b99Wun/t2jWt/F1j1Yt2N6N5w+3YsQPu7u5IT0/H7t278fTpU1y+fBnx8fGitYcrODg4IDY2Vun+zp07RZsMGRkZiQ0bNmDhwoUKkx7btGmjleMTRo0ahezsbIV3hN7e3i/cCE0TNm7cqLCs9/PPP4e5uTnc3d1fqTP0qirmFAGAiYmJsATYx8dHtGXGUmV36NBBWMHUo0cPhIWFYevWrfjss8+0cnjm999/r3IVk7u7O3bt2iVq9uHDh+Hi4oJz587B1dUVrq6uOHv2LFq3bo2jR4+Kmv3+++9jwYIFSvcXLlyIDz/8UNTsxMREDBgwAA4ODnBwcICvry9OnjwpWt7UqVMxdepUyGQyhIaGCtdTp05FYGAgPvroIz6/qzaSuvJ6k7Rt25ZWrVpFRM/eEefm5lJ5eTmNGzeOwsLCRM3etWsX6ejoUN++fSkiIoIiIiKob9++pKurS3FxcaJktmjRgo4dO0ZEf/9+iZ69Izc3Nxcls0LdunWFd7+Vs69duyZah6syR0dHOn78OBERJSUlkaGhIa1Zs4YGDBgganfP0dGRkpOTiYioa9euNH/+fCIi2rFjBzVo0EC0XCmyf/rpJ4qPjyciooKCAurbty+ZmpqSm5sb/fLLLxrPq0pdFzU7O1v0v2Pt27enGTNmKN2fMWMGdejQQdTs+vXr06VLl5TuX7p0iaysrETL3bx5M+nq6tKQIUNo+fLltHz5choyZAjp6enR1q1bRcn08PAgDw8Pkslk5O7uLlx7eHhQnz59aPz48ULXkdUeXPi8AiMjI8rLyyMiIktLS+Gbx5UrV6hhw4ai558/f56GDRtGbm5u5ObmRsOGDaOLFy+KlmdgYEDXrl0jIsXi4/Lly2RsbCxaLhFRgwYNhN9b5ewjR46QjY2NqNlERIaGhnT9+nUiIvr888+Fx2tpaWlUv3590XJnzJhBkZGRRPSs4NDV1SUHBwfS19dX+YOypmRLoXXr1rRy5Uql+ytWrCBnZ2dRs+vUqaPyB25mZqboRZeBgQFlZGQo3U9PTycDAwPRclu1akVLlixRur948WJq1aqVaLlERKNGjaLi4mJRM9ibgwufV9CkSROh2Gnbti1t27aNiJ51BOrWravxvKCgIHrw4AERESUmJtLTp081nvE8bm5utHnzZiJSLD7Cw8OpW7duomaPGTOG3nvvPXry5AmZmJjQ1atX6fr169ShQwetzL+oXHi1b9+eNm3aRETP5mWIXfRVlpSURIsXL6Z9+/ZpLVOq7ISEBDp48CAVFhZqJW/dunVkaGhIYWFhlJCQQAkJCRQaGkpGRkb07bffipptY2NDsbGxSvd37txJtra2oma//fbbFB4ernT/q6++Ijc3N9Fy9fX1JeuwMVYZFz6v4JNPPqHFixcTEVFERAQ1aNCAxo4dS3Z2dqI8/tDV1aVbt24REZFcLqeCggKNZzzPnj17yMzMjKKiosjIyIgWLVpEY8eOJX19fTpy5Iio2UVFReTl5UXm5uako6NDtra2pKenR927dxeKQTENHTqU3NzcaMyYMWRkZER3794lIqK9e/dS69atRc+vyaKiomjWrFnCdXl5OfXt25dkMhnJZDKytramtLQ0rYxl9erV1KRJEyHb3t6eNm7cKHpueHg4mZubU1RUFJ04cYJOnDhB8+fPJ3Nzc4qIiBA1e9++faSrq0sjR46kDRs20IYNG2jEiBGkq6tLu3fvFi23RYsWFBMTo3T/m2++IQcHB9FyK0i1MIVVP1z4vIJ79+7RjRs3iIiorKyM5s+fTwMGDKCpU6eK8i7VwcGBvvzyS0pISCCZTEZ79uyhxMRElR9iOXHiBHl5eVGDBg3I0NCQunbtSocPHxYtr6pTp05RdHQ0LViwgI4ePaq13D/++IMmTpxIvr6+dOjQIeF+WFgYzZ07V+N5mZmZdPbsWYV7x44dIw8PD3r77beFR1Bi0HZ2hw4daMeOHcJ1bGwsGRoa0qlTp+jevXv07rvv0ocffqjRzBe5ffs2/fnnn1rLKy8vpyVLligUXU2aNKFly5ZReXm56PkHDhwgd3d3MjIyonr16lHPnj0pISFB1MzVq1eTvr4++fv706ZNm2jTpk00YcIEqlOnjsqCSJO2b99Oenp65OPjQ/r6+uTj40OOjo5kZmZGo0aNEjWbVT9c+FRju3fvJmtra5LJZCSXy4VvkFU/5HK51EPVmEePHtH+/fuF65CQEAoKChI+pk+fTiUlJRKOUBzvvfcehYaGCtdXr14lQ0ND6tOnD02ZMoVMTExo6dKlNSLb3Nycrly5IlyPGjVKYYuCM2fOaGUeV1XaftRW4f79+3T//n2tZkolLi6OunbtSpaWlmRpaUldu3alPXv2iJ4r5cIUVv1w4fMSKoqL533o6OiIlv/nn3+STCajrKwsKioqUvlRU3zzzTfk4+MjXJuYmFCnTp2ElRgNGzZUOUFSE1JSUqisrEz49fM+NM3GxoaSkpKE6zlz5lC7du2E67Vr1ypcv8nZleeLERE5OTnRN998I1xfv35d1Em2Uj5qe/ToEe3du1dloVNcXEx79+6lx48fi5KtSklJCW3YsIGio6Nr9OomqRemsOqFC5+XsGfPHrUfM2bMIENDQ9En5yUkJGhlcrO5uTlZWFi81IcYunXrpjCZtuoPyc2bN1Pnzp1FyZbJZMI8KlVdtoprMTpsBgYGlJ+fL1x7enoq/HDOyckhMzMzjedKkd2uXTtav349ET0rcmQyGV2+fFl4/fTp09SkSRON5VUl5aO2ZcuWkaenp9rXe/XqJXQmNC0oKIgmTZokXP/111/Url070tPTIzMzMzI2NlYogDWlsLCQVqxYoXJVVVFRkdrXNEnbC1NY9caHlL6EgQMHKt3LzMxESEgI9u/fj2HDhiEiIkLUMXh6emply/Vly5Zp5Ov8Uzk5OcImegBgYGAAufzvfTY7duyIiRMnipKdl5eHBg0aCL/WJktLS9y8eRO2trYoLy/H+fPnMXXqVOH1J0+eqD1D7E3LnjhxIiZNmoSTJ08iOTkZXbp0gYuLi/B6fHy8cBiwGPLy8hTOmjt48CA++OADYTPDWbNmibaR39atWxEaGqr29c8++wwRERGi/B0/cuQI5s2bpzCW/Px8ZGdno2nTphg9ejTmzp2r8c0qV61ahUuXLmHy5MlKr5mZmeHkyZO4f/8+Zs6cqdHcyt555x0cPXoUbdu2xYcffojAwEDEx8fj6NGj8PT0FC2XVVNSV15vmhs3btDYsWOFiXKpqalaya3cjag6HjEfC2ibuj1GKqSnp9fIpa9Dhw4lHx8fys/Pp8WLF5OJiYnC6rVdu3aRq6trjclet24dvffee+Tv7083b95UeC0gIEC0TTmJpH3UZm5uLuwPpcr169dF2xzU1NRUYTn5xx9/TOPGjROuf/75Z2rUqJHGc9u1aydshKrKsWPHqH379hrPrex5C1N+//13UbNZ9cMdn5dUXFyMefPmYeXKlWjfvj2OHz+O7t27i567YsUKAM9OBl+7di1MTEyE18rKynDixAlRTzXOzc3F+vXrkZubi+XLl8PKygqHDh1C06ZN0bp1a43n2djYIC0tDU5OTipfv3TpEmxsbDSeCwD79u176c/19fXVaHZkZCR69+4NOzs76OjoYMWKFTA2NhZe37x5s2jvTKXIHj16NEaPHq3ytdWrVytcR0VFwd/fH+bm5hrJbtGiBU6cOIHmzZsjPz8fWVlZeOedd4TXf/vtN9SrV08jWVWVlpbizp07ak8Dv3PnDkpLS0XJlsvlCp275ORkhe6Tubk5/vjjD43n5ubmPvdYnZYtWyI3N1fjuZVZWloKv5bL5QgJCcHjx48RHR2NDh064NatW6Lms2pG6srrTbBgwQKytLQkFxcXraxAqKxZs2bUrFkzkslkZGtrK1w3a9aMHB0dqU+fPsIxA5qWkJBAhoaG5OXlRfr6+sK75Pnz59P7778vSuaUKVPIxcVF5cqtR48ekYuLC02ZMkWUbHWr5rS1iu7p06f0yy+/CO9MK/vll1+EvYRqWvaLmJqaKnRoXte3335LxsbGNHr0aHJxcSF3d3eF1+fMmaMwwV6TOnXqRFFRUWpfnzdvHnXq1EmU7M6dOwv7kKWlpZFcLqerV68KryckJJCdnZ3Gc83MzOjMmTNqXz9z5oxo89ceP35MISEh9NZbb1GXLl2EfYq+++47aty4Mdna2j73/wermbjweQkymYyMjIzI19dXaeMrbW2C5eHhofVltpW/UVZ+PHD27FnRJp/eunWLGjZsSE2bNqWFCxcKk8gXLFhAtra21KhRI2FTx9pM08VAdc6u+mhKE6R61LZmzRoyNjZW2LKhwr59+8jY2JjWrFkjSnZcXBzp6+uTp6cnWVtbKxV3n3/+uSiTuj08PJ575Mnnn39OHh4eGs+t+NpmZmb0/vvvU6NGjUhXV5fGjRtHbdu2pe3bt1Npaakouax640ddL2HkyJGQyWSSjuF///uf1jNTU1Oxbds2pftWVla4e/euKJnW1tZISkpCQEAAQkJChNa8TCZD7969sXr1alhbW4uS/SYhkSY6V/dsTZHqUdv48eNx4sQJ+Pr6olWrVsIj3YyMDGRlZWHIkCEYP378a+eoMmjQIBw8eBAHDhxAnz59lCYbGxkZ4dNPP9V47qRJk/Dxxx/DxsYGAQEB0NHRAfDsUf3q1auxdOlSld9nNOG///0vNm3aBF9fX6SlpcHV1RWlpaVISUmR/Hs6k46MasJ3sVrit99+w759+5Cfn48nT54ovLZkyRKN59nY2CA2Nhbu7u4wNTVFSkoKmjdvjt27d2PatGmiP5cvLCxETk4OAMDBwUHhOb02HD9+HEuXLkV6ejoAwNnZGZ999hm8vLy0Og5VKv//qOnZUv5eAaBu3br45ZdfNJofGxuLbdu2ITs7G0QER0dHDB06FEOGDNFYxuv69NNPERERgfr167/215o5cybmz58PU1NT4c/x6tWrePDgAaZPn46oqKjXzlBFX18feXl5aNKkCQDA0NAQ586dU1g5ymof7vi8IY4fPw5fX180b94cGRkZaNOmDa5duwYigpubmyiZH3/8MWbMmIH//ve/kMlkKC8vx+nTpzFt2jSMHDlSlMzKLC0t0bFjR9FzVFm9ejUCAwPxwQcfIDAwEMCzyaDe3t5YunSpaEvqWfUjxnvDIUOGvFSRo+mJ3a9iy5YtmDZtmkYKn8jISAwcOBBbt25FTk4OiAg9evTA0KFDRf03XlZWBn19feFaV1dXYYEIq5244/OG6NixI/r374/w8HDhHbCVlRWGDRuGfv36ISAgQOOZT548wcSJE7FhwwaUlZVBV1cXZWVlGDp0KNavXw9d3ZpbN9vY2CAkJASTJk1SuB8dHY158+bhxo0bEo3sGe74aI+U+WJ0m16WVL9vTXaa5HI5+vfvjzp16gAA9u/fD09PT4VViwAQFxf32lnszSF/8aew6iA9PV3osujq6qKkpAQmJiaIiIjAggULRMnU19fHf/7zH1y9ehUHDhzAli1bkJGRgc2bN9foogcAioqK0K9fP6X7ffr0QXFxsQQjUiTl/ARtZ3fv3h2GhoZazawuauP70i1btuD+/fsa+Vp+fn6wsrKCmZkZzMzMMHz4cDRu3Fi4rvhgtUvN/ulVgxgbGwvzeho1aoTc3FxhHx2xJhpXsLW1ha2trXAdFxeH2bNn49KlS6LmSsnX1xe7d+/G9OnTFe7v3bsXPj4+Eo3qb2/y5Oby8nIsWrQI+/btw5MnT9CrVy989dVXaoubgwcPvlYee7No8u/2+vXrNfa1WM3Bhc8bonPnzjh16hScnZ3h7e2N4OBgpKamIi4uDp07d9Z43po1a3D06FHo6+sjMDAQnTp1Qnx8PIKDg5GVlaWVOT7aVrFZJAC4uLggMjISCQkJ6NKlC4Bnc3xOnz6N4OBgrY8tPT0d69atw9dffw0AOHTokDBhU9teNzsyMhKzZ8+Gl5cXDA0NsXz5cty+fRvfffedBkfJGGOq8RyfN0TFCghXV1c8fPgQwcHBSEpKQsuWLbFkyRLY2dlpLCsqKgphYWFwdXVFRkYGiAgzZ87EypUrERgYiAkTJsDCwkJjedWFvb39S32eTCbD1atXRR4N8PDhQ+zYsQPr1q1DcnIyXFxckJaWpvGcwYMHq7xvZmYGR0dHjB07VjjDTBNatmyJadOmYcKECQCAY8eO4d1330VJSYnCuWzVhbe3N9atW4dGjRppPbs2zeWSOpfVHlz4MCVOTk748ssv4efnh5MnT6JHjx7w9vbGzp07lSYFMs07ffo01q1bh9jYWJSUlCAoKAhjx44V7WiSf//73yrvFxUVISUlBUVFRThx4gTatGmjkbw6deogJydH4fGpgYEBcnJyRDuORJWDBw9CR0cHffv2Vbh/+PBhlJeXo3///lobizqaLgJKS0sxb948jB49+oV/1gEBAZgzZ45GJhm/Ci58mOi0u18iexMYGBhQfn6+cK2vr0/nz5+XcEQ1X0FBAS1YsICcnJyoYcOGFBQURD/99BPp6urS5cuXJRtXWVkZjR49WqNHOMjlcrp9+7bCPRMTE4XjE7Shbdu29MMPPyjdP3TokGgHwr6q/v37a/wQTRMTE8rLy9Po19QkMXbqZqwynuNTjVlYWLz0CprCwkKN5f71118wMDAQrvX19bW+eaDU1O3qW0HT81Hs7OzwwQcfYPny5ejdu3e1eeQjl8sxZcoUjXY/iAijRo0SlhgDwOPHj+Hv76/QURR7iXF2djZcXFyU7rdq1UrYOFMsL9ttEmNit6enJxITE9GsWTONf211XqXTNHz4cNStW1dLI2O1ERc+1diyZcskyw4NDYWRkRGAZ/v5zJ07V2nZpxi7RVcXVU+pfvr0KdLS0lBUVCTKKel2dnY4deoUmjZtCjs7O9Eea/0TxsbGePTokca+nqojYIYPH66xr/+yzMzMcPXqVaUCICcnR/RHuiEhISp3KyYihISEiPqYrX///ggJCUFqaireeustpd+rr6+vxjN1dXWxaNGil1oU8c0332g8n7HKuPCpxvz8/FBWVoavv/76pZf+asI777yDzMxM4drd3V1pMm9NP+dm9+7dSvfKy8sREBCAFi1aaDwvIyNDmNvz9ttvw9HRUSgGpP6zPnr0KBwdHTX29TZs2KCxr/U6Bg4ciM8++wy7d+8W/p/m5OQgODhYlB/+lUnZbao4j0vVGxeZTIaysjJRcqXoNDGmCk9urubmzJmjsPT38OHD+OSTT3jpr0QyMzPh4eGBmzdvipbx4MEDbN++HevXr0dycrKwtf97772n0dVVFfbt26fyfnFxMS5cuIC1a9di7dq1+PjjjzWSp24VWWW6urpo2LAhevfujQEDBmgkt6ri4mL069cP58+fFx6//Pbbb+jevTvi4uJEPSaiYcOG2LZtm1L38NixYxg6dChu374tWrZUYmJiEB4ejmHDhmmt08SYKlz4VHNvwtJfKbfV17aDBw/Cz88Pd+7c0UrelStXsG7dOmzZsgWFhYV4+vSpxjPU/T0yNTWFk5MTpk6dqrGiB1C/iqyy8vJy3L59G4mJiZg2bRoiIiI0ll8ZEeHo0aNISUmBoaEhXF1d8c4774iSVdmECRNw5swZpW7T+++/j7fffhtr164VfQzAs7lVlefziel536/E7DQxVhUXPtVcdVn6+zw1cfnp1KlTFa6JCDdv3sQPP/wAPz8/rFq1SqvjKS0txb59+16qW1KTHDhwAJ9++iny8/OlHopGSdltKisrw7x58xATE4OCggJkZWWhefPmCA0NRbNmzTBmzBjRshmrDniOTzVXWlqq9I5MT09PlHf+7G8///wzZDKZsH2+XC5HgwYNsHjx4heu+Pon5HL5C+fyyGQylJaWajzb29sb27dvFyavVz0R/N69e+jevTuuXLmi8ewX6datG/71r39p7OutWLEC48ePh4GBgcJO3apMmTJFY7lVmZmZISkpSZJuU2RkJDZu3IiFCxdi3Lhxwv02bdpg2bJlWil8tNlpYqwq7vhUc1VPFwZUnzAs5enCNanjU3GO1N69e/H06VN4enpi9uzZoh+SuXfvXrWvnTlzBitWrAARoaSkROPZcrkct27dgpWVFQDlR5cFBQVo3LhxjXgUYW9vj/Pnz6NevXrP3albW7tzS8HBwQFr1qxBr169FP7tZmRkoEuXLkorGjWFO02suuCOTzXn5+endE+Kpb+1RdVzpFasWIE7d+6IPpl84MCBSvcyMzMREhKC/fv3Y9iwYaLNc6mqJr8XysvLU/lrbagu3aYbN27AwcFB6X55ebmoneTq0GliDADv3Mxen6mpaY3ZadXBwYFiYmKE66NHj5K+vj6VlZVpbQw3btygsWPHkp6eHvn4+FBqaqqoeTKZjAoKCoTrqjvn3rp1i+RyuahjkEJ4eDg9fPhQ6f6jR48oPDxc43nNmjWju3fvCr9W92Fvb6/x7Mrc3Nxo8+bNRKT4/zo8PJy6desmWm6LFi3o2LFjSrnp6elkbm4uWi5jVXHHhym4f//+K++aSjWoQ5Cfnw9vb2/h2svLCzKZDL///rvok8mLi4sxb948rFy5Eu3bt8fx48fRvXt3UTOBZ491qs4vknrvIG0IDw+Hv7+/sFFnhUePHiE8PBxhYWEazZOy21RZWFgY/Pz8cOPGDZSXlyMuLg6ZmZnYtGkTDhw4IFquVJ0mxqqqHuuhWbVhYWEh7CHi6emJoqKiF/43hw4dQpMmTUQemXZINZl84cKFaN68OQ4cOIDt27cjKSlJK0UP8PcREoMHD8bgwYOF4yMqrsWYzF0dEJHKAi8lJUX0I1oiIiJU7oZdUlIi+iPNgQMHYv/+/Th27BiMjY0RFhaG9PR07N+/H7179xYt18XFBSdPnlS6v2vXLnTo0EG0XMaq4snNTIGZmRmSk5Ph7OwMuVyOgoICUTbNq66kmkwul8thaGgILy8v6OjoqP08MSaxv8y+OgCwfv16jWdLoeIMvOLiYtStW1eh+CkrK8ODBw/g7++P6Oho0cago6ODmzdvChPKK9y7dw9WVlY1YiJ5VXv37oWfnx+++OILREREIDw8XKHTJGbRxVhl/KiLKfDy8kLPnj3h7OwMABg0aBD09fVVfm58fLw2h6YVUk0mV3V+lbbUlILmZS1btgxEhNGjRyM8PFzhDDp9fX00a9YMXbp0EXUMUnabfv31V8hkMuHR7blz57Bt2za4uLhg/PjxouVWdJoiIiKETpObm5vonSbGquKOD1NQUlKCjRs3Ijc3F4sXL8a4ceOU5kBUWLp0qZZHx5jmJCYmwt3dHXp6elrLrA7dpu7du2P8+PEYMWIEbt26BUdHR7Rp0wbZ2dmYPHmyxuc2MVbdcOHD1OrZsyd2794t6i6yjGlT5cn79+/ff+7nvuok/5exceNGodu0bNkySbpNFhYWSE5OhpOTE1asWIGdO3fi9OnTOHLkCPz9/UXbv0iqThNjVfGjLqbW//73P+HXFfVxbVjtw2ouCwsLYW6Nubm5yr/PFY+hxJhnU/Eo1d7eXuvdpgpPnz4V5rAdO3ZMOBy0VatWoh6+O3ToUIVOk5eXF9q0aYOtW7fi1q1b3GliWsOFD3uuTZs2YdGiRcjOzgYAODo6Yvr06RgxYoTEI2Ps1cXHxwtzaCoX9tpQudvUoUMHlJSUqN2JW4xuU4XWrVsjJiYG7777Lo4ePYo5c+YAAH7//XfUq1dPtNy0tDR07NgRABAbG4u2bdsqdJq48GHawoUPU2vJkiUIDQ3FpEmT0LVrVwDAqVOn4O/vj7t37yIoKEjiETL2anr06AHg2bYFiYmJGD16tNYO+5W621RhwYIFGDRoEBYuXIhRo0ahXbt2AIB9+/YJhYkYpOo0MVYVz/Fhatnb2yM8PBwjR45UuL9x40bMnj1b0k3YGHtdpqamSE1NRbNmzbSSl5iYiK5du0JXVxeJiYnP/dyKAk0sZWVluH//PiwsLIR7165dg5GRkdISe03p1KkTevbsiXfffRd9+vRBcnIy2rVrh+TkZHzwwQf47bffRMllrCru+DC1bt68CXd3d6X77u7u/A6NvfE8PT2RmJiotcJHym4T8PeKsqrMzMzg6OiIadOmibqsXKpOE2NVcceHqdWmTRsMHToUX375pcL9uXPnYufOnUhNTZVoZIy9vpiYGISHh2PYsGF46623FDaoBCA8ihGDtrtNwLNOrSpFRUW4cOECdu7ciV27dmHAgAGijUGKThNjVXHhw9T6/vvv8dFHH8HLy0uY43P69GkcP34csbGxGDRokMQjZOyfk8vVn9gj9jybgQMHYvDgwSo3zJTKkiVLsGvXLiQlJWn060rdaWKsKi582HNduHABS5cuRXp6OgDA2dkZwcHBfLYOY69Bym6TOllZWejcuTMKCws1+nWrQ6eJscq48GGvLSoqCv7+/rzRIXsjxMfHY9KkSUhOTlZaNl5cXAx3d3fExMSIekislN0mdVJTU9G7d2/cunVLq7lidZoYU4dPZ2evbd68eRp/l8iYWJYtW4Zx48ap3CvHzMwMEyZMwJIlS0QdQ3l5udoPqQ4oXbduHdq3b6/1XB8fH2RkZGg9l9VevKqLvTZuGrI3SUpKChYsWKD29T59+uDrr78WJVvKbtPUqVNV3i8uLsbFixeRlZWFEydOaDz3Rf766y+1ByEzJgYufBhjtUpBQcFzj4rQ1dXFnTt3RMl+2W6TGIXPzz//rPJ+3bp10bt3b8TFxcHe3l7juS8iVaeJ1V5c+DDGapUmTZogLS0NDg4OKl+/dOkSGjVqJEq2lN0mbR/RUaG6dppY7cWFD2OsVvH29kZoaCj69esHAwMDhddKSkrw1VdfwcfHR5RsKbtNUqmunSZWe3HhwxirVWbNmoW4uDg4Ojpi0qRJcHJyAgBkZGQgOjoaZWVlmDlzpijZUnabpCJVp4kxdbjwYSqVlpZi27Zt6Nu3L6ytrZ/7ud27d4ehoaGWRsbY67G2tkZSUhICAgLwxRdfCJPzZTIZ+vbti+jo6Bf+nf+npOw2Mcae4X18mFpGRkZIT0+HnZ2d1ENhTBR//PEHcnJyQERo2bKlwlEKYigoKICbmxt0dHTUdpsuXrwoWuHFGOPChz2Hh4cHgoKCMHDgQKmHwliNcf36dQQEBODw4cMqu00834UxcXHhw9SKjY3FF198gaCgIJXb6ru6uko0MsbefNruNjHGnuHCh6mlalt9mUwGIpJsW33GGGPsdfDkZqZWXl6e1ENgjDHGNIo7PowxxhirNbjjw17oypUryM/Px5MnTxTu+/r6SjQixhhj7J/hwoepdfXqVQwaNAipqanC3B7g2TwfADzHhzHG2BtHefYqY/8vMDAQ9vb2uH37NoyMjHD58mWcOHEC//rXv5CQkCD18BhjjLFXxnN8mFr169dHfHw8XF1dYWZmhnPnzsHJyQnx8fEIDg5WewYPY4wxVl1xx4epVVZWBlNTUwDPiqDff/8dAGBnZ4fMzEwph8YYY4z9IzzHh6nVpk0bpKSkwN7eHp06dcLChQuhr6+Pb7/9Fs2bN5d6eIwxxtgr40ddTK3Dhw/j4cOHGDx4MHJycuDj44OsrCzUq1cPO3fuhKenp9RDZIwxxl4JFz7slRQWFsLCwkJY2cUYY4y9SXiOD3uhnJwcHD58GCUlJbC0tJR6OIwxxtg/xoUPU+vevXvo1asXHB0d4e3tjZs3bwIAxowZg+DgYIlHxxhjjL06LnyYWkFBQdDT00N+fj6MjIyE+x999BF+/PFHCUfGGGOM/TO8qoupdeTIERw+fBg2NjYK91u2bInr169LNCrGGGPsn+OOD1Pr4cOHCp2eCoWFhahTp44EI2KMMcZeDxc+TEnFRoXdu3fHpk2bhPsymQzl5eVYuHAhevbsKdXwGGOMsX+Ml7MzJRYWFoiOjka7du3g6ekJNzc3xMfHw9fXF5cvX0ZhYSFOnz6NFi1aSD1Uxhhj7JXwHB+mJDIyEhMmTEC/fv1w5coVxMTEwNTUFA8ePMDgwYMxceJENGrUSOphMsYYY6+MOz5Mpby8PIwZMwZXrlzBt99+C19fX6mHxBhjjL02LnzYc61atQpBQUFwdnaGrq5ig/DixYsSjYoxxhj7Z/hRF1Pr+vXriIuLg4WFBQYOHKhU+DDGGGNvGv5JxlT6z3/+g+DgYHh5eeHy5cto0KCB1ENijDHGXhsXPkxJv379cO7cOaxatQojR46UejiMMcaYxnDhw5SUlZXh0qVLSjs2M8YYY286ntzMGGOMsVqDd25mjDHGWK3BhQ9jjDHGag0ufBhjjDFWa3DhwxhjjLFagwsfxhhjjNUaXPgwxhhjrNbgwocxxhhjtQYXPowxxhirNbjwYYwxxlit8X+7e2qaW1ZsGQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zd2mTspW0y13",
        "outputId": "46a4332f-19fc-4bb3-9223-6113fea8e610"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
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              "(6825, 13)"
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    },
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      "cell_type": "code",
      "source": [
        "base.loc[base['NA_Sales'] > 1]"
      ],
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          "height": 424
        },
        "id": "KbnoAwIB03or",
        "outputId": "fb37b5a0-a5a4-43c2-a335-e6a6818bb395"
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            "text/plain": [
              "                                Name Platform  Year_of_Release         Genre  \\\n",
              "0                         Wii Sports      Wii           2006.0        Sports   \n",
              "2                     Mario Kart Wii      Wii           2008.0        Racing   \n",
              "3                  Wii Sports Resort      Wii           2009.0        Sports   \n",
              "6              New Super Mario Bros.       DS           2006.0      Platform   \n",
              "7                           Wii Play      Wii           2006.0          Misc   \n",
              "...                              ...      ...              ...           ...   \n",
              "1872                NCAA Football 14     X360           2013.0        Sports   \n",
              "1877                         NFL 2K1       DC           2000.0        Sports   \n",
              "1881                 Madden NFL 2004       XB           2003.0        Sports   \n",
              "1889                NCAA Football 13     X360           2012.0        Action   \n",
              "1953  Diablo II: Lord of Destruction       PC           2001.0  Role-Playing   \n",
              "\n",
              "            Publisher  NA_Sales  EU_Sales  JP_Sales  Critic_Score  \\\n",
              "0            Nintendo     41.36     28.96      3.77          76.0   \n",
              "2            Nintendo     15.68     12.76      3.79          82.0   \n",
              "3            Nintendo     15.61     10.93      3.28          80.0   \n",
              "6            Nintendo     11.28      9.14      6.50          89.0   \n",
              "7            Nintendo     13.96      9.18      2.93          58.0   \n",
              "...               ...       ...       ...       ...           ...   \n",
              "1872  Electronic Arts      1.01      0.00      0.00          77.0   \n",
              "1877             Sega      1.02      0.05      0.00          97.0   \n",
              "1881  Electronic Arts      1.02      0.02      0.00          92.0   \n",
              "1889  Electronic Arts      1.02      0.00      0.00          76.0   \n",
              "1953    Vivendi Games      1.03      0.02      0.00          87.0   \n",
              "\n",
              "      Critic_Count User_Score  User_Count Rating  \n",
              "0             51.0          8       322.0      E  \n",
              "2             73.0        8.3       709.0      E  \n",
              "3             73.0          8       192.0      E  \n",
              "6             65.0        8.5       431.0      E  \n",
              "7             41.0        6.6       129.0      E  \n",
              "...            ...        ...         ...    ...  \n",
              "1872          18.0        5.8        74.0      E  \n",
              "1877          22.0          6        47.0      E  \n",
              "1881          27.0        8.3        48.0      E  \n",
              "1889          20.0        5.5        65.0      E  \n",
              "1953          21.0        9.1       639.0      M  \n",
              "\n",
              "[584 rows x 13 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Platform</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Wii Sports</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>41.36</td>\n",
              "      <td>28.96</td>\n",
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              "      <td>51.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mario Kart Wii</td>\n",
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              "      <td>Racing</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>15.68</td>\n",
              "      <td>12.76</td>\n",
              "      <td>3.79</td>\n",
              "      <td>82.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>709.0</td>\n",
              "      <td>E</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Wii Sports Resort</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>15.61</td>\n",
              "      <td>10.93</td>\n",
              "      <td>3.28</td>\n",
              "      <td>80.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>8</td>\n",
              "      <td>192.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>New Super Mario Bros.</td>\n",
              "      <td>DS</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Platform</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>11.28</td>\n",
              "      <td>9.14</td>\n",
              "      <td>6.50</td>\n",
              "      <td>89.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>431.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Wii Play</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Misc</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>13.96</td>\n",
              "      <td>9.18</td>\n",
              "      <td>2.93</td>\n",
              "      <td>58.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>129.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1872</th>\n",
              "      <td>NCAA Football 14</td>\n",
              "      <td>X360</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Electronic Arts</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>77.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>74.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1877</th>\n",
              "      <td>NFL 2K1</td>\n",
              "      <td>DC</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Sega</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>97.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>6</td>\n",
              "      <td>47.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1881</th>\n",
              "      <td>Madden NFL 2004</td>\n",
              "      <td>XB</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Electronic Arts</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>92.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1889</th>\n",
              "      <td>NCAA Football 13</td>\n",
              "      <td>X360</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Electronic Arts</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>76.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.5</td>\n",
              "      <td>65.0</td>\n",
              "      <td>E</td>\n",
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              "    <tr>\n",
              "      <th>1953</th>\n",
              "      <td>Diablo II: Lord of Destruction</td>\n",
              "      <td>PC</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>Role-Playing</td>\n",
              "      <td>Vivendi Games</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>87.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>9.1</td>\n",
              "      <td>639.0</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>584 rows × 13 columns</p>\n",
              "</div>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-64e0fce8-4e0e-4c4d-beda-f92c310e6f3d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base = base.loc[base['NA_Sales'] > 1]"
      ],
      "metadata": {
        "id": "hc7rRObW1Z8G"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base.loc[base['EU_Sales'] > 1]"
      ],
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "b1g71C361jGL",
        "outputId": "003204ab-8a0c-45a6-febf-f7f4d992fff2"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Name Platform  Year_of_Release       Genre  \\\n",
              "0                    Wii Sports      Wii           2006.0      Sports   \n",
              "2                Mario Kart Wii      Wii           2008.0      Racing   \n",
              "3             Wii Sports Resort      Wii           2009.0      Sports   \n",
              "6         New Super Mario Bros.       DS           2006.0    Platform   \n",
              "7                      Wii Play      Wii           2006.0        Misc   \n",
              "..                          ...      ...              ...         ...   \n",
              "568              Medal of Honor       PS           1998.0     Shooter   \n",
              "573  Tom Clancy's Splinter Cell      PS2           2003.0      Action   \n",
              "591      Mario Strikers Charged      Wii           2007.0      Sports   \n",
              "610                  Crazy Taxi      PS2           2001.0      Racing   \n",
              "636       The Sims: Bustin' Out      PS2           2003.0  Simulation   \n",
              "\n",
              "                 Publisher  NA_Sales  EU_Sales  JP_Sales  Critic_Score  \\\n",
              "0                 Nintendo     41.36     28.96      3.77          76.0   \n",
              "2                 Nintendo     15.68     12.76      3.79          82.0   \n",
              "3                 Nintendo     15.61     10.93      3.28          80.0   \n",
              "6                 Nintendo     11.28      9.14      6.50          89.0   \n",
              "7                 Nintendo     13.96      9.18      2.93          58.0   \n",
              "..                     ...       ...       ...       ...           ...   \n",
              "568        Electronic Arts      1.44      1.09      0.00          92.0   \n",
              "573                Ubisoft      1.15      1.11      0.00          89.0   \n",
              "591               Nintendo      1.05      1.05      0.24          79.0   \n",
              "610  Acclaim Entertainment      1.13      1.12      0.06          80.0   \n",
              "636        Electronic Arts      1.07      1.19      0.00          81.0   \n",
              "\n",
              "     Critic_Count User_Score  User_Count Rating  \n",
              "0            51.0          8       322.0      E  \n",
              "2            73.0        8.3       709.0      E  \n",
              "3            73.0          8       192.0      E  \n",
              "6            65.0        8.5       431.0      E  \n",
              "7            41.0        6.6       129.0      E  \n",
              "..            ...        ...         ...    ...  \n",
              "568          17.0        8.6        80.0      T  \n",
              "573          24.0        7.7       102.0      T  \n",
              "591          47.0          8       124.0   E10+  \n",
              "610          15.0        7.9        46.0      T  \n",
              "636          26.0        8.4        42.0      T  \n",
              "\n",
              "[258 rows x 13 columns]"
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              "      <td>58.0</td>\n",
              "      <td>41.0</td>\n",
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              "      <td>129.0</td>\n",
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              "      <td>1.44</td>\n",
              "      <td>1.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>92.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>80.0</td>\n",
              "      <td>T</td>\n",
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              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>Tom Clancy's Splinter Cell</td>\n",
              "      <td>PS2</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Ubisoft</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>89.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>7.7</td>\n",
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              "      <td>T</td>\n",
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              "    <tr>\n",
              "      <th>591</th>\n",
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              "      <td>Wii</td>\n",
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              "      <td>Nintendo</td>\n",
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              "      <td>47.0</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>7.9</td>\n",
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              "      <th>636</th>\n",
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              "      <td>1.19</td>\n",
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              "      <td>81.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>8.4</td>\n",
              "      <td>42.0</td>\n",
              "      <td>T</td>\n",
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              "<p>258 rows × 13 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base = base.loc[base['EU_Sales'] > 1]"
      ],
      "metadata": {
        "id": "WXNFQNEL1xKK"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
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          "height": 206
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        "id": "hGiRhI242GFM",
        "outputId": "7a75a0b2-ce69-4bd2-8754-479a29015804"
      },
      "execution_count": 43,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "                    Name Platform  Year_of_Release     Genre Publisher  \\\n",
              "0             Wii Sports      Wii           2006.0    Sports  Nintendo   \n",
              "2         Mario Kart Wii      Wii           2008.0    Racing  Nintendo   \n",
              "3      Wii Sports Resort      Wii           2009.0    Sports  Nintendo   \n",
              "6  New Super Mario Bros.       DS           2006.0  Platform  Nintendo   \n",
              "7               Wii Play      Wii           2006.0      Misc  Nintendo   \n",
              "\n",
              "   NA_Sales  EU_Sales  JP_Sales  Critic_Score  Critic_Count User_Score  \\\n",
              "0     41.36     28.96      3.77          76.0          51.0          8   \n",
              "2     15.68     12.76      3.79          82.0          73.0        8.3   \n",
              "3     15.61     10.93      3.28          80.0          73.0          8   \n",
              "6     11.28      9.14      6.50          89.0          65.0        8.5   \n",
              "7     13.96      9.18      2.93          58.0          41.0        6.6   \n",
              "\n",
              "   User_Count Rating  \n",
              "0       322.0      E  \n",
              "2       709.0      E  \n",
              "3       192.0      E  \n",
              "6       431.0      E  \n",
              "7       129.0      E  "
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              "      <td>51.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>Wii Sports Resort</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>15.61</td>\n",
              "      <td>10.93</td>\n",
              "      <td>3.28</td>\n",
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              "      <td>73.0</td>\n",
              "      <td>8</td>\n",
              "      <td>192.0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>New Super Mario Bros.</td>\n",
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              "      <td>2006.0</td>\n",
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              "      <td>9.14</td>\n",
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              "      <td>65.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>431.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Wii Play</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Misc</td>\n",
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              "      <td>13.96</td>\n",
              "      <td>9.18</td>\n",
              "      <td>2.93</td>\n",
              "      <td>58.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>129.0</td>\n",
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              "        document.querySelector('#df-d10872b5-5ed9-4a59-9085-e8e9088f1e04 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d10872b5-5ed9-4a59-9085-e8e9088f1e04');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7c883cc1-6625-47a7-ab37-23413abe30be button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base['Name'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXzbaaPC2KMF",
        "outputId": "b9d2a82c-31e4-407e-ea98-eb494d8801dc"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Grand Theft Auto V                 4\n",
              "Assassin's Creed IV: Black Flag    3\n",
              "Call of Duty: Ghosts               3\n",
              "Battlefield 4                      3\n",
              "The Elder Scrolls V: Skyrim        3\n",
              "                                  ..\n",
              "Tekken 2                           1\n",
              "Destiny                            1\n",
              "Cooking Mama                       1\n",
              "Forza Motorsport 3                 1\n",
              "The Sims: Bustin' Out              1\n",
              "Name: Name, Length: 223, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base = base.drop('Name', axis = 1)"
      ],
      "metadata": {
        "id": "LCcnk3la3dFI"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIZa0Lxs3nkG",
        "outputId": "182e2caf-2aa0-4a47-b4b9-14831e21f6bd"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(258, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base.iloc[:, [1, 2]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZUMbppxg32vd",
        "outputId": "c630b1c8-c9d5-477b-d117-a8c71cc2727b"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Year_of_Release       Genre\n",
              "0             2006.0      Sports\n",
              "2             2008.0      Racing\n",
              "3             2009.0      Sports\n",
              "6             2006.0    Platform\n",
              "7             2006.0        Misc\n",
              "..               ...         ...\n",
              "568           1998.0     Shooter\n",
              "573           2003.0      Action\n",
              "591           2007.0      Sports\n",
              "610           2001.0      Racing\n",
              "636           2003.0  Simulation\n",
              "\n",
              "[258 rows x 2 columns]"
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              "<p>258 rows × 2 columns</p>\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5ceec44e-f1bf-47db-a082-68ad46e7ecf2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5ceec44e-f1bf-47db-a082-68ad46e7ecf2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9877c03c-531a-452b-9359-2f4d5e693624\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9877c03c-531a-452b-9359-2f4d5e693624')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9877c03c-531a-452b-9359-2f4d5e693624 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XRLkGYLR-CXo",
        "outputId": "653950fc-1e38-4d47-de4a-0462bd178b96"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Platform  Year_of_Release     Genre Publisher  NA_Sales  EU_Sales  JP_Sales  \\\n",
              "0      Wii           2006.0    Sports  Nintendo     41.36     28.96      3.77   \n",
              "2      Wii           2008.0    Racing  Nintendo     15.68     12.76      3.79   \n",
              "3      Wii           2009.0    Sports  Nintendo     15.61     10.93      3.28   \n",
              "6       DS           2006.0  Platform  Nintendo     11.28      9.14      6.50   \n",
              "7      Wii           2006.0      Misc  Nintendo     13.96      9.18      2.93   \n",
              "\n",
              "   Critic_Score  Critic_Count User_Score  User_Count Rating  \n",
              "0          76.0          51.0          8       322.0      E  \n",
              "2          82.0          73.0        8.3       709.0      E  \n",
              "3          80.0          73.0          8       192.0      E  \n",
              "6          89.0          65.0        8.5       431.0      E  \n",
              "7          58.0          41.0        6.6       129.0      E  "
            ],
            "text/html": [
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              "  <div id=\"df-72152383-9032-40a2-a824-8e97dbf1b0cf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Platform</th>\n",
              "      <th>Year_of_Release</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>NA_Sales</th>\n",
              "      <th>EU_Sales</th>\n",
              "      <th>JP_Sales</th>\n",
              "      <th>Critic_Score</th>\n",
              "      <th>Critic_Count</th>\n",
              "      <th>User_Score</th>\n",
              "      <th>User_Count</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Wii</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>41.36</td>\n",
              "      <td>28.96</td>\n",
              "      <td>3.77</td>\n",
              "      <td>76.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>8</td>\n",
              "      <td>322.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Wii</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>15.68</td>\n",
              "      <td>12.76</td>\n",
              "      <td>3.79</td>\n",
              "      <td>82.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>709.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Wii</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>15.61</td>\n",
              "      <td>10.93</td>\n",
              "      <td>3.28</td>\n",
              "      <td>80.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>8</td>\n",
              "      <td>192.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>DS</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Platform</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>11.28</td>\n",
              "      <td>9.14</td>\n",
              "      <td>6.50</td>\n",
              "      <td>89.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>431.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Wii</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Misc</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>13.96</td>\n",
              "      <td>9.18</td>\n",
              "      <td>2.93</td>\n",
              "      <td>58.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>129.0</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72152383-9032-40a2-a824-8e97dbf1b0cf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-72152383-9032-40a2-a824-8e97dbf1b0cf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72152383-9032-40a2-a824-8e97dbf1b0cf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-97a95ee3-da1d-4706-a733-b726005c0355\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-97a95ee3-da1d-4706-a733-b726005c0355')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-97a95ee3-da1d-4706-a733-b726005c0355 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# selecionando as features\n",
        "previsores = base.iloc[:, [0,1,2,3,7,8,9,10,11]].values\n",
        "vendas = base.iloc[:, 4:7].values"
      ],
      "metadata": {
        "id": "tKSljkRb3qy2"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "previsores"
      ],
      "metadata": {
        "id": "K0iPSbvo4Tus"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vendas"
      ],
      "metadata": {
        "id": "D-9WPMW24YYi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "onehotencoder = ColumnTransformer(\n",
        "    transformers=[(\"OneHot\",\n",
        "    OneHotEncoder(), [0, 2, 3, 8])],\n",
        "    remainder='passthrough')\n",
        "\n",
        "previsores = onehotencoder.fit_transform(previsores).toarray()"
      ],
      "metadata": {
        "id": "byI4XGNH42VM"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "previsores.shape, vendas.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3tTckSP6_2G",
        "outputId": "4ce9fc85-baa4-4379-81e7-288305405398"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((258, 61), (258, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# calculando quantas entradas em cada camada devemos definir\n",
        "\n",
        "### usamos os valores de shape/dimensão\n",
        "###((258, 61), (258, 3))\n",
        "\n",
        "### 61 + 3 / 2 -> 32 => ((input_dim + output_dim) / 2)\n",
        "### 61 -> 32 -> 32 -> 3"
      ],
      "metadata": {
        "id": "40NE9nY-Abxx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class regressor_torch(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.dense0 = nn.Linear(61, 32)\n",
        "        self.dense1 = nn.Linear(32, 32)\n",
        "        self.output = nn.Linear(32, 3)\n",
        "        self.activation = nn.Sigmoid()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.dense0(x)\n",
        "        x = self.activation(x)\n",
        "        x = self.dense1(x)\n",
        "        x = self.activation(x)\n",
        "        out = self.output(x)\n",
        "        return out"
      ],
      "metadata": {
        "id": "LKWVregq-Uvf"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regressor = regressor_torch()"
      ],
      "metadata": {
        "id": "BzkwoKYNAnXo"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.Adam(regressor.parameters())\n"
      ],
      "metadata": {
        "id": "j9Kl1Sw4Aq-p"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(previsores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHiDlOd0BDCB",
        "outputId": "d84c69fe-422c-4487-8d90-5533c7f5d07b"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsores = torch.tensor(previsores, dtype=torch.float)\n",
        "vendas = torch.tensor(vendas, dtype=torch.float)\n"
      ],
      "metadata": {
        "id": "nKmdnqYXBJUK"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(previsores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPtznFGBBYWx",
        "outputId": "b0879a3e-cd99-477e-82bc-c148ef0d9909"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Criando uma estrutura para o pytorch usar os dados como entrada"
      ],
      "metadata": {
        "id": "CGqb3R3yCi-K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = torch.utils.data.TensorDataset(previsores, vendas)\n",
        "\n",
        "# divide a base de dados em lotes de tamanho 10\n",
        "train_loader = torch.utils.data.DataLoader(dataset,\n",
        "                                           batch_size=10,\n",
        "                                           shuffle=True)"
      ],
      "metadata": {
        "id": "Eu56BKpECGCj"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset, train_loader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hsk-ZcQwCtT5",
        "outputId": "d074c923-fa07-488d-cb27-8bd30b15adeb"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataset.TensorDataset at 0x7d0dcf4c44f0>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x7d0dcf4c5570>)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3 TRAIN THE MODEL"
      ],
      "metadata": {
        "id": "zIoMO4ltDC4C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhTo9XmjDGsS",
        "outputId": "643104c5-85b4-4cc8-9be4-cad0b25d2156"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "regressor_torch(\n",
              "  (dense0): Linear(in_features=61, out_features=32, bias=True)\n",
              "  (dense1): Linear(in_features=32, out_features=32, bias=True)\n",
              "  (output): Linear(in_features=32, out_features=3, bias=True)\n",
              "  (activation): Sigmoid()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(5000):\n",
        "    regressor.train()\n",
        "    running_loss = 0. #Totaol loss\n",
        "    running_mse_0 = 0. # NA\n",
        "    running_mse_1 = 0. #EU\n",
        "    running_mse_2 = 0. #JP\n",
        "\n",
        "    # itera sobre os valores de cada lote_tamano_10 (train_loader)\n",
        "    for i, data in enumerate(train_loader):\n",
        "        inputs, labels = data\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        y_pred = regressor(inputs)\n",
        "\n",
        "        # Medindo os erros por cada variável predita\n",
        "        mse_0 = F.mse_loss(y_pred[:, 0], labels[:, 0]).item()\n",
        "        mse_1 = F.mse_loss(y_pred[:, 1], labels[:, 1]).item()\n",
        "        mse_2 = F.mse_loss(y_pred[:, 2], labels[:, 2]).item()\n",
        "\n",
        "        # erro das amostras somente para visualização\n",
        "        running_mse_0 += mse_0 * len(inputs)\n",
        "        running_mse_1 += mse_1 * len(inputs)\n",
        "        running_mse_2 += mse_2 * len(inputs)\n",
        "\n",
        "        loss = criterion(y_pred, labels)\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        loss = loss.item()\n",
        "        running_loss += loss * len(inputs)\n",
        "\n",
        "        if i % 500 == 0:\n",
        "            print(f\"Epoch: {epoch}\" +\n",
        "                f\"NA_erro: {running_mse_0/len(dataset)} \\n\"+\n",
        "                f\"EU_erro: {running_mse_1/len(dataset)}\" +\n",
        "                f\"JP_erro: {running_mse_2/len(dataset)}\" +\n",
        "                f\"loss: {running_loss/len(dataset)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FeMcND1EDNah",
        "outputId": "deea7bd2-a2fd-4473-fe0e-2092fe4ba51a"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Epoch: 2500NA_erro: 6.016594938529554 \n",
            "EU_erro: 3.0318459799123363JP_erro: 0.10527677314226018loss: 3.0512394276700276\n",
            "Epoch: 2501NA_erro: 0.06910115711448729 \n",
            "EU_erro: 0.09229019630786985JP_erro: 0.007391391220942949loss: 0.05626091199327809\n",
            "Epoch: 2502NA_erro: 0.13039995533551357 \n",
            "EU_erro: 0.030552102613818737JP_erro: 0.010512507008027661loss: 0.05715485229048618\n",
            "Epoch: 2503NA_erro: 0.06447629411091176 \n",
            "EU_erro: 0.037001322868258454JP_erro: 0.013391403264777606loss: 0.03828967726507852\n",
            "Epoch: 2504NA_erro: 0.08364950039590052 \n",
            "EU_erro: 0.034389253272566686JP_erro: 0.11041191197180933loss: 0.07615022419034972\n",
            "Epoch: 2505NA_erro: 0.48777997955795405 \n",
            "EU_erro: 0.23252744083256685JP_erro: 0.1217773995658224loss: 0.28069496154785156\n",
            "Epoch: 2506NA_erro: 0.15274712281633718 \n",
            "EU_erro: 0.1043248361395311JP_erro: 0.008494475091150564loss: 0.08852215700371321\n",
            "Epoch: 2507NA_erro: 5.702870095423026 \n",
            "EU_erro: 2.776956188586331JP_erro: 0.047047637229742006loss: 2.842291602792666\n",
            "Epoch: 2508NA_erro: 0.12337393538896428 \n",
            "EU_erro: 0.022738069988960444JP_erro: 0.042518030765444734loss: 0.06287667640419893\n",
            "Epoch: 2509NA_erro: 0.12117159459017968 \n",
            "EU_erro: 0.034285490364991414JP_erro: 0.008425934370173964loss: 0.054627680039221\n",
            "Epoch: 2510NA_erro: 0.0819005430206772 \n",
            "EU_erro: 0.04668009835620259JP_erro: 0.014504998229270758loss: 0.047695211661878484\n",
            "Epoch: 2511NA_erro: 0.06941297257593436 \n",
            "EU_erro: 0.05419309749159702JP_erro: 0.010843192653138508loss: 0.044816427452619684\n",
            "Epoch: 2512NA_erro: 0.22022005199461944 \n",
            "EU_erro: 0.1714945578759955JP_erro: 0.04137571467909702loss: 0.14436344767725745\n",
            "Epoch: 2513NA_erro: 0.11786292689715246 \n",
            "EU_erro: 0.04193695016609606JP_erro: 0.010532451461451922loss: 0.05677743937618049\n",
            "Epoch: 2514NA_erro: 0.05345040051512016 \n",
            "EU_erro: 0.012456994186076082JP_erro: 0.009899909875189612loss: 0.025269100370333177\n",
            "Epoch: 2515NA_erro: 0.1452885394872621 \n",
            "EU_erro: 0.09517916413240654JP_erro: 0.009189982746922693loss: 0.08321922878886379\n",
            "Epoch: 2516NA_erro: 0.07747727771138035 \n",
            "EU_erro: 0.02224196990331014JP_erro: 0.007330331113911414loss: 0.03568319849265638\n",
            "Epoch: 2517NA_erro: 0.09418663128401882 \n",
            "EU_erro: 0.09612847668255946JP_erro: 0.03863566836645437loss: 0.07631692775460176\n",
            "Epoch: 2518NA_erro: 0.6186592856118845 \n",
            "EU_erro: 0.3334456451179445JP_erro: 0.03884795562241428loss: 0.330317593360132\n",
            "Epoch: 2519NA_erro: 0.263335057931353 \n",
            "EU_erro: 0.06411972434021705JP_erro: 0.08702125660208769loss: 0.13815867808438087\n",
            "Epoch: 2520NA_erro: 0.30465009600617166 \n",
            "EU_erro: 0.20971388779869377JP_erro: 0.17850757569305656loss: 0.2309572049813677\n",
            "Epoch: 2521NA_erro: 0.8138567902321039 \n",
            "EU_erro: 0.4610581730687341JP_erro: 0.0672288504681846loss: 0.44738126355548236\n",
            "Epoch: 2522NA_erro: 0.17946139786594598 \n",
            "EU_erro: 0.07428743580515071JP_erro: 0.09316417597985083loss: 0.11563767758450767\n",
            "Epoch: 2523NA_erro: 0.043712895970011864 \n",
            "EU_erro: 0.03317951231963875JP_erro: 0.014777029900587806loss: 0.030556480551874914\n",
            "Epoch: 2524NA_erro: 6.361653527548147 \n",
            "EU_erro: 2.925253284069919JP_erro: 0.07625242998433668loss: 3.1210531190384265\n",
            "Epoch: 2525NA_erro: 0.06888537443885508 \n",
            "EU_erro: 0.03532141447067261JP_erro: 0.010528351909430453loss: 0.03824505233025366\n",
            "Epoch: 2526NA_erro: 0.07644631141840025 \n",
            "EU_erro: 0.04247925540273504JP_erro: 0.010260185298993606loss: 0.043061920838762625\n",
            "Epoch: 2527NA_erro: 0.11083220326623251 \n",
            "EU_erro: 0.03659167493036551JP_erro: 0.011269805967345718loss: 0.05289789780165798\n",
            "Epoch: 2528NA_erro: 0.3239232070686281 \n",
            "EU_erro: 0.35333507744840875JP_erro: 0.10115633639254311loss: 0.2594715495442235\n",
            "Epoch: 2529NA_erro: 0.17535453618958938 \n",
            "EU_erro: 0.05542433077050734JP_erro: 0.042409143706624824loss: 0.09106267330258391\n",
            "Epoch: 2530NA_erro: 0.34240227337031404 \n",
            "EU_erro: 0.36153516104055006JP_erro: 0.05152783652608709loss: 0.2518217693003573\n",
            "Epoch: 2531NA_erro: 0.6541078404862751 \n",
            "EU_erro: 0.0775469088739203JP_erro: 0.011946040299511695loss: 0.24786695953487425\n",
            "Epoch: 2532NA_erro: 0.7365669575772544 \n",
            "EU_erro: 0.15333542528078536JP_erro: 0.15907017759574477loss: 0.34965751707091813\n",
            "Epoch: 2533NA_erro: 0.14493618824685267 \n",
            "EU_erro: 0.031212817790896394JP_erro: 0.009961685006932695loss: 0.062036902405494865\n",
            "Epoch: 2534NA_erro: 0.04096442876860153 \n",
            "EU_erro: 0.057202475939610205JP_erro: 0.09985107784123384loss: 0.06600599418314852\n",
            "Epoch: 2535NA_erro: 0.7057111577470173 \n",
            "EU_erro: 0.13641171677168026JP_erro: 0.09274186089981434loss: 0.31162158463352413\n",
            "Epoch: 2536NA_erro: 0.09088762046754822 \n",
            "EU_erro: 0.017559953438219172JP_erro: 0.029300319594006207loss: 0.045915966810182084\n",
            "Epoch: 2537NA_erro: 0.21416634552238523 \n",
            "EU_erro: 0.28081411539122114JP_erro: 0.03485298202943432loss: 0.17661114995793778\n",
            "Epoch: 2538NA_erro: 0.0952805197516153 \n",
            "EU_erro: 0.022554545439490977JP_erro: 0.04486316858335983loss: 0.0542327507521755\n",
            "Epoch: 2539NA_erro: 0.6721448528674222 \n",
            "EU_erro: 0.45092146526011384JP_erro: 0.04685979480891265loss: 0.3899753555770992\n",
            "Epoch: 2540NA_erro: 0.06499053895935532 \n",
            "EU_erro: 0.054880371389462966JP_erro: 0.026695125324781552loss: 0.04885534907496253\n",
            "Epoch: 2541NA_erro: 0.17044852870379307 \n",
            "EU_erro: 0.05163201528002125JP_erro: 0.04533966382344564loss: 0.08914007696994515\n",
            "Epoch: 2542NA_erro: 0.17968996550685676 \n",
            "EU_erro: 0.024908120317976606JP_erro: 0.015356181434882704loss: 0.0733180887015291\n",
            "Epoch: 2543NA_erro: 0.7200397077456925 \n",
            "EU_erro: 0.4658948173818662JP_erro: 0.04604312338570292loss: 0.41065922079160233\n",
            "Epoch: 2544NA_erro: 0.19080439279245776 \n",
            "EU_erro: 0.0648808987565743JP_erro: 0.004685182095498078loss: 0.08679016615993293\n",
            "Epoch: 2545NA_erro: 0.6592162080513414 \n",
            "EU_erro: 0.45563250549079837JP_erro: 0.0495657763739889loss: 0.38813820180966874\n",
            "Epoch: 2546NA_erro: 0.09371304696844529 \n",
            "EU_erro: 0.03438041884769765JP_erro: 0.018831839164098103loss: 0.048975108205810074\n",
            "Epoch: 2547NA_erro: 0.10128274444461793 \n",
            "EU_erro: 0.022769487181375194JP_erro: 0.04553653935129328loss: 0.05652959494627723\n",
            "Epoch: 2548NA_erro: 0.10010404180186663 \n",
            "EU_erro: 0.08457312288210374JP_erro: 0.020282675129498624loss: 0.06831994814466136\n",
            "Epoch: 2549NA_erro: 0.1285078636435575 \n",
            "EU_erro: 0.048382074333900627JP_erro: 0.011001593151757883loss: 0.06263050922127657\n",
            "Epoch: 2550NA_erro: 0.08010139761044997 \n",
            "EU_erro: 0.02600142891092818JP_erro: 0.007153232545815697loss: 0.03775202026662901\n",
            "Epoch: 2551NA_erro: 0.22044113440107005 \n",
            "EU_erro: 0.3891865412394206JP_erro: 0.10460946911065154loss: 0.2380790821341581\n",
            "Epoch: 2552NA_erro: 0.07892241773679275 \n",
            "EU_erro: 0.03535323133764341JP_erro: 0.017284906880800113loss: 0.043853525967560994\n",
            "Epoch: 2553NA_erro: 0.6862567931182625 \n",
            "EU_erro: 0.35458154456560004JP_erro: 0.19639434740524883loss: 0.4124109135117642\n",
            "Epoch: 2554NA_erro: 0.17409729403118754 \n",
            "EU_erro: 0.14122211655905081JP_erro: 0.04987931990808295loss: 0.1217329132464505\n",
            "Epoch: 2555NA_erro: 0.04222434158473052 \n",
            "EU_erro: 0.02416534479274306JP_erro: 0.008667690693870071loss: 0.025019125882969347\n",
            "Epoch: 2556NA_erro: 0.2113333968229072 \n",
            "EU_erro: 0.1839010660038438JP_erro: 0.022536878900010455loss: 0.13925712237986484\n",
            "Epoch: 2557NA_erro: 0.1850555848705676 \n",
            "EU_erro: 0.1721616308818492JP_erro: 0.02059226581292559loss: 0.1259364915448566\n",
            "Epoch: 2558NA_erro: 0.09088631748229034 \n",
            "EU_erro: 0.036968519521314046JP_erro: 0.009071883536124415loss: 0.045642238254694975\n",
            "Epoch: 2559NA_erro: 0.06687718306400979 \n",
            "EU_erro: 0.05839192127996637JP_erro: 0.02132451580476391loss: 0.048864539279494176\n",
            "Epoch: 2560NA_erro: 0.061897709388141486 \n",
            "EU_erro: 0.2021708044894906JP_erro: 0.06429596926814826loss: 0.10945482771526012\n",
            "Epoch: 2561NA_erro: 0.11940456175988959 \n",
            "EU_erro: 0.1148550270139709JP_erro: 0.012324437383533449loss: 0.0821946757708409\n",
            "Epoch: 2562NA_erro: 0.053744061972743784 \n",
            "EU_erro: 0.04210439301276392JP_erro: 0.011970869792524234loss: 0.035939773385838945\n",
            "Epoch: 2563NA_erro: 0.07023236086202223 \n",
            "EU_erro: 0.015901364097299503JP_erro: 0.02454592738040658loss: 0.03689322129700535\n",
            "Epoch: 2564NA_erro: 0.8740242507106574 \n",
            "EU_erro: 0.4950984688692315JP_erro: 0.045486148013625036loss: 0.4715363184611003\n",
            "Epoch: 2565NA_erro: 0.6395795363788457 \n",
            "EU_erro: 0.08270725723384886JP_erro: 0.027798485848330712loss: 0.25002843649812445\n",
            "Epoch: 2566NA_erro: 0.06959478984507479 \n",
            "EU_erro: 0.022690938424694447JP_erro: 0.06698564041492551loss: 0.05309045776840328\n",
            "Epoch: 2567NA_erro: 0.08222106815308564 \n",
            "EU_erro: 0.1097294711327368JP_erro: 0.031574252963990204loss: 0.07450826870378598\n",
            "Epoch: 2568NA_erro: 0.6800695907237918 \n",
            "EU_erro: 0.3074379299962243JP_erro: 0.16691063725671104loss: 0.38480607114096943\n",
            "Epoch: 2569NA_erro: 0.08106813874355583 \n",
            "EU_erro: 0.02471764189328334JP_erro: 0.06189056607179864loss: 0.05589212096014688\n",
            "Epoch: 2570NA_erro: 0.16261621963146122 \n",
            "EU_erro: 0.08937495623448098JP_erro: 0.10808027067849803loss: 0.1200238216755002\n",
            "Epoch: 2571NA_erro: 0.2529878024907075 \n",
            "EU_erro: 0.13370116551717123JP_erro: 0.08512325989183529loss: 0.1572707457135814\n",
            "Epoch: 2572NA_erro: 0.2731372958929964 \n",
            "EU_erro: 0.1629784864972728JP_erro: 0.04543562268101892loss: 0.16051713810410612\n",
            "Epoch: 2573NA_erro: 0.12798676195070724 \n",
            "EU_erro: 0.07698651894118434JP_erro: 0.03934113092200701loss: 0.08143814035164293\n",
            "Epoch: 2574NA_erro: 5.733650266662124 \n",
            "EU_erro: 2.7772264702375544JP_erro: 0.04721456719923389loss: 2.8526971506517986\n",
            "Epoch: 2575NA_erro: 0.05797925383545632 \n",
            "EU_erro: 0.03501595683800158JP_erro: 0.034369080103645026loss: 0.04245476205219594\n",
            "Epoch: 2576NA_erro: 0.10126385577889375 \n",
            "EU_erro: 0.06642335145048393JP_erro: 0.09323517481486003loss: 0.08697412734807924\n",
            "Epoch: 2577NA_erro: 0.09019691814747892 \n",
            "EU_erro: 0.07850261621697005JP_erro: 0.009182175231534381loss: 0.05929390127344649\n",
            "Epoch: 2578NA_erro: 0.15605281489764072 \n",
            "EU_erro: 0.19828027533006298JP_erro: 0.03522951242535613loss: 0.12985420781512594\n",
            "Epoch: 2579NA_erro: 5.728902890700702 \n",
            "EU_erro: 2.8925116487251694JP_erro: 0.05781602951907372loss: 2.8930767562038215\n",
            "Epoch: 2580NA_erro: 0.7908945305402889 \n",
            "EU_erro: 0.1018634045770926JP_erro: 0.11773158413495204loss: 0.33682985823283823\n",
            "Epoch: 2581NA_erro: 0.15081826106522433 \n",
            "EU_erro: 0.04249098227005596JP_erro: 0.016394718441852304loss: 0.06990132405776386\n",
            "Epoch: 2582NA_erro: 0.8870116684787958 \n",
            "EU_erro: 0.2874812044838602JP_erro: 0.07607618505640547loss: 0.41685636653456576\n",
            "Epoch: 2583NA_erro: 0.08233037105826445 \n",
            "EU_erro: 0.2524448365204094JP_erro: 0.05714663701464039loss: 0.13064061948495317\n",
            "Epoch: 2584NA_erro: 0.5035146816756374 \n",
            "EU_erro: 0.20764746407205745JP_erro: 0.02730632706205974loss: 0.2461561306502468\n",
            "Epoch: 2585NA_erro: 0.149351672608723 \n",
            "EU_erro: 0.10154364645019058JP_erro: 0.012187083331189414loss: 0.08769414221593576\n",
            "Epoch: 2586NA_erro: 0.5970280299815096 \n",
            "EU_erro: 0.10302657304808151JP_erro: 0.07243075112039729loss: 0.25749515193377354\n",
            "Epoch: 2587NA_erro: 0.6673035141109496 \n",
            "EU_erro: 0.3212443433066671JP_erro: 0.0693936172381852loss: 0.35264713819636856\n",
            "Epoch: 2588NA_erro: 0.09049825890119685 \n",
            "EU_erro: 0.08585577787354935JP_erro: 0.009008614822875622loss: 0.06178755168766938\n",
            "Epoch: 2589NA_erro: 0.11212092037348784 \n",
            "EU_erro: 0.05238422127657159JP_erro: 0.011332527149555295loss: 0.05861256011696749\n",
            "Epoch: 2590NA_erro: 0.8322232268577399 \n",
            "EU_erro: 0.5495694995850555JP_erro: 0.1105878427047138loss: 0.4974601804748062\n",
            "Epoch: 2591NA_erro: 0.0862746183262315 \n",
            "EU_erro: 0.05891883558081102JP_erro: 0.10387634122094443loss: 0.08302327274352081\n",
            "Epoch: 2592NA_erro: 0.046025902725929435 \n",
            "EU_erro: 0.05034213842347611JP_erro: 0.01757471020831618loss: 0.0379809205846269\n",
            "Epoch: 2593NA_erro: 0.25693305703096614 \n",
            "EU_erro: 0.18843456756236943JP_erro: 0.06784453410510868loss: 0.17107074574906697\n",
            "Epoch: 2594NA_erro: 0.08150374242501665 \n",
            "EU_erro: 0.03468523191851239JP_erro: 0.014648042445959047loss: 0.043612339699915216\n",
            "Epoch: 2595NA_erro: 0.1990382246268812 \n",
            "EU_erro: 0.13457159663355628JP_erro: 0.0510741111844085loss: 0.12822797132092853\n",
            "Epoch: 2596NA_erro: 0.7004896060440892 \n",
            "EU_erro: 0.4485457072886386JP_erro: 0.05472644816997439loss: 0.4012538850769516\n",
            "Epoch: 2597NA_erro: 0.044310199197872664 \n",
            "EU_erro: 0.03258712300958559JP_erro: 0.013780910377354585loss: 0.030226081378700198\n",
            "Epoch: 2598NA_erro: 0.0601955116257187 \n",
            "EU_erro: 0.04971529624258825JP_erro: 0.011085672665011976loss: 0.0403321621029876\n",
            "Epoch: 2599NA_erro: 0.6894411042679188 \n",
            "EU_erro: 0.31775873760844386JP_erro: 0.042021029679350154loss: 0.34974027973736904\n",
            "Epoch: 2600NA_erro: 0.08779767871827118 \n",
            "EU_erro: 0.040785693383032035JP_erro: 0.02675832704056141loss: 0.051780569460964945\n",
            "Epoch: 2601NA_erro: 0.06701942562132843 \n",
            "EU_erro: 0.029179964416710905JP_erro: 0.00886673439842786loss: 0.03502204205638679\n",
            "Epoch: 2602NA_erro: 0.07894756257996079 \n",
            "EU_erro: 0.03184812475544538JP_erro: 0.031778329564619436loss: 0.047524673070094385\n",
            "Epoch: 2603NA_erro: 0.8396154595899952 \n",
            "EU_erro: 0.309225459431493JP_erro: 0.03174333840377571loss: 0.3935281250827996\n",
            "Epoch: 2604NA_erro: 0.05460054837456045 \n",
            "EU_erro: 0.0414790155351624JP_erro: 0.011484905961872072loss: 0.03585482290548871\n",
            "Epoch: 2605NA_erro: 0.27023941971534904 \n",
            "EU_erro: 0.04998058773750483JP_erro: 0.010423202847325525loss: 0.11021440343339314\n",
            "Epoch: 2606NA_erro: 0.24936753650044285 \n",
            "EU_erro: 0.16915068145870238JP_erro: 0.04001096237537473loss: 0.15284307243288026\n",
            "Epoch: 2607NA_erro: 0.541192468746688 \n",
            "EU_erro: 0.15572592269542607JP_erro: 0.16287262125532756loss: 0.2865970042324805\n",
            "Epoch: 2608NA_erro: 0.05518401778021524 \n",
            "EU_erro: 0.010699565096418987JP_erro: 0.01241649537123451loss: 0.026100026082622914\n",
            "Epoch: 2609NA_erro: 0.08315435675687569 \n",
            "EU_erro: 0.038690597049949704JP_erro: 0.010778281346772068loss: 0.04420774389607038\n",
            "Epoch: 2610NA_erro: 0.26659904524337413 \n",
            "EU_erro: 0.03472712612891382JP_erro: 0.009724423866863398loss: 0.10368353636689888\n",
            "Epoch: 2611NA_erro: 0.06077803382577822 \n",
            "EU_erro: 0.031067622724429582JP_erro: 0.015764547179835713loss: 0.03587006829505743\n",
            "Epoch: 2612NA_erro: 0.05356083082598309 \n",
            "EU_erro: 0.06509150183478067JP_erro: 0.10161835093830907loss: 0.07342356119969094\n",
            "Epoch: 2613NA_erro: 0.0926865625751111 \n",
            "EU_erro: 0.03579868364703748JP_erro: 0.029407365377559218loss: 0.05263086899306423\n",
            "Epoch: 2614NA_erro: 0.0499370255211527 \n",
            "EU_erro: 0.21335481673248055JP_erro: 0.073342198549315loss: 0.11221135309500288\n",
            "Epoch: 2615NA_erro: 0.12441228526507238 \n",
            "EU_erro: 0.03923766834791317JP_erro: 0.032775598440983496loss: 0.06547518478807553\n",
            "Epoch: 2616NA_erro: 0.08149308751719866 \n",
            "EU_erro: 0.21086373070413753JP_erro: 0.05827403345773386loss: 0.11687696442123531\n",
            "Epoch: 2617NA_erro: 0.646436750426773 \n",
            "EU_erro: 0.6149544457132502JP_erro: 0.140022593875264loss: 0.46713795772818634\n",
            "Epoch: 2618NA_erro: 0.14530013698016025 \n",
            "EU_erro: 0.024829204230345497JP_erro: 0.045965933060461234loss: 0.07203176040057988\n",
            "Epoch: 2619NA_erro: 0.5387781941613485 \n",
            "EU_erro: 0.13122391331103422JP_erro: 0.07186798162238542loss: 0.24729002353756926\n",
            "Epoch: 2620NA_erro: 0.5344238207321759 \n",
            "EU_erro: 0.3977737870327262JP_erro: 0.15315679616706315loss: 0.36178481671237206\n",
            "Epoch: 2621NA_erro: 0.22912147433258767 \n",
            "EU_erro: 0.033406987208728645JP_erro: 0.017326959343843682loss: 0.09328514106513917\n",
            "Epoch: 2622NA_erro: 0.7277876092481983 \n",
            "EU_erro: 0.0721618602442187JP_erro: 0.009774670813434808loss: 0.26990801789039787\n",
            "Epoch: 2623NA_erro: 0.8331238946249319 \n",
            "EU_erro: 0.3143885708594507JP_erro: 0.05175552626912908loss: 0.39975598801014034\n",
            "Epoch: 2624NA_erro: 0.5075293917988621 \n",
            "EU_erro: 0.11642308198204336JP_erro: 0.08285632429196853loss: 0.235602948092675\n",
            "Epoch: 2625NA_erro: 0.09756156640459401 \n",
            "EU_erro: 0.015556427628494972JP_erro: 0.008312985878582148loss: 0.040476992148761604\n",
            "Epoch: 2626NA_erro: 0.16090779341468514 \n",
            "EU_erro: 0.04340664822925893JP_erro: 0.020715998586758162loss: 0.07501014905382497\n",
            "Epoch: 2627NA_erro: 0.06157933741576912 \n",
            "EU_erro: 0.05415903043377307JP_erro: 0.012923043596652127loss: 0.042887135993602665\n",
            "Epoch: 2628NA_erro: 0.33273164616074674 \n",
            "EU_erro: 0.16477996988814006JP_erro: 0.06632549818172011loss: 0.18794571706490923\n",
            "Epoch: 2629NA_erro: 0.09696898534316425 \n",
            "EU_erro: 0.038241975067197814JP_erro: 0.026943616626798644loss: 0.05405152952948282\n",
            "Epoch: 2630NA_erro: 0.0789524048797844 \n",
            "EU_erro: 0.04359993361687475JP_erro: 0.011977875648542892loss: 0.044843406640281976\n",
            "Epoch: 2631NA_erro: 0.05409988321999247 \n",
            "EU_erro: 0.027441042800282322JP_erro: 0.01510913981947788loss: 0.03221668938333674\n",
            "Epoch: 2632NA_erro: 0.7428468659866688 \n",
            "EU_erro: 0.3193905187207599JP_erro: 0.0547908858735432loss: 0.37234276764152585\n",
            "Epoch: 2633NA_erro: 0.055298962334329765 \n",
            "EU_erro: 0.03096118453861207JP_erro: 0.041819884795551154loss: 0.04269334696984106\n",
            "Epoch: 2634NA_erro: 0.06726696971775026 \n",
            "EU_erro: 0.03265389407327933JP_erro: 0.009745280872019686loss: 0.03655538309452146\n",
            "Epoch: 2635NA_erro: 0.6579393194627392 \n",
            "EU_erro: 0.5690300741861033JP_erro: 0.08849375931791557loss: 0.43848773305730304\n",
            "Epoch: 2636NA_erro: 0.39028918096261433 \n",
            "EU_erro: 0.1253974714944529JP_erro: 0.07443304671797642loss: 0.1967065833335699\n",
            "Epoch: 2637NA_erro: 5.979788728462633 \n",
            "EU_erro: 2.892815345941588JP_erro: 0.12194249057030493loss: 2.998182311538578\n",
            "Epoch: 2638NA_erro: 0.07164989793023398 \n",
            "EU_erro: 0.016852911821631497JP_erro: 0.03626473883325739loss: 0.0415891870971798\n",
            "Epoch: 2639NA_erro: 0.06134506343871124 \n",
            "EU_erro: 0.03864961770153785JP_erro: 0.00971607806146607loss: 0.03657025422236716\n",
            "Epoch: 2640NA_erro: 0.04804403744926748 \n",
            "EU_erro: 0.03511668868767199JP_erro: 0.012737303510192753loss: 0.03196601257767788\n",
            "Epoch: 2641NA_erro: 0.5745908086614091 \n",
            "EU_erro: 0.23634298827297004JP_erro: 0.04671504331189533loss: 0.28588294982910156\n",
            "Epoch: 2642NA_erro: 0.5252714674602184 \n",
            "EU_erro: 0.10776555815408397JP_erro: 0.08309546367142552loss: 0.23871083592259607\n",
            "Epoch: 2643NA_erro: 6.430719065111737 \n",
            "EU_erro: 3.2381501308707303JP_erro: 0.08417925169301588loss: 3.2510163063226742\n",
            "Epoch: 2644NA_erro: 0.25172904480335323 \n",
            "EU_erro: 0.03579286872878555JP_erro: 0.015119432016860607loss: 0.10088045467701993\n",
            "Epoch: 2645NA_erro: 0.11265371196953826 \n",
            "EU_erro: 0.21707806476326877JP_erro: 0.05975872509239256loss: 0.12983017189558163\n",
            "Epoch: 2646NA_erro: 0.2464600496513899 \n",
            "EU_erro: 0.2340004610460858JP_erro: 0.04244885703389959loss: 0.1743031287378119\n",
            "Epoch: 2647NA_erro: 0.7415380403977032 \n",
            "EU_erro: 0.04618378110634264JP_erro: 0.06503104701522709loss: 0.2842509284500004\n",
            "Epoch: 2648NA_erro: 0.09769739106644032 \n",
            "EU_erro: 0.03102592488591985JP_erro: 0.012452922357145206loss: 0.047058749568554784\n",
            "Epoch: 2649NA_erro: 0.04989822243535241 \n",
            "EU_erro: 0.08652215780213822JP_erro: 0.008512744607851487loss: 0.04831104315528574\n",
            "Epoch: 2650NA_erro: 0.0574342317359392 \n",
            "EU_erro: 0.0240595211354337JP_erro: 0.021966843642005626loss: 0.03448686627454536\n",
            "Epoch: 2651NA_erro: 0.6742492202640504 \n",
            "EU_erro: 0.34173329671223956JP_erro: 0.06604003813839698loss: 0.36067419273908746\n",
            "Epoch: 2652NA_erro: 0.6489893447521121 \n",
            "EU_erro: 0.31154037446014643JP_erro: 0.04220968531083691loss: 0.33424647279488023\n",
            "Epoch: 2653NA_erro: 0.07653740487357442 \n",
            "EU_erro: 0.03974184047344119JP_erro: 0.016240401323451552loss: 0.04417321478673654\n",
            "Epoch: 2654NA_erro: 0.12225650077642397 \n",
            "EU_erro: 0.03488469724507295JP_erro: 0.016861913740172867loss: 0.058001039564147476\n",
            "Epoch: 2655NA_erro: 0.0717799469482067 \n",
            "EU_erro: 0.028155439121778623JP_erro: 0.013483266035715738loss: 0.03780621428822362\n",
            "Epoch: 2656NA_erro: 0.7921605516773785 \n",
            "EU_erro: 0.3556920206824014JP_erro: 0.11676415916561156loss: 0.42153890742812045\n",
            "Epoch: 2657NA_erro: 0.19089306971823522 \n",
            "EU_erro: 0.23908794388290525JP_erro: 0.020637380522350934loss: 0.15020614446595656\n",
            "Epoch: 2658NA_erro: 0.8242011994354485 \n",
            "EU_erro: 0.31011784723562785JP_erro: 0.035740294197733086loss: 0.3900198234144107\n",
            "Epoch: 2659NA_erro: 0.06315037723659545 \n",
            "EU_erro: 0.040710392848465796JP_erro: 0.011252829039743704loss: 0.03837120163348294\n",
            "Epoch: 2660NA_erro: 0.05206618198128634 \n",
            "EU_erro: 0.03181241972501888JP_erro: 0.04532721153525419loss: 0.043068606724110683\n",
            "Epoch: 2661NA_erro: 0.5973937160284944 \n",
            "EU_erro: 0.11090430178383524JP_erro: 0.07503899955010229loss: 0.2611123683840729\n",
            "Epoch: 2662NA_erro: 0.15234099802120712 \n",
            "EU_erro: 0.21468826042589292JP_erro: 0.056701071502626404loss: 0.14124344485674717\n",
            "Epoch: 2663NA_erro: 0.08012848306995954 \n",
            "EU_erro: 0.025737759216811307JP_erro: 0.007534457962642345loss: 0.03780023534168569\n",
            "Epoch: 2664NA_erro: 0.31242928763692696 \n",
            "EU_erro: 0.1380916621333869JP_erro: 0.05650229694307313loss: 0.1690077781677246\n",
            "Epoch: 2665NA_erro: 0.9223909895549449 \n",
            "EU_erro: 0.5357899037442466JP_erro: 0.250356862711352loss: 0.5695126038189082\n",
            "Epoch: 2666NA_erro: 0.0952666951704395 \n",
            "EU_erro: 0.0386695944985678JP_erro: 0.010992007893185282loss: 0.048309435216031336\n",
            "Epoch: 2667NA_erro: 0.6842832787092342 \n",
            "EU_erro: 0.3106893495071766JP_erro: 0.12362235276273979loss: 0.37286499674006024\n",
            "Epoch: 2668NA_erro: 0.09645068368246389 \n",
            "EU_erro: 0.03681003585342289JP_erro: 0.010073467973590822loss: 0.04777806211811628\n",
            "Epoch: 2669NA_erro: 0.05935308083083279 \n",
            "EU_erro: 0.03428155368612718JP_erro: 0.025235654771790024loss: 0.03962343053300251\n",
            "Epoch: 2670NA_erro: 0.09049995000972304 \n",
            "EU_erro: 0.03287755010664001JP_erro: 0.018912663524465044loss: 0.04743005416189977\n",
            "Epoch: 2671NA_erro: 0.13232577678769133 \n",
            "EU_erro: 0.033552006695621694JP_erro: 0.012180226486782695loss: 0.059352669604988986\n",
            "Epoch: 2672NA_erro: 0.3491457488185676 \n",
            "EU_erro: 0.2349270036978315JP_erro: 0.022166699402092038loss: 0.20207983578822408\n",
            "Epoch: 2673NA_erro: 0.12516512427219126 \n",
            "EU_erro: 0.2536311814951342JP_erro: 0.04063552664231884loss: 0.13981061388355817\n",
            "Epoch: 2674NA_erro: 0.02583705870680107 \n",
            "EU_erro: 0.017686955226484197JP_erro: 0.06242342697557553loss: 0.03531581902688788\n",
            "Epoch: 2675NA_erro: 0.05601760490920193 \n",
            "EU_erro: 0.1012249325597009JP_erro: 0.08959836738054143loss: 0.08228030315665312\n",
            "Epoch: 2676NA_erro: 0.6372276572294013 \n",
            "EU_erro: 0.21711281103681224JP_erro: 0.027456669382346692loss: 0.293932367664899\n",
            "Epoch: 2677NA_erro: 0.07314198700956596 \n",
            "EU_erro: 0.025218196155488953JP_erro: 0.009665906775829404loss: 0.03600869760956875\n",
            "Epoch: 2678NA_erro: 0.15098105105318765 \n",
            "EU_erro: 0.22176674170087474JP_erro: 0.04233764123546985loss: 0.13836181441018747\n",
            "Epoch: 2679NA_erro: 0.2684901111809782 \n",
            "EU_erro: 0.13712781344273295JP_erro: 0.07383690323940544loss: 0.1598182759543722\n",
            "Epoch: 2680NA_erro: 0.08350633835607721 \n",
            "EU_erro: 0.030941734480303386JP_erro: 0.018779451763907145loss: 0.0444091752518055\n",
            "Epoch: 2681NA_erro: 0.745739086653835 \n",
            "EU_erro: 0.31680295633715255JP_erro: 0.03689469524132189loss: 0.3664789273757343\n",
            "Epoch: 2682NA_erro: 0.10686976965083632 \n",
            "EU_erro: 0.06223880505377008JP_erro: 0.012594210778095926loss: 0.0605675970861154\n",
            "Epoch: 2683NA_erro: 0.06883192432019138 \n",
            "EU_erro: 0.01662591862124066JP_erro: 0.009803014208180036loss: 0.03175362136012824\n",
            "Epoch: 2684NA_erro: 0.5355293436567913 \n",
            "EU_erro: 0.20511176234991976JP_erro: 0.029926297738570577loss: 0.25685582050057343\n",
            "Epoch: 2685NA_erro: 0.1351814584214558 \n",
            "EU_erro: 0.0377208202384239JP_erro: 0.021752837554428927loss: 0.06488504335861797\n",
            "Epoch: 2686NA_erro: 0.06097877210424852 \n",
            "EU_erro: 0.05061558512754218JP_erro: 0.010823059913723967loss: 0.04080580648525741\n",
            "Epoch: 2687NA_erro: 0.07118080937585165 \n",
            "EU_erro: 0.1207582340684048JP_erro: 0.09068066759626994loss: 0.09420657342718552\n",
            "Epoch: 2688NA_erro: 5.7082230915394865 \n",
            "EU_erro: 2.7768526890481167JP_erro: 0.0599364485851554loss: 2.848337454389232\n",
            "Epoch: 2689NA_erro: 0.10254278663516969 \n",
            "EU_erro: 0.04022547440935475JP_erro: 0.022447818471479787loss: 0.05507203035576399\n",
            "Epoch: 2690NA_erro: 1.2379997460417045 \n",
            "EU_erro: 0.5258185734120451JP_erro: 0.1067600971044496loss: 0.6235260926475821\n",
            "Epoch: 2691NA_erro: 0.07648454156032829 \n",
            "EU_erro: 0.07999924726264421JP_erro: 0.04477782304896865loss: 0.06708721319834392\n",
            "Epoch: 2692NA_erro: 0.5835540534914002 \n",
            "EU_erro: 0.11644407760265262JP_erro: 0.07606572421022163loss: 0.2586879841116972\n",
            "Epoch: 2693NA_erro: 0.056472790333651754 \n",
            "EU_erro: 0.029910523300023042JP_erro: 0.012204308611477992loss: 0.03286254267359889\n",
            "Epoch: 2694NA_erro: 0.804054718609004 \n",
            "EU_erro: 0.5751620152199916JP_erro: 0.0814113506050997loss: 0.48687602198401164\n",
            "Epoch: 2695NA_erro: 0.11770049730936687 \n",
            "EU_erro: 0.0338855131652004JP_erro: 0.011056123315825943loss: 0.05421404690705529\n",
            "Epoch: 2696NA_erro: 0.11074025501576505 \n",
            "EU_erro: 0.03846642351889795JP_erro: 0.03682659577953723loss: 0.062011092208152596\n",
            "Epoch: 2697NA_erro: 0.6707742232684941 \n",
            "EU_erro: 0.23635917855787647JP_erro: 0.010859088380207388loss: 0.3059975306193034\n",
            "Epoch: 2698NA_erro: 0.6881485428921011 \n",
            "EU_erro: 0.3141322616458863JP_erro: 0.045128372288489524loss: 0.34913639689600745\n",
            "Epoch: 2699NA_erro: 0.10726465735324593 \n",
            "EU_erro: 0.07735806380131448JP_erro: 0.07294042165889296loss: 0.0858543917190197\n",
            "Epoch: 2700NA_erro: 0.19041192623995995 \n",
            "EU_erro: 0.044456225957057276JP_erro: 0.018132640871890756loss: 0.08433359537937844\n",
            "Epoch: 2701NA_erro: 0.058257260987924976 \n",
            "EU_erro: 0.021300600018612174JP_erro: 0.02601122440293778loss: 0.03518969744674919\n",
            "Epoch: 2702NA_erro: 0.07872010386267374 \n",
            "EU_erro: 0.04511725994967675JP_erro: 0.021707316239674885loss: 0.04851489566093267\n",
            "Epoch: 2703NA_erro: 0.10927473851876665 \n",
            "EU_erro: 0.08194449336029762JP_erro: 0.049680420594622loss: 0.08029988569806712\n",
            "Epoch: 2704NA_erro: 0.05557272323342257 \n",
            "EU_erro: 0.02253887034201807JP_erro: 0.011923882619355076loss: 0.030011825783308164\n",
            "Epoch: 2705NA_erro: 0.5174501921779425 \n",
            "EU_erro: 0.20491775616194852JP_erro: 0.0363277511079182loss: 0.2528985711031182\n",
            "Epoch: 2706NA_erro: 0.10594109232111494 \n",
            "EU_erro: 0.03369495619175046JP_erro: 0.02486166103865749loss: 0.054832567540250086\n",
            "Epoch: 2707NA_erro: 5.849194785421209 \n",
            "EU_erro: 2.807236900625303JP_erro: 0.04489765148754268loss: 2.9004432619080065\n",
            "Epoch: 2708NA_erro: 0.44770436693531596 \n",
            "EU_erro: 0.22822592609612516JP_erro: 0.13663442559944566loss: 0.2708548908085786\n",
            "Epoch: 2709NA_erro: 0.15773069026858308 \n",
            "EU_erro: 0.08512350015862044JP_erro: 0.03872249015541964loss: 0.09385888890702596\n",
            "Epoch: 2710NA_erro: 0.06123934143273405 \n",
            "EU_erro: 0.03211399150449176JP_erro: 0.01019921760226405loss: 0.03451751877171125\n",
            "Epoch: 2711NA_erro: 0.2660945958869402 \n",
            "EU_erro: 0.18980846848598745JP_erro: 0.022644347460695016loss: 0.15951581703600035\n",
            "Epoch: 2712NA_erro: 0.29968699743581373 \n",
            "EU_erro: 0.057421335878298264JP_erro: 0.007263752956723058loss: 0.12145737344904463\n",
            "Epoch: 2713NA_erro: 0.07159063058306081 \n",
            "EU_erro: 0.031121301558590674JP_erro: 0.09789228439331055loss: 0.06686807140823482\n",
            "Epoch: 2714NA_erro: 0.5931044733801554 \n",
            "EU_erro: 0.20489628000776897JP_erro: 0.027797039627104767loss: 0.27526594871698423\n",
            "Epoch: 2715NA_erro: 0.6446167110472687 \n",
            "EU_erro: 0.4143992874973504JP_erro: 0.12422505275223607loss: 0.39441371148870896\n",
            "Epoch: 2716NA_erro: 0.08489715036495711 \n",
            "EU_erro: 0.04474773425464482JP_erro: 0.20148098006729007loss: 0.1103752897691357\n",
            "Epoch: 2717NA_erro: 0.10654954947242441 \n",
            "EU_erro: 0.039278384326964386JP_erro: 0.060070120086965634loss: 0.06863268770912821\n",
            "Epoch: 2718NA_erro: 0.08499871852786042 \n",
            "EU_erro: 0.034279218015744704JP_erro: 0.010083091351412988loss: 0.043120342631672706\n",
            "Epoch: 2719NA_erro: 0.6534808550694192 \n",
            "EU_erro: 0.34967123075973155JP_erro: 0.05877554878708004loss: 0.3539758874464405\n",
            "Epoch: 2720NA_erro: 0.05318368128103803 \n",
            "EU_erro: 0.023534087709678235JP_erro: 0.012436378602833711loss: 0.029718051123064616\n",
            "Epoch: 2721NA_erro: 0.25239741155343465 \n",
            "EU_erro: 0.0809751832207968JP_erro: 0.018110534017400225loss: 0.11716105217157408\n",
            "Epoch: 2722NA_erro: 0.11450300845064858 \n",
            "EU_erro: 0.21237061005230098JP_erro: 0.01457885600799738loss: 0.1138174995895504\n",
            "Epoch: 2723NA_erro: 0.6596622171328049 \n",
            "EU_erro: 0.31841529432193255JP_erro: 0.03821853288384371loss: 0.33876533656157265\n",
            "Epoch: 2724NA_erro: 0.19385156705397968 \n",
            "EU_erro: 0.19598295522290607JP_erro: 0.022223236024841782loss: 0.13735259226126265\n",
            "Epoch: 2725NA_erro: 0.11337253474449926 \n",
            "EU_erro: 0.05635212096132974JP_erro: 0.022679488788279453loss: 0.06413472253222798\n",
            "Epoch: 2726NA_erro: 0.5561981275100116 \n",
            "EU_erro: 0.21125791608825212JP_erro: 0.050971665123636406loss: 0.27280925780303716\n",
            "Epoch: 2727NA_erro: 5.740518496018048 \n",
            "EU_erro: 2.779998483583909JP_erro: 0.09963845097741415loss: 2.8733855254890384\n",
            "Epoch: 2728NA_erro: 0.0992686157078706 \n",
            "EU_erro: 0.034762117289757546JP_erro: 0.0066990647898163905loss: 0.04690993201825046\n",
            "Epoch: 2729NA_erro: 0.6888328226961831 \n",
            "EU_erro: 0.4615238840265792JP_erro: 0.04530645156091498loss: 0.39855435837146846\n",
            "Epoch: 2730NA_erro: 0.07542236368785533 \n",
            "EU_erro: 0.08768588997596918JP_erro: 0.06375210229740587loss: 0.07562011711357176\n",
            "Epoch: 2731NA_erro: 0.08806836697482323 \n",
            "EU_erro: 0.028659853824349338JP_erro: 0.015355564596117005loss: 0.04402792730996775\n",
            "Epoch: 2732NA_erro: 0.2451919770055963 \n",
            "EU_erro: 0.12770283129788185JP_erro: 0.06176229133162388loss: 0.14488569525785225\n",
            "Epoch: 2733NA_erro: 0.18151314683662828 \n",
            "EU_erro: 0.1570797521014546JP_erro: 0.044169920359471045loss: 0.1275876141333765\n",
            "Epoch: 2734NA_erro: 0.05529729432837908 \n",
            "EU_erro: 0.03605811170829359JP_erro: 0.008821574638980303loss: 0.033392328624577486\n",
            "Epoch: 2735NA_erro: 0.4524071641670641 \n",
            "EU_erro: 0.34796940263851667JP_erro: 0.15166167140931122loss: 0.3173461071280546\n",
            "Epoch: 2736NA_erro: 0.5945099231808685 \n",
            "EU_erro: 0.2845801493918249JP_erro: 0.11443182479503543loss: 0.3311739781106165\n",
            "Epoch: 2737NA_erro: 0.7397738168405932 \n",
            "EU_erro: 0.6332922351452731JP_erro: 0.046456557835719385loss: 0.473174161689226\n",
            "Epoch: 2738NA_erro: 0.6818207289821417 \n",
            "EU_erro: 0.16774622968925063JP_erro: 0.12021901071533676loss: 0.3232620298400406\n",
            "Epoch: 2739NA_erro: 0.14123953590097355 \n",
            "EU_erro: 0.04406277523484341JP_erro: 0.014353009850479836loss: 0.06655177404714185\n",
            "Epoch: 2740NA_erro: 0.24390701175660126 \n",
            "EU_erro: 0.13044230697690978JP_erro: 0.05552644415419231loss: 0.14329192250273948\n",
            "Epoch: 2741NA_erro: 0.1022689471873202 \n",
            "EU_erro: 0.039964960527050404JP_erro: 0.008279833682747774loss: 0.050171248672544494\n",
            "Epoch: 2742NA_erro: 5.870804897574491 \n",
            "EU_erro: 2.792777423710786JP_erro: 0.04260403703349505loss: 2.902061994685683\n",
            "Epoch: 2743NA_erro: 0.7769805701204049 \n",
            "EU_erro: 0.459817324497903JP_erro: 0.054461618726567705loss: 0.4304199070893517\n",
            "Epoch: 2744NA_erro: 0.6846543984819752 \n",
            "EU_erro: 0.47181247740752935JP_erro: 0.13044659481492155loss: 0.42897117230319237\n",
            "Epoch: 2745NA_erro: 0.0819915302040041 \n",
            "EU_erro: 0.02172528773315193JP_erro: 0.024386651294175967loss: 0.042701155640358146\n",
            "Epoch: 2746NA_erro: 6.240458525428476 \n",
            "EU_erro: 2.792187772055929JP_erro: 0.054111240446105484loss: 3.0289196043975593\n",
            "Epoch: 2747NA_erro: 5.768986191860465 \n",
            "EU_erro: 2.7659252078034156JP_erro: 0.12687823569127757loss: 2.8872633349987886\n",
            "Epoch: 2748NA_erro: 0.17123763875443807 \n",
            "EU_erro: 0.06276130676269531JP_erro: 0.016160944635553877loss: 0.08338663929192595\n",
            "Epoch: 2749NA_erro: 6.202247678771499 \n",
            "EU_erro: 2.8501220821410187JP_erro: 0.13579393542090126loss: 3.062721370726593\n",
            "Epoch: 2750NA_erro: 0.6734623280606529 \n",
            "EU_erro: 0.3083247362181198JP_erro: 0.08004042529320532loss: 0.35394254580948703\n",
            "Epoch: 2751NA_erro: 0.26390532190485516 \n",
            "EU_erro: 0.05839884743209957JP_erro: 0.013626199360041655loss: 0.11197679726652397\n",
            "Epoch: 2752NA_erro: 0.20079865936161012 \n",
            "EU_erro: 0.17278920772463777JP_erro: 0.04341587077739627loss: 0.13900124749471976\n",
            "Epoch: 2753NA_erro: 0.5730254520741545 \n",
            "EU_erro: 0.15643210374107658JP_erro: 0.068348766297333loss: 0.26593542838281437\n",
            "Epoch: 2754NA_erro: 0.14488281205643055 \n",
            "EU_erro: 0.036160081856010495JP_erro: 0.01674904264220896loss: 0.06593064744343129\n",
            "Epoch: 2755NA_erro: 0.24508328400841056 \n",
            "EU_erro: 0.3607881161593652JP_erro: 0.07117791231288466loss: 0.22568310878073522\n",
            "Epoch: 2756NA_erro: 0.09367040885511295 \n",
            "EU_erro: 0.07589834143025007JP_erro: 0.015601842671401741loss: 0.061723534450974574\n",
            "Epoch: 2757NA_erro: 0.1440458242283311 \n",
            "EU_erro: 0.10028986967811289JP_erro: 0.005687512168588565loss: 0.08334107177202092\n",
            "Epoch: 2758NA_erro: 0.07364451885223389 \n",
            "EU_erro: 0.0331421646960946JP_erro: 0.009672131764796353loss: 0.038819608762282735\n",
            "Epoch: 2759NA_erro: 0.7271980315215828 \n",
            "EU_erro: 0.3134557073430497JP_erro: 0.1483888127083002loss: 0.39634752643200777\n",
            "Epoch: 2760NA_erro: 0.19084588501804559 \n",
            "EU_erro: 0.06232664566631465JP_erro: 0.018150601969208827loss: 0.09044105692427287\n",
            "Epoch: 2761NA_erro: 0.09189317392748456 \n",
            "EU_erro: 0.07593851218851962JP_erro: 0.012062722167303397loss: 0.05996480006580205\n",
            "Epoch: 2762NA_erro: 0.6224295889684396 \n",
            "EU_erro: 0.44167818025101063JP_erro: 0.04746833050897879loss: 0.3705254266428393\n",
            "Epoch: 2763NA_erro: 0.07938783298167147 \n",
            "EU_erro: 0.0339046929233758JP_erro: 0.02293568248896636loss: 0.045409410498863045\n",
            "Epoch: 2764NA_erro: 0.7657315010248229 \n",
            "EU_erro: 0.503804184669672JP_erro: 0.06543434405511664loss: 0.4449899806532749\n",
            "Epoch: 2765NA_erro: 0.07173565469046896 \n",
            "EU_erro: 0.040415169656738754JP_erro: 0.007965833872787712loss: 0.040038888768632284\n",
            "Epoch: 2766NA_erro: 0.35956050074377727 \n",
            "EU_erro: 0.24984452151512915JP_erro: 0.13588330542394358loss: 0.24842944256094998\n",
            "Epoch: 2767NA_erro: 0.5828075630720272 \n",
            "EU_erro: 0.05150610162306202JP_erro: 0.016059019537859185loss: 0.21679089050884395\n",
            "Epoch: 2768NA_erro: 0.3478752180587414 \n",
            "EU_erro: 0.21516042162281598JP_erro: 0.13767241507537606loss: 0.23356934850530106\n",
            "Epoch: 2769NA_erro: 0.04672904347264489 \n",
            "EU_erro: 0.04883835482042889JP_erro: 0.01000064634537512loss: 0.03518935090811678\n",
            "Epoch: 2770NA_erro: 0.07783907328465188 \n",
            "EU_erro: 0.032968869967054025JP_erro: 0.014445650023083354loss: 0.04175119621809139\n",
            "Epoch: 2771NA_erro: 0.09783116421958273 \n",
            "EU_erro: 0.07566071295923041JP_erro: 0.01078341242878936loss: 0.06142510000125382\n",
            "Epoch: 2772NA_erro: 0.16437214474345363 \n",
            "EU_erro: 0.0705759747083797JP_erro: 0.033602608266726945loss: 0.08951691694037858\n",
            "Epoch: 2773NA_erro: 0.6624573700187742 \n",
            "EU_erro: 0.45129587483960526JP_erro: 0.06508489449818929loss: 0.3929460880368255\n",
            "Epoch: 2774NA_erro: 0.10884301606998888 \n",
            "EU_erro: 0.03662973642349243JP_erro: 0.015650719635246335loss: 0.05370782327282336\n",
            "Epoch: 2775NA_erro: 0.07060243177783582 \n",
            "EU_erro: 0.03925169623175333JP_erro: 0.01421653947164846loss: 0.04135689070058424\n",
            "Epoch: 2776NA_erro: 0.10224937468536141 \n",
            "EU_erro: 0.02330205006192821JP_erro: 0.077274760534597loss: 0.06760873073755308\n",
            "Epoch: 2777NA_erro: 6.055023429929748 \n",
            "EU_erro: 3.1373114179271138JP_erro: 0.2632052399391352loss: 3.1518468930739765\n",
            "Epoch: 2778NA_erro: 0.5241682732752128 \n",
            "EU_erro: 0.2082307024519573JP_erro: 0.03129901118980822loss: 0.2545659856278767\n",
            "Epoch: 2779NA_erro: 0.6992942603059518 \n",
            "EU_erro: 0.33865211545959JP_erro: 0.03579249446706254loss: 0.35791297291600427\n",
            "Epoch: 2780NA_erro: 0.02340049475662468 \n",
            "EU_erro: 0.026439285093499708JP_erro: 0.009404773166937422loss: 0.01974818549414938\n",
            "Epoch: 2781NA_erro: 0.14216683631719546 \n",
            "EU_erro: 0.025239981884180113JP_erro: 0.009260615983674692loss: 0.0588891441507857\n",
            "Epoch: 2782NA_erro: 0.08492767348770024 \n",
            "EU_erro: 0.039952392725981486JP_erro: 0.09615908297457436loss: 0.07367972255677216\n",
            "Epoch: 2783NA_erro: 0.07826073225154433 \n",
            "EU_erro: 0.03336469101351361JP_erro: 0.017953017192293506loss: 0.04319281541099844\n",
            "Epoch: 2784NA_erro: 0.2994162167689597 \n",
            "EU_erro: 0.20669855812723323JP_erro: 0.045563860457072884loss: 0.183892896933149\n",
            "Epoch: 2785NA_erro: 0.3208582900291265 \n",
            "EU_erro: 0.206843050875405JP_erro: 0.15672894411308821loss: 0.2281434221785198\n",
            "Epoch: 2786NA_erro: 0.13723222784293715 \n",
            "EU_erro: 0.1993732304536095JP_erro: 0.012561936480130336loss: 0.11638913043709688\n",
            "Epoch: 2787NA_erro: 0.353663725446361 \n",
            "EU_erro: 0.2547586241433787JP_erro: 0.17427427824153457loss: 0.2608988451403241\n",
            "Epoch: 2788NA_erro: 0.6887587465981181 \n",
            "EU_erro: 0.44812272685442783JP_erro: 0.047389444454695825loss: 0.39475699727849445\n",
            "Epoch: 2789NA_erro: 0.105808649876321 \n",
            "EU_erro: 0.03288505613341812JP_erro: 0.011335053416185601loss: 0.05000959071078042\n",
            "Epoch: 2790NA_erro: 0.5734447730604069 \n",
            "EU_erro: 0.12821840685467387JP_erro: 0.07050146890241046loss: 0.25738823321438575\n",
            "Epoch: 2791NA_erro: 0.5824652383493822 \n",
            "EU_erro: 0.06356586781583091JP_erro: 0.027693741081296935loss: 0.22457496140354363\n",
            "Epoch: 2792NA_erro: 0.24866699248321297 \n",
            "EU_erro: 0.2988238112871037JP_erro: 0.06252523078474888loss: 0.20333866740381995\n",
            "Epoch: 2793NA_erro: 0.12878731239673702 \n",
            "EU_erro: 0.20812485569207242JP_erro: 0.008969462310620981loss: 0.11529386505600094\n",
            "Epoch: 2794NA_erro: 0.20377678464549456 \n",
            "EU_erro: 0.1549918337385784JP_erro: 0.05617506282274113loss: 0.13831456502278647\n",
            "Epoch: 2795NA_erro: 0.0471064055612845 \n",
            "EU_erro: 0.015753876100214878JP_erro: 0.009723402733026548loss: 0.0241945603097132\n",
            "Epoch: 2796NA_erro: 0.16044400459112124 \n",
            "EU_erro: 0.045157601667004964JP_erro: 0.013213933900345204loss: 0.07293851338615713\n",
            "Epoch: 2797NA_erro: 0.08589658626290254 \n",
            "EU_erro: 0.05665488483369813JP_erro: 0.03245015245999477loss: 0.05833387836929439\n",
            "Epoch: 2798NA_erro: 0.17076009927793992 \n",
            "EU_erro: 0.13920399569725805JP_erro: 0.03887041132579478loss: 0.11627818262854288\n",
            "Epoch: 2799NA_erro: 0.21869450576545657 \n",
            "EU_erro: 0.1490286339161008JP_erro: 0.04265830498333125loss: 0.1367938148882962\n",
            "Epoch: 2800NA_erro: 0.10149821754573851 \n",
            "EU_erro: 0.03365057614422584JP_erro: 0.016047112470449404loss: 0.05039863346158996\n",
            "Epoch: 2801NA_erro: 0.6126836288807004 \n",
            "EU_erro: 0.054104064786156945JP_erro: 0.0075889193965483085loss: 0.2247922180234924\n",
            "Epoch: 2802NA_erro: 0.6157149085702822 \n",
            "EU_erro: 0.2552077197289282JP_erro: 0.06390238455099653loss: 0.3116083514782809\n",
            "Epoch: 2803NA_erro: 0.7040553129920664 \n",
            "EU_erro: 0.12225926384445308JP_erro: 0.08339201757150103loss: 0.30323555303174393\n",
            "Epoch: 2804NA_erro: 0.11729977851690249 \n",
            "EU_erro: 0.06221947281859642JP_erro: 0.005540092324101647loss: 0.061686450196791066\n",
            "Epoch: 2805NA_erro: 0.14043213785156722 \n",
            "EU_erro: 0.2080645856931228JP_erro: 0.0540912105131519loss: 0.13419598572013913\n",
            "Epoch: 2806NA_erro: 0.06478355374447135 \n",
            "EU_erro: 0.041286432465841606JP_erro: 0.006644016550492871loss: 0.03757133733394534\n",
            "Epoch: 2807NA_erro: 0.7148965199788412 \n",
            "EU_erro: 0.3749875135199968JP_erro: 0.034364154634549636loss: 0.3747494645821032\n",
            "Epoch: 2808NA_erro: 0.10013508242230083 \n",
            "EU_erro: 0.11856339698614077JP_erro: 0.019100161716919535loss: 0.07926621178323909\n",
            "Epoch: 2809NA_erro: 0.11172592177871586 \n",
            "EU_erro: 0.22042522134706954JP_erro: 0.05900389002275097loss: 0.13038500334865363\n",
            "Epoch: 2810NA_erro: 0.1306837843370068 \n",
            "EU_erro: 0.033901576385941615JP_erro: 0.00695253123146619loss: 0.05717929943587429\n",
            "Epoch: 2811NA_erro: 0.24299671483594318 \n",
            "EU_erro: 0.1666027446125829JP_erro: 0.05858073862948159loss: 0.15606005986531576\n",
            "Epoch: 2812NA_erro: 0.6951844414999319 \n",
            "EU_erro: 0.43888823930607285JP_erro: 0.04628069641054139loss: 0.3934511657833129\n",
            "Epoch: 2813NA_erro: 0.12064691661864288 \n",
            "EU_erro: 0.059309832809507385JP_erro: 0.01602276697639347loss: 0.06532650585322416\n",
            "Epoch: 2814NA_erro: 0.2624548498050187 \n",
            "EU_erro: 0.2111742847649626JP_erro: 0.043345102968142014loss: 0.1723247535468996\n",
            "Epoch: 2815NA_erro: 0.13141231943470563 \n",
            "EU_erro: 0.04459514636401982JP_erro: 0.010468743799268737loss: 0.062158740768136905\n",
            "Epoch: 2816NA_erro: 0.2044682170069495 \n",
            "EU_erro: 0.14535817064980203JP_erro: 0.06184925404629966loss: 0.13722521390101705\n",
            "Epoch: 2817NA_erro: 0.1390271131382432 \n",
            "EU_erro: 0.028483486452768016JP_erro: 0.023006808388140773loss: 0.06350580574006073\n",
            "Epoch: 2818NA_erro: 0.1841450476831244 \n",
            "EU_erro: 0.018763203722561978JP_erro: 0.004814236836377965loss: 0.06924083066541095\n",
            "Epoch: 2819NA_erro: 0.23845424947812577 \n",
            "EU_erro: 0.06688302801560986JP_erro: 0.018452304278233255loss: 0.1079298544299695\n",
            "Epoch: 2820NA_erro: 0.09024294771889384 \n",
            "EU_erro: 0.04778840745142264JP_erro: 0.04758889360945354loss: 0.06187341471975164\n",
            "Epoch: 2821NA_erro: 0.06698953088863875 \n",
            "EU_erro: 0.03274734399115392JP_erro: 0.0995978643727857loss: 0.06644491077393525\n",
            "Epoch: 2822NA_erro: 0.120584438013476 \n",
            "EU_erro: 0.02779828485592391JP_erro: 0.02799040125321972loss: 0.05879104137420654\n",
            "Epoch: 2823NA_erro: 0.09119542994240458 \n",
            "EU_erro: 0.023962072161741035JP_erro: 0.0257828762364942loss: 0.046980126883632456\n",
            "Epoch: 2824NA_erro: 0.4671596926312114 \n",
            "EU_erro: 0.2292910102725953JP_erro: 0.1448111755903377loss: 0.2804206323254016\n",
            "Epoch: 2825NA_erro: 0.10553749032722887 \n",
            "EU_erro: 0.21360330803449765JP_erro: 0.08444930231848428loss: 0.13453003047972686\n",
            "Epoch: 2826NA_erro: 0.5038227406583091 \n",
            "EU_erro: 0.21156124366346254JP_erro: 0.03093561229779739loss: 0.24877319040224533\n",
            "Epoch: 2827NA_erro: 0.05525183308032132 \n",
            "EU_erro: 0.017425583083500232JP_erro: 0.07076049035833787loss: 0.047812632812086\n",
            "Epoch: 2828NA_erro: 0.12018516082172245 \n",
            "EU_erro: 0.04175191239793171JP_erro: 0.010264925947485045loss: 0.05740066831426103\n",
            "Epoch: 2829NA_erro: 0.30605476956034816 \n",
            "EU_erro: 0.18803391345711643JP_erro: 0.04803814167200133loss: 0.1807089369426402\n",
            "Epoch: 2830NA_erro: 0.06712086903032406 \n",
            "EU_erro: 0.08268555005391438JP_erro: 0.04615431146104206loss: 0.06532024043475011\n",
            "Epoch: 2831NA_erro: 0.11427342429641606 \n",
            "EU_erro: 0.10760272196096968JP_erro: 0.01254737608192503loss: 0.07814118104387624\n",
            "Epoch: 2832NA_erro: 0.09795106658639834 \n",
            "EU_erro: 0.02468124609584956JP_erro: 0.02073738695115082loss: 0.04778989987779957\n",
            "Epoch: 2833NA_erro: 0.07336378559585689 \n",
            "EU_erro: 0.06544445836266806JP_erro: 0.09549819221792295loss: 0.078102146932321\n",
            "Epoch: 2834NA_erro: 0.2528593706530194 \n",
            "EU_erro: 0.20273362019265345JP_erro: 0.02084493406059206loss: 0.1588126485661943\n",
            "Epoch: 2835NA_erro: 0.6041099119556043 \n",
            "EU_erro: 0.25100802266320515JP_erro: 0.030020242051560748loss: 0.29504607814226963\n",
            "Epoch: 2836NA_erro: 0.06568673045136207 \n",
            "EU_erro: 0.04161545010500176JP_erro: 0.010909352653710416loss: 0.03940384517344393\n",
            "Epoch: 2837NA_erro: 0.13156967569691266 \n",
            "EU_erro: 0.03635779831760613JP_erro: 0.013169720191364141loss: 0.06036573602247608\n",
            "Epoch: 2838NA_erro: 0.22297805593919384 \n",
            "EU_erro: 0.12440104817235192JP_erro: 0.06969560486401698loss: 0.13902491377305615\n",
            "Epoch: 2839NA_erro: 0.7162524378576944 \n",
            "EU_erro: 0.4841821877531303JP_erro: 0.0435197769209396loss: 0.414651493693507\n",
            "Epoch: 2840NA_erro: 0.062150821205257445 \n",
            "EU_erro: 0.04929538845091827JP_erro: 0.018453982680342918loss: 0.04330006680747335\n",
            "Epoch: 2841NA_erro: 0.9577019270076308 \n",
            "EU_erro: 0.5190475227296815JP_erro: 0.17513885054477427loss: 0.5506294827128566\n",
            "Epoch: 2842NA_erro: 0.16802919003390526 \n",
            "EU_erro: 0.10352292726206225JP_erro: 0.013419938410899435loss: 0.09499067483946334\n",
            "Epoch: 2843NA_erro: 0.12197355891382972 \n",
            "EU_erro: 0.035518920698831245JP_erro: 0.011305778987647952loss: 0.05626609159070392\n",
            "Epoch: 2844NA_erro: 0.7522723471471505 \n",
            "EU_erro: 0.14340560565623203JP_erro: 0.02193638520647389loss: 0.3058714460032855\n",
            "Epoch: 2845NA_erro: 0.6424869123355362 \n",
            "EU_erro: 0.31664267990940304JP_erro: 0.037647173848263056loss: 0.3322589489840722\n",
            "Epoch: 2846NA_erro: 0.08224132449127906 \n",
            "EU_erro: 0.06384198517762413JP_erro: 0.009842608557191006loss: 0.05197531031083691\n",
            "Epoch: 2847NA_erro: 0.09831489518631337 \n",
            "EU_erro: 0.1024530007857685JP_erro: 0.04217048024022302loss: 0.08097945257674816\n",
            "Epoch: 2848NA_erro: 5.885578717372214 \n",
            "EU_erro: 2.8386608389920966JP_erro: 0.15969232071277706loss: 2.9613104531931325\n",
            "Epoch: 2849NA_erro: 0.2673178680183351 \n",
            "EU_erro: 0.29101024302401285JP_erro: 0.10965452637783317loss: 0.22266088530074718\n",
            "Epoch: 2850NA_erro: 0.2660739883895992 \n",
            "EU_erro: 0.03239828717800998JP_erro: 0.04404031029043272loss: 0.11417087658431178\n",
            "Epoch: 2851NA_erro: 0.08220419403194457 \n",
            "EU_erro: 0.06511837475059569JP_erro: 0.043980996738108555loss: 0.063767858253893\n",
            "Epoch: 2852NA_erro: 5.818809686705124 \n",
            "EU_erro: 2.7828024339306263JP_erro: 0.06356681502142618loss: 2.8883932542431263\n",
            "Epoch: 2853NA_erro: 0.8088752835295921 \n",
            "EU_erro: 0.5796086510946584JP_erro: 0.07934047270190808loss: 0.4892748086027397\n",
            "Epoch: 2854NA_erro: 0.2112253137337145 \n",
            "EU_erro: 0.18310289974360502JP_erro: 0.027316141036129737loss: 0.1405481312626092\n",
            "Epoch: 2855NA_erro: 0.08498602135236873 \n",
            "EU_erro: 0.07795565812162651JP_erro: 0.11005692703779353loss: 0.090999538584273\n",
            "Epoch: 2856NA_erro: 0.41300736656484677 \n",
            "EU_erro: 0.35240761069364324JP_erro: 0.174332903337109loss: 0.31324929969255316\n",
            "Epoch: 2857NA_erro: 0.6579931392226108 \n",
            "EU_erro: 0.44263159581857137JP_erro: 0.045036710509958194loss: 0.3818871254144713\n",
            "Epoch: 2858NA_erro: 0.08370050164156181 \n",
            "EU_erro: 0.02947319616643033JP_erro: 0.013274313636528428loss: 0.0421493367631306\n",
            "Epoch: 2859NA_erro: 0.09859685749970665 \n",
            "EU_erro: 0.18770421198172163JP_erro: 0.02556834803071133loss: 0.10395647943481919\n",
            "Epoch: 2860NA_erro: 0.043400817139204156 \n",
            "EU_erro: 0.05653489467709563JP_erro: 0.14251777367998464loss: 0.08081783619961998\n",
            "Epoch: 2861NA_erro: 0.22394318913304528 \n",
            "EU_erro: 0.03144336763278458JP_erro: 0.007764082546381988loss: 0.08771687515022218\n",
            "Epoch: 2862NA_erro: 0.050151260324226794 \n",
            "EU_erro: 0.027347207069396973JP_erro: 0.012680937846501669loss: 0.030059800591579703\n",
            "Epoch: 2863NA_erro: 0.1363695407098578 \n",
            "EU_erro: 0.02765297658683718JP_erro: 0.014669605234796686loss: 0.05956404430921688\n",
            "Epoch: 2864NA_erro: 0.08201302484024403 \n",
            "EU_erro: 0.0409751298815705JP_erro: 0.009634488428285879loss: 0.044207545213921125\n",
            "Epoch: 2865NA_erro: 0.6372790373573007 \n",
            "EU_erro: 0.32148453616356665JP_erro: 0.03606819136198177loss: 0.33161059830539913\n",
            "Epoch: 2866NA_erro: 0.2510014060855836 \n",
            "EU_erro: 0.3623729898023975JP_erro: 0.06942321625790855loss: 0.22759921791017518\n",
            "Epoch: 2867NA_erro: 0.577013141425081 \n",
            "EU_erro: 0.09906265162682348JP_erro: 0.06996391355529312loss: 0.2486799114434294\n",
            "Epoch: 2868NA_erro: 0.05689798861510994 \n",
            "EU_erro: 0.02791725387868955JP_erro: 0.013642036175543024loss: 0.03281909365986669\n",
            "Epoch: 2869NA_erro: 0.09302944175956786 \n",
            "EU_erro: 0.04643033179201821JP_erro: 0.026020909002585003loss: 0.05516023136848627\n",
            "Epoch: 2870NA_erro: 0.10846502097078072 \n",
            "EU_erro: 0.05233145961465761JP_erro: 0.017337483722110126loss: 0.05937799464824588\n",
            "Epoch: 2871NA_erro: 0.07447578648264094 \n",
            "EU_erro: 0.05340216695800308JP_erro: 0.028331968211388402loss: 0.05206998004469761\n",
            "Epoch: 2872NA_erro: 1.1855108423750529 \n",
            "EU_erro: 0.6467062188673389JP_erro: 0.06824605224668517loss: 0.6334877753442572\n",
            "Epoch: 2873NA_erro: 0.21098255187042 \n",
            "EU_erro: 0.04205858060555865JP_erro: 0.022487425526907277loss: 0.09184285651805789\n",
            "Epoch: 2874NA_erro: 0.08646395779395288 \n",
            "EU_erro: 0.029502929181091545JP_erro: 0.026280628156292347loss: 0.04741584145745566\n",
            "Epoch: 2875NA_erro: 0.08500194364739944 \n",
            "EU_erro: 0.0344246833823448JP_erro: 0.022068333256152248loss: 0.047164989072223044\n",
            "Epoch: 2876NA_erro: 0.06512253783469976 \n",
            "EU_erro: 0.028591243795646253JP_erro: 0.0265621261079182loss: 0.040091973866603174\n",
            "Epoch: 2877NA_erro: 0.18119518146958463 \n",
            "EU_erro: 0.03489434950111448JP_erro: 0.018556977427283loss: 0.07821550664975661\n",
            "Epoch: 2878NA_erro: 0.6938351032345794 \n",
            "EU_erro: 0.31488041545069495JP_erro: 0.03869213568147763loss: 0.34913587939831636\n",
            "Epoch: 2879NA_erro: 0.23363126340762588 \n",
            "EU_erro: 0.05859883718712385JP_erro: 0.048953927764596866loss: 0.11372800945311554\n",
            "Epoch: 2880NA_erro: 0.11389451433521833 \n",
            "EU_erro: 0.03923086694968763JP_erro: 0.009668969599775565loss: 0.05426478940387105\n",
            "Epoch: 2881NA_erro: 0.6214324448459833 \n",
            "EU_erro: 0.05383694818777631JP_erro: 0.009079360107118769loss: 0.22811627203179885\n",
            "Epoch: 2882NA_erro: 0.10186053061670111 \n",
            "EU_erro: 0.0369954178499621JP_erro: 0.024533740771833318loss: 0.05446323590685231\n",
            "Epoch: 2883NA_erro: 0.044652556264123254 \n",
            "EU_erro: 0.04158128139584563JP_erro: 0.019631642241810642loss: 0.03528849561085073\n",
            "Epoch: 2884NA_erro: 0.7554535533106604 \n",
            "EU_erro: 0.4632915822110435JP_erro: 0.04567876804706662loss: 0.4214746637861858\n",
            "Epoch: 2885NA_erro: 0.5973475848057473 \n",
            "EU_erro: 0.10305812192517658JP_erro: 0.12372655461924945loss: 0.2747107476227043\n",
            "Epoch: 2886NA_erro: 0.20263899204342864 \n",
            "EU_erro: 0.04405869069949601JP_erro: 0.06607349066771279loss: 0.10425706242406091\n",
            "Epoch: 2887NA_erro: 0.32749493916829425 \n",
            "EU_erro: 0.034353892470515054JP_erro: 0.010925380065459614loss: 0.12425806171210237\n",
            "Epoch: 2888NA_erro: 0.06292752979337707 \n",
            "EU_erro: 0.03729965566664703JP_erro: 0.01235857836959898loss: 0.03752859063850817\n",
            "Epoch: 2889NA_erro: 0.12661354486332382 \n",
            "EU_erro: 0.04015097784441571JP_erro: 0.006755901168483173loss: 0.05784014860788981\n",
            "Epoch: 2890NA_erro: 0.519704264263774 \n",
            "EU_erro: 0.2149812380472819JP_erro: 0.030410292074661845loss: 0.2550319183704465\n",
            "Epoch: 2891NA_erro: 0.17574995987175046 \n",
            "EU_erro: 0.0665030322333639JP_erro: 0.020250038121097772loss: 0.0875010139258333\n",
            "Epoch: 2892NA_erro: 0.05113900631897209 \n",
            "EU_erro: 0.023935603540997174JP_erro: 0.008764225383137548loss: 0.02794627995454064\n",
            "Epoch: 2893NA_erro: 0.06529972072719603 \n",
            "EU_erro: 0.020886691965798076JP_erro: 0.04623319751532503loss: 0.04413986852926801\n",
            "Epoch: 2894NA_erro: 0.18990187681922616 \n",
            "EU_erro: 0.17875198245972626JP_erro: 0.020404698312744614loss: 0.1296861874040707\n",
            "Epoch: 2895NA_erro: 0.621463420779206 \n",
            "EU_erro: 0.15268726866374643JP_erro: 0.08738977964534315loss: 0.2871801686841388\n",
            "Epoch: 2896NA_erro: 0.19894078720447628 \n",
            "EU_erro: 0.16843283823294233JP_erro: 0.051681824432787044loss: 0.13968516689862392\n",
            "Epoch: 2897NA_erro: 0.36406838616659476 \n",
            "EU_erro: 0.23672519728194835JP_erro: 0.17146785129872402loss: 0.25742050289183627\n",
            "Epoch: 2898NA_erro: 0.033355357573013895 \n",
            "EU_erro: 0.02366540736930315JP_erro: 0.023252471934917363loss: 0.0267577471659165\n",
            "Epoch: 2899NA_erro: 0.39125187452449356 \n",
            "EU_erro: 0.15871903693029124JP_erro: 0.06838639577229817loss: 0.20611912705177485\n",
            "Epoch: 2900NA_erro: 0.08105232734088749 \n",
            "EU_erro: 0.023321525533069935JP_erro: 0.06320235803145771loss: 0.05585873773855756\n",
            "Epoch: 2901NA_erro: 0.10321628215701081 \n",
            "EU_erro: 0.22923733836920687JP_erro: 0.0695123552351959loss: 0.13398865396662277\n",
            "Epoch: 2902NA_erro: 0.06294043027153311 \n",
            "EU_erro: 0.21382476008215615JP_erro: 0.05644717881845873loss: 0.11107079742490783\n",
            "Epoch: 2903NA_erro: 0.5255158372627672 \n",
            "EU_erro: 0.2090241742688556JP_erro: 0.0642766601355501loss: 0.26627222696940106\n",
            "Epoch: 2904NA_erro: 0.09679973587509273 \n",
            "EU_erro: 0.05873771600945051JP_erro: 0.053430841874706654loss: 0.06965609945992167\n",
            "Epoch: 2905NA_erro: 0.086768671523693 \n",
            "EU_erro: 0.03243401300075442JP_erro: 0.02792713947074358loss: 0.049043279285578764\n",
            "Epoch: 2906NA_erro: 0.054616539977317635 \n",
            "EU_erro: 0.07796963055928548JP_erro: 0.10398687318313954loss: 0.07885768432025761\n",
            "Epoch: 2907NA_erro: 0.22101564924846323 \n",
            "EU_erro: 0.12765803078348323JP_erro: 0.07004851980726848loss: 0.13957408047461695\n",
            "Epoch: 2908NA_erro: 0.18610887749250546 \n",
            "EU_erro: 0.0649902155232984JP_erro: 0.009626029420268628loss: 0.08690837741822235\n",
            "Epoch: 2909NA_erro: 0.19206991491391678 \n",
            "EU_erro: 0.028011253637860913JP_erro: 0.023834571357845336loss: 0.08130524509636931\n",
            "Epoch: 2910NA_erro: 0.10132119637127071 \n",
            "EU_erro: 0.0533877879150154JP_erro: 0.04511924215065417loss: 0.06660941959351532\n",
            "Epoch: 2911NA_erro: 0.08402190467183904 \n",
            "EU_erro: 0.03034164508183797JP_erro: 0.01213232907213906loss: 0.04216529140176699\n",
            "Epoch: 2912NA_erro: 0.21369435066400572 \n",
            "EU_erro: 0.2561289210652196JP_erro: 0.05544481351394062loss: 0.17508937406909558\n",
            "Epoch: 2913NA_erro: 0.06184498006983321 \n",
            "EU_erro: 0.04304328630136889JP_erro: 0.10807317356730617loss: 0.07098714972651282\n",
            "Epoch: 2914NA_erro: 0.09684280831684437 \n",
            "EU_erro: 0.027879568957543188JP_erro: 0.026884157528248867loss: 0.05053551160087881\n",
            "Epoch: 2915NA_erro: 0.33172825510187665 \n",
            "EU_erro: 0.05265346793241279JP_erro: 0.005531082897223243loss: 0.1299709405085837\n",
            "Epoch: 2916NA_erro: 0.5585860097131063 \n",
            "EU_erro: 0.10300500448359999JP_erro: 0.09282233179077622loss: 0.25147110916847404\n",
            "Epoch: 2917NA_erro: 0.07511375486388687 \n",
            "EU_erro: 0.07129694602286168JP_erro: 0.008803595059601835loss: 0.05173810230669125\n",
            "Epoch: 2918NA_erro: 0.819332802942557 \n",
            "EU_erro: 0.47268856403439546JP_erro: 0.04762174085129139loss: 0.44654772263164666\n",
            "Epoch: 2919NA_erro: 0.23512575977532438 \n",
            "EU_erro: 0.13232344804808152JP_erro: 0.07176486558692399loss: 0.14640469883763513\n",
            "Epoch: 2920NA_erro: 0.23002770519995874 \n",
            "EU_erro: 0.030014836048895075JP_erro: 0.006814841036648713loss: 0.08895246557487074\n",
            "Epoch: 2921NA_erro: 0.26019551033197447 \n",
            "EU_erro: 0.13443395148876103JP_erro: 0.05406212899111962loss: 0.1495638743851536\n",
            "Epoch: 2922NA_erro: 0.16616057979968168 \n",
            "EU_erro: 0.06948287634886513JP_erro: 0.009491693141848542loss: 0.08171173029167708\n",
            "Epoch: 2923NA_erro: 0.20871269610501075 \n",
            "EU_erro: 0.059374727944070976JP_erro: 0.009769893200822579loss: 0.09261910305466763\n",
            "Epoch: 2924NA_erro: 0.09348317634227664 \n",
            "EU_erro: 0.07556955481684485JP_erro: 0.08939669113750606loss: 0.08614980897238088\n",
            "Epoch: 2925NA_erro: 0.11032585025757782 \n",
            "EU_erro: 0.052435541337774706JP_erro: 0.02021229313325512loss: 0.060991224392439966\n",
            "Epoch: 2926NA_erro: 0.10454057723052743 \n",
            "EU_erro: 0.03693993239439735JP_erro: 0.023277073867561282loss: 0.05491919757783875\n",
            "Epoch: 2927NA_erro: 0.5601701440737229 \n",
            "EU_erro: 0.11212713958681092JP_erro: 0.07385463215583979loss: 0.24871733761573023\n",
            "Epoch: 2928NA_erro: 0.10486864304357721 \n",
            "EU_erro: 0.08692263632781747JP_erro: 0.01649300603903541loss: 0.06942809552185294\n",
            "Epoch: 2929NA_erro: 0.5444860088732816 \n",
            "EU_erro: 0.10622270347536072JP_erro: 0.1244297046069951loss: 0.2583794630775156\n",
            "Epoch: 2930NA_erro: 0.8954257373661958 \n",
            "EU_erro: 0.04707410354022832JP_erro: 0.009414167251697806loss: 0.31730467034864795\n",
            "Epoch: 2931NA_erro: 0.0867486554522847 \n",
            "EU_erro: 0.032773209634677385JP_erro: 0.009428462547849315loss: 0.04298344601032346\n",
            "Epoch: 2932NA_erro: 0.12204635974972747 \n",
            "EU_erro: 0.031185845534006756JP_erro: 0.010149157786554144loss: 0.05446045435676279\n",
            "Epoch: 2933NA_erro: 0.5255131758460703 \n",
            "EU_erro: 0.26988315951916597JP_erro: 0.0448853147122287loss: 0.2800938879796701\n",
            "Epoch: 2934NA_erro: 0.2777610083883123 \n",
            "EU_erro: 0.13427722361660743JP_erro: 0.11346399322036625loss: 0.17516742381014566\n",
            "Epoch: 2935NA_erro: 0.054513188295586164 \n",
            "EU_erro: 0.03015393665594648JP_erro: 0.009232930434766666loss: 0.03130001846209977\n",
            "Epoch: 2936NA_erro: 0.6822319178618201 \n",
            "EU_erro: 0.12838483780853507JP_erro: 0.07233272227205971loss: 0.2943164803260981\n",
            "Epoch: 2937NA_erro: 0.07000027238860611 \n",
            "EU_erro: 0.0803089419076609JP_erro: 0.06365725698397141loss: 0.07132216017375621\n",
            "Epoch: 2938NA_erro: 0.1951725538386855 \n",
            "EU_erro: 0.08248975110608478JP_erro: 0.04629500614580258loss: 0.10798577190369599\n",
            "Epoch: 2939NA_erro: 1.0365211871243263 \n",
            "EU_erro: 0.23883137592049533JP_erro: 0.03783294858858567loss: 0.4377284530521363\n",
            "Epoch: 2940NA_erro: 0.05646028721979422 \n",
            "EU_erro: 0.024377255476722422JP_erro: 0.04329323768615723loss: 0.04137692987456802\n",
            "Epoch: 2941NA_erro: 0.16095643819764602 \n",
            "EU_erro: 0.1002657228662062JP_erro: 0.013757031555323637loss: 0.09165972702262938\n",
            "Epoch: 2942NA_erro: 0.22187528684157734 \n",
            "EU_erro: 0.15661241472229476JP_erro: 0.05152922268061675loss: 0.14333895934644594\n",
            "Epoch: 2943NA_erro: 0.15878538752711097 \n",
            "EU_erro: 0.03935776477636293JP_erro: 0.0085807274016299loss: 0.06890796413717344\n",
            "Epoch: 2944NA_erro: 0.09856389474499133 \n",
            "EU_erro: 0.020870296067969744JP_erro: 0.007997823431510334loss: 0.04247734250948411\n",
            "Epoch: 2945NA_erro: 0.06963737251222596 \n",
            "EU_erro: 0.025199919708015384JP_erro: 0.04226816254992818loss: 0.045701819796894874\n",
            "Epoch: 2946NA_erro: 0.3403390100760053 \n",
            "EU_erro: 0.20387989606044088JP_erro: 0.02059088196865348loss: 0.18826992936836656\n",
            "Epoch: 2947NA_erro: 0.23270516432532967 \n",
            "EU_erro: 0.16785218734149784JP_erro: 0.03958531590395196loss: 0.14671422714410826\n",
            "Epoch: 2948NA_erro: 0.4647103760593621 \n",
            "EU_erro: 0.3565759437028752JP_erro: 0.15722040058106415loss: 0.3261689252631609\n",
            "Epoch: 2949NA_erro: 0.11267696240151576 \n",
            "EU_erro: 0.21501382191975912JP_erro: 0.054474713266357895loss: 0.1273884976557059\n",
            "Epoch: 2950NA_erro: 0.1993442136187886 \n",
            "EU_erro: 0.07424851258595784JP_erro: 0.009737546129744182loss: 0.09444343027218367\n",
            "Epoch: 2951NA_erro: 0.1287070448084395 \n",
            "EU_erro: 0.08757497913153596JP_erro: 0.00996342232060987loss: 0.0754151556843011\n",
            "Epoch: 2952NA_erro: 0.08509153543516647 \n",
            "EU_erro: 0.2175018029619557JP_erro: 0.054745452348576036loss: 0.11911293332890947\n",
            "Epoch: 2953NA_erro: 0.05880324877509775 \n",
            "EU_erro: 0.03705248814220576JP_erro: 0.08981396985608478loss: 0.06188990995865459\n",
            "Epoch: 2954NA_erro: 0.21283460217852926 \n",
            "EU_erro: 0.05518351876458456JP_erro: 0.00957945520563643loss: 0.09253253308377525\n",
            "Epoch: 2955NA_erro: 0.21091828974642496 \n",
            "EU_erro: 0.2147683247115261JP_erro: 0.0338188137194907loss: 0.1531684860702633\n",
            "Epoch: 2956NA_erro: 0.6574124328849852 \n",
            "EU_erro: 0.6296579967173495JP_erro: 0.09418073550675267loss: 0.46041699342949444\n",
            "Epoch: 2957NA_erro: 0.6988997792088708 \n",
            "EU_erro: 0.4876597722371419JP_erro: 0.04501023495844168loss: 0.41052326675533324\n",
            "Epoch: 2958NA_erro: 0.35957055498463236 \n",
            "EU_erro: 0.27249559875606566JP_erro: 0.18742881079976872loss: 0.2731649820194688\n",
            "Epoch: 2959NA_erro: 0.5973190115403759 \n",
            "EU_erro: 0.1161450840706049JP_erro: 0.07179052792778311loss: 0.26175155196078986\n",
            "Epoch: 2960NA_erro: 0.585036647412204 \n",
            "EU_erro: 0.22578729215518448JP_erro: 0.054530196411665094loss: 0.28845138328019965\n",
            "Epoch: 2961NA_erro: 0.5608794855517011 \n",
            "EU_erro: 0.13742077258206153JP_erro: 0.10883649190266927loss: 0.26904562647028485\n",
            "Epoch: 2962NA_erro: 0.09200290192005246 \n",
            "EU_erro: 0.05267405232717825JP_erro: 0.013049274914024411loss: 0.05257541357084762\n",
            "Epoch: 2963NA_erro: 0.19160006397454313 \n",
            "EU_erro: 0.04293004209681075JP_erro: 0.10342082311940748loss: 0.11265031127042549\n",
            "Epoch: 2964NA_erro: 0.04699478777804116 \n",
            "EU_erro: 0.02949540698251059JP_erro: 0.05660258522329405loss: 0.04436425922452941\n",
            "Epoch: 2965NA_erro: 0.6652850513310395 \n",
            "EU_erro: 0.32123654387718026JP_erro: 0.061403536057287406loss: 0.3493083909500477\n",
            "Epoch: 2966NA_erro: 0.07305220578067986 \n",
            "EU_erro: 0.03618929506272309JP_erro: 0.01119595396426297loss: 0.040145821349565375\n",
            "Epoch: 2967NA_erro: 0.3997884794723156 \n",
            "EU_erro: 0.373469212258509JP_erro: 0.14242918916450914loss: 0.3052289726198182\n",
            "Epoch: 2968NA_erro: 0.27879620707312297 \n",
            "EU_erro: 0.16697362411853878JP_erro: 0.049382688463196274loss: 0.16505084296529607\n",
            "Epoch: 2969NA_erro: 0.15905160312504732 \n",
            "EU_erro: 0.025804396285567174JP_erro: 0.008662688986275547loss: 0.06450623042823732\n",
            "Epoch: 2970NA_erro: 0.10450863098913385 \n",
            "EU_erro: 0.21425489307373993JP_erro: 0.0862427829772003loss: 0.13500209926634796\n",
            "Epoch: 2971NA_erro: 0.08191162301588428 \n",
            "EU_erro: 0.035715916360071466JP_erro: 0.007806902014931967loss: 0.04181148007858631\n",
            "Epoch: 2972NA_erro: 6.334557348443556 \n",
            "EU_erro: 2.8943444776904674JP_erro: 0.10703972143720286loss: 3.1119806452314984\n",
            "Epoch: 2973NA_erro: 0.8253037652304006 \n",
            "EU_erro: 0.33775488535563153JP_erro: 0.05518549172453178loss: 0.40608139925224834\n",
            "Epoch: 2974NA_erro: 0.35004915193069813 \n",
            "EU_erro: 0.04427038421926573JP_erro: 0.06663473539574202loss: 0.1536514407904573\n",
            "Epoch: 2975NA_erro: 0.6749074773270954 \n",
            "EU_erro: 0.3165917433509531JP_erro: 0.041866746059683865loss: 0.3444552976031636\n",
            "Epoch: 2976NA_erro: 0.1779623549113902 \n",
            "EU_erro: 0.19801644391791764JP_erro: 0.14290777287741965loss: 0.17296219980993935\n",
            "Epoch: 2977NA_erro: 0.08193688799244489 \n",
            "EU_erro: 0.019253969423530637JP_erro: 0.032693524231282314loss: 0.04462813222131064\n",
            "Epoch: 2978NA_erro: 0.9505373193312061 \n",
            "EU_erro: 0.4583830796470938JP_erro: 0.05032460356867591loss: 0.486415005469507\n",
            "Epoch: 2979NA_erro: 0.6152624307676803 \n",
            "EU_erro: 0.12844868408617124JP_erro: 0.07355177125265432loss: 0.2724209681961888\n",
            "Epoch: 2980NA_erro: 0.574511446694071 \n",
            "EU_erro: 0.2284742325775383JP_erro: 0.03207299829453461loss: 0.27835289637247723\n",
            "Epoch: 2981NA_erro: 0.6649677513181701 \n",
            "EU_erro: 0.3221101169438325JP_erro: 0.05779385104659916loss: 0.34829058388406914\n",
            "Epoch: 2982NA_erro: 0.062387793563133065 \n",
            "EU_erro: 0.02552640992541646JP_erro: 0.00803262803905694loss: 0.031982281411341\n",
            "Epoch: 2983NA_erro: 0.05373054234556449 \n",
            "EU_erro: 0.032867904781371124JP_erro: 0.011942648841429126loss: 0.03284703391466954\n",
            "Epoch: 2984NA_erro: 0.2079956291257873 \n",
            "EU_erro: 0.13246393018914748JP_erro: 0.08872107941974965loss: 0.14306021291156149\n",
            "Epoch: 2985NA_erro: 0.5423556187356166 \n",
            "EU_erro: 0.2227808153906534JP_erro: 0.02933251996373021loss: 0.26482299316761104\n",
            "Epoch: 2986NA_erro: 0.05190497683000195 \n",
            "EU_erro: 0.015130615973657415JP_erro: 0.04185568454653718loss: 0.036297091679979666\n",
            "Epoch: 2987NA_erro: 0.5582011023233103 \n",
            "EU_erro: 0.21800632624663124JP_erro: 0.028894573681114255loss: 0.2683673718178919\n",
            "Epoch: 2988NA_erro: 0.2535998913668847 \n",
            "EU_erro: 0.15147371809611948JP_erro: 0.049336635789205864loss: 0.15147008637125178\n",
            "Epoch: 2989NA_erro: 0.6500187955161397 \n",
            "EU_erro: 0.3128076154132222JP_erro: 0.061207852622335274loss: 0.34134476683860604\n",
            "Epoch: 2990NA_erro: 0.5156985364219014 \n",
            "EU_erro: 0.2284795738929926JP_erro: 0.04460025203320407loss: 0.2629261053809824\n",
            "Epoch: 2991NA_erro: 0.06582522115042043 \n",
            "EU_erro: 0.02324378536653149JP_erro: 0.012088382197904956loss: 0.03371913072674773\n",
            "Epoch: 2992NA_erro: 5.855654339457667 \n",
            "EU_erro: 2.8218093398929565JP_erro: 0.04438926545224448loss: 2.907283989958061\n",
            "Epoch: 2993NA_erro: 0.11909592983334563 \n",
            "EU_erro: 0.0798613803331242JP_erro: 0.00946670019811438loss: 0.06947467031404954\n",
            "Epoch: 2994NA_erro: 0.19834864047146583 \n",
            "EU_erro: 0.24669601011645886JP_erro: 0.05538315736046133loss: 0.16680926315544187\n",
            "Epoch: 2995NA_erro: 0.2702972685643869 \n",
            "EU_erro: 0.05596185839453409JP_erro: 0.008897918254830117loss: 0.11171902797018834\n",
            "Epoch: 2996NA_erro: 0.07336956123973049 \n",
            "EU_erro: 0.043887509856113165JP_erro: 0.09715966476026432loss: 0.07147224374519762\n",
            "Epoch: 2997NA_erro: 0.8533861470776934 \n",
            "EU_erro: 0.3391049629033998JP_erro: 0.04413127437118412loss: 0.41220746299093086\n",
            "Epoch: 2998NA_erro: 0.23453156153361002 \n",
            "EU_erro: 0.3142728731613751JP_erro: 0.0824933643488921loss: 0.21043263664541317\n",
            "Epoch: 2999NA_erro: 0.07141575332759886 \n",
            "EU_erro: 0.02892196409462034JP_erro: 0.012084613012713056loss: 0.03747411245523497\n",
            "Epoch: 3000NA_erro: 0.8049079244451005 \n",
            "EU_erro: 0.29498697251312495JP_erro: 0.20560661951700845loss: 0.4351672091225321\n",
            "Epoch: 3001NA_erro: 0.5899908006653305 \n",
            "EU_erro: 0.11095603307088216JP_erro: 0.07321293501890908loss: 0.25805325471153556\n",
            "Epoch: 3002NA_erro: 0.058526572330977564 \n",
            "EU_erro: 0.037654418815938076JP_erro: 0.010978109383767889loss: 0.035719700561937434\n",
            "Epoch: 3003NA_erro: 0.3456812496333159 \n",
            "EU_erro: 0.28547573459240816JP_erro: 0.08439902187317841loss: 0.23851869642272477\n",
            "Epoch: 3004NA_erro: 0.08442634759947311 \n",
            "EU_erro: 0.05264572394910709JP_erro: 0.03988225330678068loss: 0.058984779572302054\n",
            "Epoch: 3005NA_erro: 0.03063034179598786 \n",
            "EU_erro: 0.024248225282329JP_erro: 0.035857736140258556loss: 0.030245434406191804\n",
            "Epoch: 3006NA_erro: 0.04681133946707083 \n",
            "EU_erro: 0.03515522609385409JP_erro: 0.013334722250931023loss: 0.031767097092414084\n",
            "Epoch: 3007NA_erro: 0.4055697049281394 \n",
            "EU_erro: 0.22975261821303256JP_erro: 0.14920995216961055loss: 0.2615107861600181\n",
            "Epoch: 3008NA_erro: 0.25728950204775314 \n",
            "EU_erro: 0.04929850267809491JP_erro: 0.02348996179048405loss: 0.11002598806869152\n",
            "Epoch: 3009NA_erro: 0.07143515949101412 \n",
            "EU_erro: 0.03387874873109566JP_erro: 0.01655709142832793loss: 0.04062367040057515\n",
            "Epoch: 3010NA_erro: 0.11247416799382647 \n",
            "EU_erro: 0.05373699258464251JP_erro: 0.040336057197215947loss: 0.06884907567223837\n",
            "Epoch: 3011NA_erro: 0.567161759664846 \n",
            "EU_erro: 0.1368950873382332JP_erro: 0.07135723450387171loss: 0.25847137436386225\n",
            "Epoch: 3012NA_erro: 0.16162297522374827 \n",
            "EU_erro: 0.04762140355368917JP_erro: 0.008905702667642934loss: 0.0727167009383209\n",
            "Epoch: 3013NA_erro: 1.0199206744053566 \n",
            "EU_erro: 0.20367890365364016JP_erro: 0.07554863774499228loss: 0.4330494237500568\n",
            "Epoch: 3014NA_erro: 0.198395289191904 \n",
            "EU_erro: 0.15784718269525572JP_erro: 0.04586334838423618loss: 0.13403526572294014\n",
            "Epoch: 3015NA_erro: 0.5170254005018131 \n",
            "EU_erro: 0.20706522372341896JP_erro: 0.09583548982014027loss: 0.2733087170031644\n",
            "Epoch: 3016NA_erro: 0.0848919199418652 \n",
            "EU_erro: 0.02783962691477103JP_erro: 0.01321243685345317loss: 0.041981325593105585\n",
            "Epoch: 3017NA_erro: 0.08264651594235915 \n",
            "EU_erro: 0.029256461664687757JP_erro: 0.04017770290374756loss: 0.05069356094035067\n",
            "Epoch: 3018NA_erro: 0.15679041544596353 \n",
            "EU_erro: 0.04129198632499044JP_erro: 0.01080550888712092loss: 0.06962930971337844\n",
            "Epoch: 3019NA_erro: 0.2076884387999542 \n",
            "EU_erro: 0.23334013399227646JP_erro: 0.019338340029235956loss: 0.1534556510836579\n",
            "Epoch: 3020NA_erro: 0.6630939661070357 \n",
            "EU_erro: 0.3265691357989644JP_erro: 0.039084997288016385loss: 0.3429160376851873\n",
            "Epoch: 3021NA_erro: 0.5290830227755761 \n",
            "EU_erro: 0.21980522214904313JP_erro: 0.024128315984740738loss: 0.25767221007236213\n",
            "Epoch: 3022NA_erro: 0.2880435396534528 \n",
            "EU_erro: 0.31520311222519987JP_erro: 0.04067039766976999loss: 0.21463902421699937\n",
            "Epoch: 3023NA_erro: 0.17336581104485563 \n",
            "EU_erro: 0.11151076287262199JP_erro: 0.008719991459402927loss: 0.0978655223698579\n",
            "Epoch: 3024NA_erro: 0.19925145215766374 \n",
            "EU_erro: 0.18006437508634818JP_erro: 0.03299434517705163loss: 0.13743673184121302\n",
            "Epoch: 3025NA_erro: 0.058475538741710575 \n",
            "EU_erro: 0.21758979605149853JP_erro: 0.05584493625995725loss: 0.11063676471858061\n",
            "Epoch: 3026NA_erro: 0.20014247228932935 \n",
            "EU_erro: 0.12427031531814457JP_erro: 0.09215852086858232loss: 0.13885710590569547\n",
            "Epoch: 3027NA_erro: 0.14828218970187876 \n",
            "EU_erro: 0.06100260010061338JP_erro: 0.014909176632415417loss: 0.0747313286906989\n",
            "Epoch: 3028NA_erro: 0.4908439355303151 \n",
            "EU_erro: 0.21558114724565847JP_erro: 0.06051652191221252loss: 0.25564720464307206\n",
            "Epoch: 3029NA_erro: 0.045164278311322824 \n",
            "EU_erro: 0.090391617412715JP_erro: 0.10385761889376381loss: 0.07980451103328734\n",
            "Epoch: 3030NA_erro: 0.21028217419173367 \n",
            "EU_erro: 0.055445774581081186JP_erro: 0.00749071670133014loss: 0.09107289388198261\n",
            "Epoch: 3031NA_erro: 0.04412560037864271 \n",
            "EU_erro: 0.03525155459263528JP_erro: 0.01979028069695761loss: 0.03305581188941187\n",
            "Epoch: 3032NA_erro: 0.08798721224762672 \n",
            "EU_erro: 0.04972403825715531JP_erro: 0.013400395942288775loss: 0.05037054535030395\n",
            "Epoch: 3033NA_erro: 0.23743346680042354 \n",
            "EU_erro: 0.03985704377640125JP_erro: 0.027168254519617834loss: 0.10148625965266264\n",
            "Epoch: 3034NA_erro: 5.782456508902616 \n",
            "EU_erro: 2.810029909592266JP_erro: 0.05573096201401348loss: 2.8827395180399105\n",
            "Epoch: 3035NA_erro: 0.36802750225215 \n",
            "EU_erro: 0.26324824769367544JP_erro: 0.09440678034641946loss: 0.2418941860051118\n",
            "Epoch: 3036NA_erro: 0.1536732218986334 \n",
            "EU_erro: 0.05947899910830712JP_erro: 0.009885804597721544loss: 0.07434601007505905\n",
            "Epoch: 3037NA_erro: 0.3134847611419914 \n",
            "EU_erro: 0.2563799444095109JP_erro: 0.1439278532368268loss: 0.23793085600978645\n",
            "Epoch: 3038NA_erro: 0.08478735768517782 \n",
            "EU_erro: 0.030238607595133228JP_erro: 0.012453208829081335loss: 0.04249305688133535\n",
            "Epoch: 3039NA_erro: 0.6207094266433124 \n",
            "EU_erro: 0.2814104575519414JP_erro: 0.02868700859158538loss: 0.31026899352554205\n",
            "Epoch: 3040NA_erro: 0.1975601957749951 \n",
            "EU_erro: 0.03744120514670084JP_erro: 0.1035790387974229loss: 0.11286013810209526\n",
            "Epoch: 3041NA_erro: 0.2064249127410179 \n",
            "EU_erro: 0.13526521911916806JP_erro: 0.11123306991518006loss: 0.15097440675247548\n",
            "Epoch: 3042NA_erro: 0.07792872051860011 \n",
            "EU_erro: 0.03594672726106274JP_erro: 0.06338246108949647loss: 0.05908597347348235\n",
            "Epoch: 3043NA_erro: 0.7045498190000076 \n",
            "EU_erro: 0.45970432518064513JP_erro: 0.04495030687760937loss: 0.40306812108949175\n",
            "Epoch: 3044NA_erro: 0.09497847667960234 \n",
            "EU_erro: 0.03992126893627551JP_erro: 0.009273800045944924loss: 0.0480578435483829\n",
            "Epoch: 3045NA_erro: 0.21780949230341948 \n",
            "EU_erro: 0.06481710330460423JP_erro: 0.028115487837976263loss: 0.10358068370079809\n",
            "Epoch: 3046NA_erro: 0.14869675155757933 \n",
            "EU_erro: 0.05656291348065517JP_erro: 0.04175292891125346loss: 0.08233753285666769\n",
            "Epoch: 3047NA_erro: 0.1718874495158824 \n",
            "EU_erro: 0.04103050213451533JP_erro: 0.010605824086093164loss: 0.07450792678566866\n",
            "Epoch: 3048NA_erro: 0.05070504754088646 \n",
            "EU_erro: 0.03676704427068548JP_erro: 0.03432062938231831loss: 0.04059757373129674\n",
            "Epoch: 3049NA_erro: 5.898041836051053 \n",
            "EU_erro: 2.926624800807746JP_erro: 0.06111908328625583loss: 2.9619287890057233\n",
            "Epoch: 3050NA_erro: 0.43264854786007906 \n",
            "EU_erro: 0.17750658730203792JP_erro: 0.02156266408373219loss: 0.21057260128878807\n",
            "Epoch: 3051NA_erro: 0.2025996437368467 \n",
            "EU_erro: 0.13079440870950387JP_erro: 0.06397547647934551loss: 0.1324565096418987\n",
            "Epoch: 3052NA_erro: 0.11425051578255586 \n",
            "EU_erro: 0.024940997593162595JP_erro: 0.01856739784395972loss: 0.052586308745450755\n",
            "Epoch: 3053NA_erro: 0.17156157382698947 \n",
            "EU_erro: 0.059071599051009775JP_erro: 0.1576216461122498loss: 0.1294182806976082\n",
            "Epoch: 3054NA_erro: 0.1177541876948157 \n",
            "EU_erro: 0.08930704390355783JP_erro: 0.011851712483768316loss: 0.07297098174575688\n",
            "Epoch: 3055NA_erro: 0.0815684296364008 \n",
            "EU_erro: 0.07520525492438974JP_erro: 0.009990176258161086loss: 0.05558795245118844\n",
            "Epoch: 3056NA_erro: 5.7228851318359375 \n",
            "EU_erro: 2.7808459969454034JP_erro: 0.054253168808397395loss: 2.852661073669907\n",
            "Epoch: 3057NA_erro: 0.09522721748943477 \n",
            "EU_erro: 0.032968712869540666JP_erro: 0.010219596384107604loss: 0.04613850929940394\n",
            "Epoch: 3058NA_erro: 0.05251615084418955 \n",
            "EU_erro: 0.06758327169935832JP_erro: 0.012430300315221151loss: 0.044176578521728516\n",
            "Epoch: 3059NA_erro: 0.0553677895272425 \n",
            "EU_erro: 0.03604105969732122JP_erro: 0.013335521600043128loss: 0.034914792970169424\n",
            "Epoch: 3060NA_erro: 1.203508968501128 \n",
            "EU_erro: 0.5208251642626386JP_erro: 0.046638254971467244loss: 0.5903241431066232\n",
            "Epoch: 3061NA_erro: 0.10241473367971968 \n",
            "EU_erro: 0.09201873180478118JP_erro: 0.02070479152738586loss: 0.07171275079712387\n",
            "Epoch: 3062NA_erro: 0.24944837703261263 \n",
            "EU_erro: 0.20554716272871623JP_erro: 0.02560664285985074loss: 0.16020072523013565\n",
            "Epoch: 3063NA_erro: 0.05970424459886181 \n",
            "EU_erro: 0.028720204682313195JP_erro: 0.011746244606121566loss: 0.0333902309107226\n",
            "Epoch: 3064NA_erro: 0.0694801040398058 \n",
            "EU_erro: 0.0854614815970724JP_erro: 0.012571401605310365loss: 0.05583766356919163\n",
            "Epoch: 3065NA_erro: 0.06348971710648647 \n",
            "EU_erro: 0.14197635096172953JP_erro: 0.1043664484985115loss: 0.103277513223101\n",
            "Epoch: 3066NA_erro: 0.050666535547537396 \n",
            "EU_erro: 0.05028281100960665JP_erro: 0.025701853193978007loss: 0.0422170688939649\n",
            "Epoch: 3067NA_erro: 0.2471968554711157 \n",
            "EU_erro: 0.08544291636740514JP_erro: 0.010149696076563162loss: 0.11426315751186637\n",
            "Epoch: 3068NA_erro: 0.06485213142956874 \n",
            "EU_erro: 0.06522918856421182JP_erro: 0.008552843170572621loss: 0.04621138868405837\n",
            "Epoch: 3069NA_erro: 0.5626039726789608 \n",
            "EU_erro: 0.21500994068707607JP_erro: 0.05061580691226693loss: 0.27607657188592954\n",
            "Epoch: 3070NA_erro: 0.1980300836784895 \n",
            "EU_erro: 0.031183412832807202JP_erro: 0.023402017216349758loss: 0.08420517278272052\n",
            "Epoch: 3071NA_erro: 0.244823648023975 \n",
            "EU_erro: 0.033706872500190437JP_erro: 0.026106631108956745loss: 0.10154572568198507\n",
            "Epoch: 3072NA_erro: 0.6073271211727646 \n",
            "EU_erro: 0.047752612320951716JP_erro: 0.009444346723630447loss: 0.22150806678357973\n",
            "Epoch: 3073NA_erro: 0.30679292457048285 \n",
            "EU_erro: 0.03675142923990885JP_erro: 0.02926378980163456loss: 0.1242693912151248\n",
            "Epoch: 3074NA_erro: 0.5433637781660686 \n",
            "EU_erro: 0.10611524877622146JP_erro: 0.07575054501378259loss: 0.2417431875716808\n",
            "Epoch: 3075NA_erro: 0.3008604234503221 \n",
            "EU_erro: 0.20512475523837778JP_erro: 0.025319861349209334loss: 0.17710168232289394\n",
            "Epoch: 3076NA_erro: 0.09214710819628812 \n",
            "EU_erro: 0.035942543384640716JP_erro: 0.04407536613848782loss: 0.05738834540049235\n",
            "Epoch: 3077NA_erro: 0.07629172746525255 \n",
            "EU_erro: 0.08436106896215631JP_erro: 0.01410277430401292loss: 0.0582518596057744\n",
            "Epoch: 3078NA_erro: 0.17076734424561493 \n",
            "EU_erro: 0.3920358835264694JP_erro: 0.16876858334208644loss: 0.24385727653207706\n",
            "Epoch: 3079NA_erro: 0.07215196772139201 \n",
            "EU_erro: 0.041868256968121194JP_erro: 0.034878956255062606loss: 0.04963306493537371\n",
            "Epoch: 3080NA_erro: 0.7127148236415183 \n",
            "EU_erro: 0.475020075953284JP_erro: 0.061308150143586385loss: 0.41634766630424086\n",
            "Epoch: 3081NA_erro: 0.2939355835434078 \n",
            "EU_erro: 0.03815059976060261JP_erro: 0.02471153126206509loss: 0.11893256690151008\n",
            "Epoch: 3082NA_erro: 0.09456922841626544 \n",
            "EU_erro: 0.02774595059165659JP_erro: 0.008607883901559105loss: 0.04364102385764898\n",
            "Epoch: 3083NA_erro: 0.06463580353315486 \n",
            "EU_erro: 0.032460121221320574JP_erro: 0.016404623671095502loss: 0.03783351691194283\n",
            "Epoch: 3084NA_erro: 0.6830100865327111 \n",
            "EU_erro: 0.2355292785999387JP_erro: 0.10247526242751484loss: 0.3403382338294687\n",
            "Epoch: 3085NA_erro: 0.7584968093753786 \n",
            "EU_erro: 0.66150332606116JP_erro: 0.047110092732333395loss: 0.48903672270072523\n",
            "Epoch: 3086NA_erro: 0.3602710620377415 \n",
            "EU_erro: 0.13228542120881784JP_erro: 0.054904273314069406loss: 0.1824869296347448\n",
            "Epoch: 3087NA_erro: 0.06621575170709182 \n",
            "EU_erro: 0.038560857606488606JP_erro: 0.0406434739282889loss: 0.04847336647122405\n",
            "Epoch: 3088NA_erro: 0.6740622557410898 \n",
            "EU_erro: 0.6142512033152026JP_erro: 0.06942005582558092loss: 0.45257786447687665\n",
            "Epoch: 3089NA_erro: 0.18121421799179196 \n",
            "EU_erro: 0.0625602173250775JP_erro: 0.00981272306553153loss: 0.08452905240909074\n",
            "Epoch: 3090NA_erro: 0.23908428443494692 \n",
            "EU_erro: 0.0873830336932988JP_erro: 0.022948989572451096loss: 0.11647211488827254\n",
            "Epoch: 3091NA_erro: 0.08114435876062674 \n",
            "EU_erro: 0.22101716477741568JP_erro: 0.05899628003438314loss: 0.12038594068482865\n",
            "Epoch: 3092NA_erro: 0.08397127306738565 \n",
            "EU_erro: 0.08834679921468098JP_erro: 0.010871529117111088loss: 0.061063207397165226\n",
            "Epoch: 3093NA_erro: 0.11931935022043627 \n",
            "EU_erro: 0.0829319528831068JP_erro: 0.026262400224227316loss: 0.07617123829301937\n",
            "Epoch: 3094NA_erro: 0.10935353678326275 \n",
            "EU_erro: 0.023169674614603206JP_erro: 0.018685954485752786loss: 0.05040305991505468\n",
            "Epoch: 3095NA_erro: 0.079731636269148 \n",
            "EU_erro: 0.02955855556236681JP_erro: 0.012523823006208553loss: 0.040604675463003705\n",
            "Epoch: 3096NA_erro: 0.5554152643957804 \n",
            "EU_erro: 0.20697061405625455JP_erro: 0.03272258727125419loss: 0.2650361652522124\n",
            "Epoch: 3097NA_erro: 0.19563754399617514 \n",
            "EU_erro: 0.11142156844915346JP_erro: 0.007835815465727518loss: 0.10496498078338859\n",
            "Epoch: 3098NA_erro: 0.19564247870630072 \n",
            "EU_erro: 0.07524120253185894JP_erro: 0.018815096727637357loss: 0.09656626124714696\n",
            "Epoch: 3099NA_erro: 0.3345158303430838 \n",
            "EU_erro: 0.22485755210699038JP_erro: 0.14073305351789608loss: 0.23336881815001023\n",
            "Epoch: 3100NA_erro: 0.1315834263498469 \n",
            "EU_erro: 0.025986070318739545JP_erro: 0.022540330424789312loss: 0.060036616731983745\n",
            "Epoch: 3101NA_erro: 0.18545185872750689 \n",
            "EU_erro: 0.03574416387912839JP_erro: 0.05900707817816919loss: 0.09340104206587917\n",
            "Epoch: 3102NA_erro: 0.11993660483249398 \n",
            "EU_erro: 0.04962447539780491JP_erro: 0.06217886311139247loss: 0.07724665394125059\n",
            "Epoch: 3103NA_erro: 0.08461923562279043 \n",
            "EU_erro: 0.050842845162679985JP_erro: 0.022733585779056993loss: 0.05273188731467077\n",
            "Epoch: 3104NA_erro: 1.4062338836433352 \n",
            "EU_erro: 0.5821527436722157JP_erro: 0.0903764713642209loss: 0.692921054455661\n",
            "Epoch: 3105NA_erro: 0.18386532169903896 \n",
            "EU_erro: 0.19748894743217055JP_erro: 0.014239771421565565loss: 0.13186467710391495\n",
            "Epoch: 3106NA_erro: 0.2879490778427716 \n",
            "EU_erro: 0.19383458204047624JP_erro: 0.04289653412131376loss: 0.17489340878272241\n",
            "Epoch: 3107NA_erro: 6.327140867248062 \n",
            "EU_erro: 3.1952738207440046JP_erro: 0.08329127186028533loss: 3.201902226884236\n",
            "Epoch: 3108NA_erro: 0.18457196479619936 \n",
            "EU_erro: 0.07935615473015364JP_erro: 0.01589406368344329loss: 0.0932740518289019\n",
            "Epoch: 3109NA_erro: 0.06923377513885498 \n",
            "EU_erro: 0.24469499440156212JP_erro: 0.06717584391897038loss: 0.12703487115312917\n",
            "Epoch: 3110NA_erro: 0.11009262513744739 \n",
            "EU_erro: 0.03123291472131892JP_erro: 0.023298221965168796loss: 0.054874591125074285\n",
            "Epoch: 3111NA_erro: 0.11528234149134436 \n",
            "EU_erro: 0.06343751452689947JP_erro: 0.023878302222998568loss: 0.06753272326417671\n",
            "Epoch: 3112NA_erro: 0.08537191753239595 \n",
            "EU_erro: 0.07492410582165386JP_erro: 0.028451662655024565loss: 0.06291589533635812\n",
            "Epoch: 3113NA_erro: 0.1772833794586418 \n",
            "EU_erro: 0.04415570765502693JP_erro: 0.00959878570811693loss: 0.07701262023097785\n",
            "Epoch: 3114NA_erro: 0.07906901743984962 \n",
            "EU_erro: 0.03596028616262037JP_erro: 0.02200023841488269loss: 0.04567651323569837\n",
            "Epoch: 3115NA_erro: 0.05155423814936202 \n",
            "EU_erro: 0.0442264107770698JP_erro: 0.018207601798597234loss: 0.037996082804923836\n",
            "Epoch: 3116NA_erro: 0.25462385295897494 \n",
            "EU_erro: 0.22888693698616916JP_erro: 0.06906538970710695loss: 0.18419206604477048\n",
            "Epoch: 3117NA_erro: 0.06353674008864765 \n",
            "EU_erro: 0.0312707220861154JP_erro: 0.011331317729728167loss: 0.03537959368654\n",
            "Epoch: 3118NA_erro: 0.11284674784933874 \n",
            "EU_erro: 0.055309372354847515JP_erro: 0.011553338100743849loss: 0.05990314853283786\n",
            "Epoch: 3119NA_erro: 0.0775354499964751 \n",
            "EU_erro: 0.034862913826639336JP_erro: 0.009781943504200426loss: 0.04072677257449128\n",
            "Epoch: 3120NA_erro: 0.06675165753031886 \n",
            "EU_erro: 0.048696237017017925JP_erro: 0.014264567415843638loss: 0.04323748655097429\n",
            "Epoch: 3121NA_erro: 0.12877285942550779 \n",
            "EU_erro: 0.19045672675435857JP_erro: 0.0607071135395257loss: 0.12664557427398918\n",
            "Epoch: 3122NA_erro: 0.09327836739000424 \n",
            "EU_erro: 0.07931168689284214JP_erro: 0.009733599054720975loss: 0.06077455919842387\n",
            "Epoch: 3123NA_erro: 0.08536862772564556 \n",
            "EU_erro: 0.027967508911162384JP_erro: 0.01084089625713437loss: 0.04139234391293784\n",
            "Epoch: 3124NA_erro: 0.7608331635940907 \n",
            "EU_erro: 0.3925661708033362JP_erro: 0.1660703318987706loss: 0.4398232097773589\n",
            "Epoch: 3125NA_erro: 0.5782831546872161 \n",
            "EU_erro: 0.11809656786364178JP_erro: 0.07156964882399684loss: 0.25598311609075974\n",
            "Epoch: 3126NA_erro: 0.13239279273868532 \n",
            "EU_erro: 0.06606878236282704JP_erro: 0.01840963266616644loss: 0.07229040759478429\n",
            "Epoch: 3127NA_erro: 0.19441726595856423 \n",
            "EU_erro: 0.0739141305287679JP_erro: 0.1083092634067979loss: 0.12554689895275029\n",
            "Epoch: 3128NA_erro: 0.07230636685393578 \n",
            "EU_erro: 0.037172381267991175JP_erro: 0.01864057409670926loss: 0.042706446130146354\n",
            "Epoch: 3129NA_erro: 0.908489375151405 \n",
            "EU_erro: 0.49819380738014396JP_erro: 0.19683932149133018loss: 0.5345075075016466\n",
            "Epoch: 3130NA_erro: 0.10808682256890823 \n",
            "EU_erro: 0.04198187081388725JP_erro: 0.025089429330456166loss: 0.05838604860527571\n",
            "Epoch: 3131NA_erro: 0.29243563496789265 \n",
            "EU_erro: 0.23439072823339654JP_erro: 0.13941035714260366loss: 0.22207892218301462\n",
            "Epoch: 3132NA_erro: 0.09166177853133327 \n",
            "EU_erro: 0.03655246985975162JP_erro: 0.010093555662983148loss: 0.04610259865605554\n",
            "Epoch: 3133NA_erro: 0.14597268991692122 \n",
            "EU_erro: 0.21765647932540538JP_erro: 0.019297270580779676loss: 0.12764214545257332\n",
            "Epoch: 3134NA_erro: 0.04699780035388562 \n",
            "EU_erro: 0.023419496624968773JP_erro: 0.01073158295579659loss: 0.027049625104711962\n",
            "Epoch: 3135NA_erro: 0.4699159592621086 \n",
            "EU_erro: 0.3903364950372267JP_erro: 0.14191339182299237loss: 0.3340552943621495\n",
            "Epoch: 3136NA_erro: 0.09344004845434381 \n",
            "EU_erro: 0.060697655345118326JP_erro: 0.016219634418339693loss: 0.05678578402644904\n",
            "Epoch: 3137NA_erro: 0.5580709886181262 \n",
            "EU_erro: 0.10488776273505632JP_erro: 0.07803507553514584loss: 0.24699793305507925\n",
            "Epoch: 3138NA_erro: 0.9154778857563817 \n",
            "EU_erro: 0.6797233108402223JP_erro: 0.21187567895697068loss: 0.602358995481979\n",
            "Epoch: 3139NA_erro: 0.16475601713786753 \n",
            "EU_erro: 0.16899262287820033JP_erro: 0.050345613050830455loss: 0.1280314330906831\n",
            "Epoch: 3140NA_erro: 0.14680211858231892 \n",
            "EU_erro: 0.04800721194392951JP_erro: 0.017047470161157062loss: 0.07061893163725387\n",
            "Epoch: 3141NA_erro: 0.19294195396955624 \n",
            "EU_erro: 0.17672529516294022JP_erro: 0.04310961841612823loss: 0.13759229534356168\n",
            "Epoch: 3142NA_erro: 0.052469603775083556 \n",
            "EU_erro: 0.0626173407532448JP_erro: 0.019939793634784313loss: 0.0450089134911234\n",
            "Epoch: 3143NA_erro: 0.11304497718811035 \n",
            "EU_erro: 0.0895017893739449JP_erro: 0.024603011534195536loss: 0.07571659808935122\n",
            "Epoch: 3144NA_erro: 0.6752882077712421 \n",
            "EU_erro: 0.27929439101108283JP_erro: 0.05411513554033383loss: 0.3362325919690982\n",
            "Epoch: 3145NA_erro: 0.5947551431581956 \n",
            "EU_erro: 0.04467568194219308JP_erro: 0.011644305646881577loss: 0.21702503973199416\n",
            "Epoch: 3146NA_erro: 0.07510515146477278 \n",
            "EU_erro: 0.08735389672508535JP_erro: 0.007676671064177225loss: 0.05671191123104835\n",
            "Epoch: 3147NA_erro: 0.23085257803746898 \n",
            "EU_erro: 0.17089492590852487JP_erro: 0.05618437778118045loss: 0.15264396519623985\n",
            "Epoch: 3148NA_erro: 0.0662162091380866 \n",
            "EU_erro: 0.0256459819254025JP_erro: 0.016897072394688923loss: 0.03625309051469315\n",
            "Epoch: 3149NA_erro: 0.26817454848178596 \n",
            "EU_erro: 0.12090932491213777JP_erro: 0.05144774913787842loss: 0.1468438787977825\n",
            "Epoch: 3150NA_erro: 0.3393892909205237 \n",
            "EU_erro: 0.19295324650845785JP_erro: 0.0504339526790057loss: 0.19425881925479385\n",
            "Epoch: 3151NA_erro: 0.21670426509177038 \n",
            "EU_erro: 0.025747598603714342JP_erro: 0.025377797988034034loss: 0.0892765577449355\n",
            "Epoch: 3152NA_erro: 5.781551035799722 \n",
            "EU_erro: 2.801811454832092JP_erro: 0.057284531667251loss: 2.880216199298238\n",
            "Epoch: 3153NA_erro: 0.17904941425767057 \n",
            "EU_erro: 0.06614087625991466JP_erro: 0.009323058202285175loss: 0.08483778598696687\n",
            "Epoch: 3154NA_erro: 0.0808801761893339 \n",
            "EU_erro: 0.060997138651766515JP_erro: 0.011111533688020337loss: 0.050996283228083174\n",
            "Epoch: 3155NA_erro: 0.22308013235875804 \n",
            "EU_erro: 0.040279839390008024JP_erro: 0.009749872508899186loss: 0.0910366228384565\n",
            "Epoch: 3156NA_erro: 0.042548544647157654 \n",
            "EU_erro: 0.12431043063023293JP_erro: 0.09371576383132343loss: 0.08685825407042984\n",
            "Epoch: 3157NA_erro: 0.14330201370771542 \n",
            "EU_erro: 0.03440720628398333JP_erro: 0.09419277656910031loss: 0.09063400039377138\n",
            "Epoch: 3158NA_erro: 0.28301717699036116 \n",
            "EU_erro: 0.1953586127406867JP_erro: 0.07917505826136863loss: 0.18585029498551242\n",
            "Epoch: 3159NA_erro: 0.5162163667900618 \n",
            "EU_erro: 0.20818640095318935JP_erro: 0.03338668235512667loss: 0.2525965003080146\n",
            "Epoch: 3160NA_erro: 0.7437148574710817 \n",
            "EU_erro: 0.46112644579983497JP_erro: 0.046349454295727635loss: 0.41706362435984057\n",
            "Epoch: 3161NA_erro: 0.05049669465353323 \n",
            "EU_erro: 0.03743387932001158JP_erro: 0.01714611815851788loss: 0.03502556750940722\n",
            "Epoch: 3162NA_erro: 0.24707877358724906 \n",
            "EU_erro: 0.2593925572180933JP_erro: 0.06820478642633719loss: 0.19155868264131767\n",
            "Epoch: 3163NA_erro: 0.13109967690105587 \n",
            "EU_erro: 0.024501387925111046JP_erro: 0.006463874795640162loss: 0.054021648658338445\n",
            "Epoch: 3164NA_erro: 1.2896703380022863 \n",
            "EU_erro: 0.5640922960384872JP_erro: 0.11209287384683772loss: 0.6552851477334666\n",
            "Epoch: 3165NA_erro: 0.06832853306171506 \n",
            "EU_erro: 0.022718319597170335JP_erro: 0.0187607721764912loss: 0.0366025446921356\n",
            "Epoch: 3166NA_erro: 0.1141414346621018 \n",
            "EU_erro: 0.18700333528740462JP_erro: 0.012085289918175039loss: 0.10441001995589382\n",
            "Epoch: 3167NA_erro: 5.821225070214087 \n",
            "EU_erro: 2.915761489276738JP_erro: 0.07444386334382286loss: 2.937143311020016\n",
            "Epoch: 3168NA_erro: 5.775912380957788 \n",
            "EU_erro: 2.8412574945494184JP_erro: 0.04328921321750611loss: 2.8868191741233646\n",
            "Epoch: 3169NA_erro: 0.04990619744441306 \n",
            "EU_erro: 0.0758643436801526JP_erro: 0.011972268653470416loss: 0.0459142710811408\n",
            "Epoch: 3170NA_erro: 0.19531883934671565 \n",
            "EU_erro: 0.17857420352078224JP_erro: 0.048967872479165245loss: 0.1409536553907764\n",
            "Epoch: 3171NA_erro: 0.07136582866195561 \n",
            "EU_erro: 0.0381453323733899JP_erro: 0.01186809452005135loss: 0.04045975300692772\n",
            "Epoch: 3172NA_erro: 5.7323254725729775 \n",
            "EU_erro: 2.8253123556920725JP_erro: 0.12956684874009716loss: 2.8957346243451734\n",
            "Epoch: 3173NA_erro: 0.4857219651687977 \n",
            "EU_erro: 0.2051319262778112JP_erro: 0.0256927068843398loss: 0.23884887843168984\n",
            "Epoch: 3174NA_erro: 0.2456962415414263 \n",
            "EU_erro: 0.030440517174181086JP_erro: 0.006595163266788158loss: 0.09424397187639577\n",
            "Epoch: 3175NA_erro: 0.06389199763305428 \n",
            "EU_erro: 0.03143837054570516JP_erro: 0.01389258245165034loss: 0.03640764905500782\n",
            "Epoch: 3176NA_erro: 0.05969552568687025 \n",
            "EU_erro: 0.030336705751197283JP_erro: 0.01815754544827365loss: 0.036063256651856175\n",
            "Epoch: 3177NA_erro: 0.17475370288819306 \n",
            "EU_erro: 0.03918656545091969JP_erro: 0.007962958757267442loss: 0.07396774236546007\n",
            "Epoch: 3178NA_erro: 5.733209062916364 \n",
            "EU_erro: 2.7842336846876514JP_erro: 0.0504298866257187loss: 2.8559573861055596\n",
            "Epoch: 3179NA_erro: 0.6916079410286837 \n",
            "EU_erro: 0.4412332061649293JP_erro: 0.04798605922580689loss: 0.3936090765073318\n",
            "Epoch: 3180NA_erro: 0.3580257504485374 \n",
            "EU_erro: 0.3031228494274524JP_erro: 0.05732988664346148loss: 0.2394928488620492\n",
            "Epoch: 3181NA_erro: 5.737758902616279 \n",
            "EU_erro: 2.7806928176288457JP_erro: 0.04763575949410136loss: 2.8553624116173086\n",
            "Epoch: 3182NA_erro: 0.8868709091068239 \n",
            "EU_erro: 0.44920222703800644JP_erro: 0.06106042584707571loss: 0.46571121659389764\n",
            "Epoch: 3183NA_erro: 0.08992128593977107 \n",
            "EU_erro: 0.21989347398743148JP_erro: 0.05887386410735374loss: 0.12289621109186216\n",
            "Epoch: 3184NA_erro: 0.0844292585239854 \n",
            "EU_erro: 0.057272047035453855JP_erro: 0.017073424749596174loss: 0.05292491395344106\n",
            "Epoch: 3185NA_erro: 0.7500802269277647 \n",
            "EU_erro: 0.10431035544521125JP_erro: 0.0109323755253193loss: 0.28844097788019696\n",
            "Epoch: 3186NA_erro: 0.31345278717750724 \n",
            "EU_erro: 0.24635693823644358JP_erro: 0.23800461791282476loss: 0.2659381082815717\n",
            "Epoch: 3187NA_erro: 0.09468835453654445 \n",
            "EU_erro: 0.03555570230927578JP_erro: 0.009560474129610284loss: 0.046601512635401056\n",
            "Epoch: 3188NA_erro: 0.1522954305013021 \n",
            "EU_erro: 0.06117252878440443JP_erro: 0.01331225730652033loss: 0.07559341053630031\n",
            "Epoch: 3189NA_erro: 0.0909248156140941 \n",
            "EU_erro: 0.023605562457742618JP_erro: 0.11127302812975506loss: 0.07526780745779821\n",
            "Epoch: 3190NA_erro: 0.04378447698992352 \n",
            "EU_erro: 0.026126570941865906JP_erro: 0.12469155843867812loss: 0.06486753160639326\n",
            "Epoch: 3191NA_erro: 0.054223352624464406 \n",
            "EU_erro: 0.031032772489296374JP_erro: 0.009717533523722212loss: 0.03165788890779481\n",
            "Epoch: 3192NA_erro: 0.5706305836522302 \n",
            "EU_erro: 0.15881532846495164JP_erro: 0.06583781667457994loss: 0.26509456856306207\n",
            "Epoch: 3193NA_erro: 0.21024378695229226 \n",
            "EU_erro: 0.03797428321468738JP_erro: 0.013169956992762957loss: 0.0871293489323106\n",
            "Epoch: 3194NA_erro: 0.08133343023847239 \n",
            "EU_erro: 0.03780474496442218JP_erro: 0.010217773590901102loss: 0.04311864690263142\n",
            "Epoch: 3195NA_erro: 0.13474877490553744 \n",
            "EU_erro: 0.20462616469508918JP_erro: 0.01011019875836927loss: 0.11649504188419313\n",
            "Epoch: 3196NA_erro: 0.2327895903772162 \n",
            "EU_erro: 0.17179169396097346JP_erro: 0.04362897817478623loss: 0.14940343161886052\n",
            "Epoch: 3197NA_erro: 0.12818348500155663 \n",
            "EU_erro: 0.028435368408528408JP_erro: 0.011310627063115438loss: 0.055976496186367304\n",
            "Epoch: 3198NA_erro: 6.009229911390201 \n",
            "EU_erro: 2.9649347113084423JP_erro: 0.18101605334023171loss: 3.051727058351502\n",
            "Epoch: 3199NA_erro: 0.08262566817823307 \n",
            "EU_erro: 0.02485876628594805JP_erro: 0.01190520303193913loss: 0.039796547372211784\n",
            "Epoch: 3200NA_erro: 0.6305514195168666 \n",
            "EU_erro: 0.10397090468295785JP_erro: 0.017036960799564686loss: 0.25051976359167766\n",
            "Epoch: 3201NA_erro: 0.07301177627356477 \n",
            "EU_erro: 0.024887147799942843JP_erro: 0.008599435867265214loss: 0.03549945215846217\n",
            "Epoch: 3202NA_erro: 0.041878167973008265 \n",
            "EU_erro: 0.02447019713793614JP_erro: 0.0347290551939676loss: 0.03369247497514237\n",
            "Epoch: 3203NA_erro: 0.09940779486367869 \n",
            "EU_erro: 0.038055271603340324JP_erro: 0.01666321310886117loss: 0.05137542883555094\n",
            "Epoch: 3204NA_erro: 0.06467855715936469 \n",
            "EU_erro: 0.03667731386746547JP_erro: 0.016702360423036324loss: 0.03935274227645046\n",
            "Epoch: 3205NA_erro: 0.07118495859841044 \n",
            "EU_erro: 0.027716067410254664JP_erro: 0.014346313568972802loss: 0.037749113962631815\n",
            "Epoch: 3206NA_erro: 0.06703771824060484 \n",
            "EU_erro: 0.028159796267516853JP_erro: 0.015067465083543645loss: 0.03675499396730763\n",
            "Epoch: 3207NA_erro: 0.07322107636651327 \n",
            "EU_erro: 0.018057919056840645JP_erro: 0.012453103712362837loss: 0.03457736368327178\n",
            "Epoch: 3208NA_erro: 0.07652359877445901 \n",
            "EU_erro: 0.03243498561918273JP_erro: 0.016802488140357556loss: 0.04192036251689112\n",
            "Epoch: 3209NA_erro: 0.10062351707340211 \n",
            "EU_erro: 0.03130884826645371JP_erro: 0.008342843069586643loss: 0.0467584022255831\n",
            "Epoch: 3210NA_erro: 0.1628700337668722 \n",
            "EU_erro: 0.06667744281680085JP_erro: 0.012248166540796442loss: 0.08059854655302773\n",
            "Epoch: 3211NA_erro: 0.1515201912369839 \n",
            "EU_erro: 0.037840999836145445JP_erro: 0.009061939032502877loss: 0.06614104721897332\n",
            "Epoch: 3212NA_erro: 0.277412214944529 \n",
            "EU_erro: 0.23172458012898764JP_erro: 0.052748063738031904loss: 0.18729496371838472\n",
            "Epoch: 3213NA_erro: 0.5101777971252914 \n",
            "EU_erro: 0.23505051930745444JP_erro: 0.030474563439687092loss: 0.25856764741646227\n",
            "Epoch: 3214NA_erro: 0.7046043041140534 \n",
            "EU_erro: 0.47462271165478137JP_erro: 0.04452544589375341loss: 0.4079175180242967\n",
            "Epoch: 3215NA_erro: 0.227609678756359 \n",
            "EU_erro: 0.05130204581475073JP_erro: 0.01112253051395564loss: 0.09667808695356975\n",
            "Epoch: 3216NA_erro: 0.26843623597492544 \n",
            "EU_erro: 0.3075648278228996JP_erro: 0.04172653190849363loss: 0.2059092078098031\n",
            "Epoch: 3217NA_erro: 0.14267942702123362 \n",
            "EU_erro: 0.03610333731008131JP_erro: 0.014780071354651637loss: 0.06452095138934232\n",
            "Epoch: 3218NA_erro: 0.14025377672772074 \n",
            "EU_erro: 0.06603987642036852JP_erro: 0.11604123337324275loss: 0.10744496833446414\n",
            "Epoch: 3219NA_erro: 0.30170438825622087 \n",
            "EU_erro: 0.20833008049070373JP_erro: 0.02266468465790268loss: 0.17756637676741727\n",
            "Epoch: 3220NA_erro: 0.10810513829076013 \n",
            "EU_erro: 0.08353923642358115JP_erro: 0.02332082552503246loss: 0.07165506828662961\n",
            "Epoch: 3221NA_erro: 0.05853158558985984 \n",
            "EU_erro: 0.04024967204692752JP_erro: 0.013441978267921035loss: 0.03740774568661239\n",
            "Epoch: 3222NA_erro: 0.6794741726660913 \n",
            "EU_erro: 0.4818959199180899JP_erro: 0.04547604756761891loss: 0.4022820790608724\n",
            "Epoch: 3223NA_erro: 0.18108212670614554 \n",
            "EU_erro: 0.18814593322517337JP_erro: 0.02864176681799482loss: 0.13262328251387723\n",
            "Epoch: 3224NA_erro: 0.08403869562370833 \n",
            "EU_erro: 0.06790875926498295JP_erro: 0.011639331662377645loss: 0.05452893039052801\n",
            "Epoch: 3225NA_erro: 0.0866275055463924 \n",
            "EU_erro: 0.03804070773974869JP_erro: 0.01151235297668812loss: 0.04539352054743804\n",
            "Epoch: 3226NA_erro: 0.26586222094158796 \n",
            "EU_erro: 0.030999264513799387JP_erro: 0.011154561079749766loss: 0.10267202244248501\n",
            "Epoch: 3227NA_erro: 0.6567547672478727 \n",
            "EU_erro: 0.32904632331789JP_erro: 0.034175164015718205loss: 0.3399921018023824\n",
            "Epoch: 3228NA_erro: 0.13386110002680343 \n",
            "EU_erro: 0.21873183952745542JP_erro: 0.073834546776705loss: 0.14214249544365462\n",
            "Epoch: 3229NA_erro: 0.08391776750254076 \n",
            "EU_erro: 0.19487467847129172JP_erro: 0.01950876426327136loss: 0.09943373443544373\n",
            "Epoch: 3230NA_erro: 0.9548318108846975 \n",
            "EU_erro: 0.4347410867380541JP_erro: 0.08083884106125942loss: 0.49013725546903386\n",
            "Epoch: 3231NA_erro: 0.10389659755913787 \n",
            "EU_erro: 0.2201043358144834JP_erro: 0.07463690384413844loss: 0.13287929601447526\n",
            "Epoch: 3232NA_erro: 0.6010796672614046 \n",
            "EU_erro: 0.1041061564009319JP_erro: 0.11254487111586933loss: 0.2725769198218057\n",
            "Epoch: 3233NA_erro: 0.14839107676069865 \n",
            "EU_erro: 0.032599161761675696JP_erro: 0.01563590510871059loss: 0.06554205288258634\n",
            "Epoch: 3234NA_erro: 0.17365150673444882 \n",
            "EU_erro: 0.23267468740773756JP_erro: 0.10922649110010428loss: 0.17185087351835976\n",
            "Epoch: 3235NA_erro: 0.125955158425856 \n",
            "EU_erro: 0.0610898215641347JP_erro: 0.019200861222984256loss: 0.06874862105347389\n",
            "Epoch: 3236NA_erro: 0.08640050888061523 \n",
            "EU_erro: 0.27658176052477934JP_erro: 0.07553489633308824loss: 0.14617240706155465\n",
            "Epoch: 3237NA_erro: 0.06752806578495706 \n",
            "EU_erro: 0.029193788997886716JP_erro: 0.0268301760503488loss: 0.041184014128160105\n",
            "Epoch: 3238NA_erro: 0.1272839538810789 \n",
            "EU_erro: 0.04514979299648788JP_erro: 0.01947078362915867loss: 0.0639681806860044\n",
            "Epoch: 3239NA_erro: 0.6481017622836801 \n",
            "EU_erro: 0.3166260460550471JP_erro: 0.042414318683535554loss: 0.33571402231852215\n",
            "Epoch: 3240NA_erro: 0.6989051020422647 \n",
            "EU_erro: 0.4617849985758464JP_erro: 0.04895822022312371loss: 0.40321612542913865\n",
            "Epoch: 3241NA_erro: 0.10496356690576834 \n",
            "EU_erro: 0.02465657254522161JP_erro: 0.00783972673175871loss: 0.045819957126942715\n",
            "Epoch: 3242NA_erro: 0.2218957649644955 \n",
            "EU_erro: 0.035125950510187666JP_erro: 0.011453553456668706loss: 0.08949177209721056\n",
            "Epoch: 3243NA_erro: 0.17915749734686326 \n",
            "EU_erro: 0.014417870331180188JP_erro: 0.01902078704316487loss: 0.07086538991262746\n",
            "Epoch: 3244NA_erro: 0.15441194061161012 \n",
            "EU_erro: 0.048959227495415265JP_erro: 0.016860267681668897loss: 0.07341048052144605\n",
            "Epoch: 3245NA_erro: 0.3228519129198651 \n",
            "EU_erro: 0.25724666063175644JP_erro: 0.145715641420941loss: 0.24193808089855107\n",
            "Epoch: 3246NA_erro: 0.1644043959388437 \n",
            "EU_erro: 0.03290275501650433JP_erro: 0.008984052742174429loss: 0.06876373475836228\n",
            "Epoch: 3247NA_erro: 0.0757023114566655 \n",
            "EU_erro: 0.05078943662865217JP_erro: 0.10802543440530467loss: 0.07817240648491439\n",
            "Epoch: 3248NA_erro: 5.729502596596415 \n",
            "EU_erro: 2.7837682324786517JP_erro: 0.12668091197346532loss: 2.8799837689067043\n",
            "Epoch: 3249NA_erro: 0.13841247373773147 \n",
            "EU_erro: 0.04884583081385886JP_erro: 0.01936354031858518loss: 0.06887395252553068\n",
            "Epoch: 3250NA_erro: 0.5297860064247782 \n",
            "EU_erro: 0.16930014588112055JP_erro: 0.06689146507618039loss: 0.25532585705897604\n",
            "Epoch: 3251NA_erro: 0.5077927981236184 \n",
            "EU_erro: 0.2136193504629209JP_erro: 0.03899335399154545loss: 0.2534685023995333\n",
            "Epoch: 3252NA_erro: 0.12074854946875757 \n",
            "EU_erro: 0.02666788507801618JP_erro: 0.07990810298180395loss: 0.07577484892320263\n",
            "Epoch: 3253NA_erro: 0.10755294977232467 \n",
            "EU_erro: 0.019343498834343845JP_erro: 0.040013873299887016loss: 0.05563678205475327\n",
            "Epoch: 3254NA_erro: 0.1273835074993991 \n",
            "EU_erro: 0.04752737145091212JP_erro: 0.025969413361808125loss: 0.06696009820745896\n",
            "Epoch: 3255NA_erro: 0.7956189887468205 \n",
            "EU_erro: 0.049019363499427024JP_erro: 0.007032881411471108loss: 0.2838904173799263\n",
            "Epoch: 3256NA_erro: 0.0844247489012489 \n",
            "EU_erro: 0.2012348544690036JP_erro: 0.07543910843457362loss: 0.12036622956741688\n",
            "Epoch: 3257NA_erro: 5.8275787590086 \n",
            "EU_erro: 2.9212185763573464JP_erro: 0.0741985093715579loss: 2.9409985209620277\n",
            "Epoch: 3258NA_erro: 0.49346288045247394 \n",
            "EU_erro: 0.19688168237375658JP_erro: 0.03255654213040374loss: 0.24096707040949386\n",
            "Epoch: 3259NA_erro: 0.5439808202344317 \n",
            "EU_erro: 0.11158289835434551JP_erro: 0.07493542608364608loss: 0.2434996856275455\n",
            "Epoch: 3260NA_erro: 0.4009335540061773 \n",
            "EU_erro: 0.35824720249619596JP_erro: 0.09970777718595757loss: 0.28629619021748387\n",
            "Epoch: 3261NA_erro: 0.09717274081799411 \n",
            "EU_erro: 0.0479346144106961JP_erro: 0.01684247985366703loss: 0.05398327990095745\n",
            "Epoch: 3262NA_erro: 0.24875635324522508 \n",
            "EU_erro: 0.33914403397907583JP_erro: 0.09058870086374209loss: 0.22616306940714517\n",
            "Epoch: 3263NA_erro: 0.054005148798920384 \n",
            "EU_erro: 0.021684525548949722JP_erro: 0.012133776448493781loss: 0.029274486294088436\n",
            "Epoch: 3264NA_erro: 0.06917182327240937 \n",
            "EU_erro: 0.032767281513805535JP_erro: 0.013997372268706329loss: 0.03864549389181211\n",
            "Epoch: 3265NA_erro: 0.6208448631818905 \n",
            "EU_erro: 0.09435782136843186JP_erro: 0.006759551952975665loss: 0.24065407671669656\n",
            "Epoch: 3266NA_erro: 0.5489137191181035 \n",
            "EU_erro: 0.3119243577469227JP_erro: 0.13839251311250436loss: 0.3330768910489341\n",
            "Epoch: 3267NA_erro: 0.6632716895997987 \n",
            "EU_erro: 0.3110437614973201JP_erro: 0.03687006327532982loss: 0.3370618450549222\n",
            "Epoch: 3268NA_erro: 0.06218318791352501 \n",
            "EU_erro: 0.07664896259012148JP_erro: 0.01911325625670973loss: 0.052648468535075814\n",
            "Epoch: 3269NA_erro: 1.1470362936803538 \n",
            "EU_erro: 0.39745449095733404JP_erro: 0.09816915489906489loss: 0.5475533285806345\n",
            "Epoch: 3270NA_erro: 0.11528624120608781 \n",
            "EU_erro: 0.025879280973774517JP_erro: 0.01736060246940731loss: 0.05284204039462777\n",
            "Epoch: 3271NA_erro: 0.1521068672801173 \n",
            "EU_erro: 0.19762552985849308JP_erro: 0.01157493785370228loss: 0.12043578680171522\n",
            "Epoch: 3272NA_erro: 0.07317625737005426 \n",
            "EU_erro: 0.24623024371243263JP_erro: 0.11733704759168995loss: 0.1455811751905338\n",
            "Epoch: 3273NA_erro: 0.105035101720529 \n",
            "EU_erro: 0.03092430127683536JP_erro: 0.04114794176678325loss: 0.059035785438478455\n",
            "Epoch: 3274NA_erro: 0.5205954692160436 \n",
            "EU_erro: 0.2154635458953621JP_erro: 0.03695628439733224loss: 0.2576717665029127\n",
            "Epoch: 3275NA_erro: 0.10161677996317546 \n",
            "EU_erro: 0.20919626073319783JP_erro: 0.1053003470102946loss: 0.1387044718099195\n",
            "Epoch: 3276NA_erro: 0.08291285167368807 \n",
            "EU_erro: 0.2132089008656583JP_erro: 0.06382820218108422loss: 0.11998333672220393\n",
            "Epoch: 3277NA_erro: 0.38592885630999424 \n",
            "EU_erro: 0.28481806895529577JP_erro: 0.09850379108458526loss: 0.25641692701236224\n",
            "Epoch: 3278NA_erro: 0.03948788772257723 \n",
            "EU_erro: 0.03672957651374876JP_erro: 0.023750161477761676loss: 0.03332254036452419\n",
            "Epoch: 3279NA_erro: 0.7417978981668635 \n",
            "EU_erro: 0.2063666203225306JP_erro: 0.13514277546904807loss: 0.3611024590425713\n",
            "Epoch: 3280NA_erro: 0.24871402932691944 \n",
            "EU_erro: 0.23544261621874432JP_erro: 0.03257132777872011loss: 0.1722426747166833\n",
            "Epoch: 3281NA_erro: 0.08667377538459245 \n",
            "EU_erro: 0.027339215888533483JP_erro: 0.009966024825739305loss: 0.041326339854750524\n",
            "Epoch: 3282NA_erro: 0.20006588263105052 \n",
            "EU_erro: 0.06998074609179829JP_erro: 0.09063522020975749loss: 0.12022728143736373\n",
            "Epoch: 3283NA_erro: 0.16330362290374992 \n",
            "EU_erro: 0.18127703851507615JP_erro: 0.008755892861721128loss: 0.11777885200441346\n",
            "Epoch: 3284NA_erro: 0.07825950319452803 \n",
            "EU_erro: 0.033921213575111804JP_erro: 0.020730604034985684loss: 0.04430377668188524\n",
            "Epoch: 3285NA_erro: 0.12454912644024044 \n",
            "EU_erro: 0.04443559073662573JP_erro: 0.016738801270492317loss: 0.0619078421777533\n",
            "Epoch: 3286NA_erro: 0.14378077300019967 \n",
            "EU_erro: 0.03379070250562919JP_erro: 0.010335251342418581loss: 0.06263557792634003\n",
            "Epoch: 3287NA_erro: 0.594059848046118 \n",
            "EU_erro: 0.11097555936768998JP_erro: 0.06814167943111686loss: 0.25772569715514665\n",
            "Epoch: 3288NA_erro: 0.11427531870760659 \n",
            "EU_erro: 0.07981537848479989JP_erro: 0.10784478150596914loss: 0.1006451688071554\n",
            "Epoch: 3289NA_erro: 0.06967209106267885 \n",
            "EU_erro: 0.06674794725669447JP_erro: 0.0979700014572735loss: 0.07813001787939737\n",
            "Epoch: 3290NA_erro: 0.22195159926895022 \n",
            "EU_erro: 0.02598311549933382JP_erro: 0.012847573258156loss: 0.08692743242249008\n",
            "Epoch: 3291NA_erro: 0.17176020053005958 \n",
            "EU_erro: 0.13083588245303132JP_erro: 0.08945918822473334loss: 0.1306850873222647\n",
            "Epoch: 3292NA_erro: 0.18728200779404752 \n",
            "EU_erro: 0.060685207677441975JP_erro: 0.018151800992876985loss: 0.08870633997658427\n",
            "Epoch: 3293NA_erro: 0.073391878327658 \n",
            "EU_erro: 0.030098543610683707JP_erro: 0.027927141781001127loss: 0.04380585611328598\n",
            "Epoch: 3294NA_erro: 5.794542889262354 \n",
            "EU_erro: 2.7815335295921146JP_erro: 0.04440387090047201loss: 2.8734937564347143\n",
            "Epoch: 3295NA_erro: 0.054167227227558463 \n",
            "EU_erro: 0.044248353603274324JP_erro: 0.02539021100184714loss: 0.041268601898075075\n",
            "Epoch: 3296NA_erro: 0.6625396521516549 \n",
            "EU_erro: 0.48066837843074356JP_erro: 0.04647329796192258loss: 0.3965604767318844\n",
            "Epoch: 3297NA_erro: 0.2091972587644592 \n",
            "EU_erro: 0.061269240785938824JP_erro: 0.034910652988640836loss: 0.1017923965010532\n",
            "Epoch: 3298NA_erro: 0.0715914160706276 \n",
            "EU_erro: 0.03716525874396627JP_erro: 0.09242733319600423loss: 0.06706133831379026\n",
            "Epoch: 3299NA_erro: 0.09038907612941062 \n",
            "EU_erro: 0.07887592611386794JP_erro: 0.0178389124167982loss: 0.06236797155335892\n",
            "Epoch: 3300NA_erro: 0.22548254146132357 \n",
            "EU_erro: 0.1436223170553991JP_erro: 0.04130899444106938loss: 0.13680462689362755\n",
            "Epoch: 3301NA_erro: 0.060349836830020875 \n",
            "EU_erro: 0.023162166277567547JP_erro: 0.01613039263459139loss: 0.033214129218759464\n",
            "Epoch: 3302NA_erro: 0.5134308615396189 \n",
            "EU_erro: 0.21141597466875417JP_erro: 0.03268417461897976loss: 0.25251033694245095\n",
            "Epoch: 3303NA_erro: 0.6578762586726699 \n",
            "EU_erro: 0.3210658805314885JP_erro: 0.04732643449029257loss: 0.3420894829801811\n",
            "Epoch: 3304NA_erro: 0.2026347042054169 \n",
            "EU_erro: 0.037084993465926294JP_erro: 0.009678806676421054loss: 0.08313284363857536\n",
            "Epoch: 3305NA_erro: 0.12883952421735423 \n",
            "EU_erro: 0.25869027588718624JP_erro: 0.19933828087740166loss: 0.19562268441961717\n",
            "Epoch: 3306NA_erro: 0.08427000785058783 \n",
            "EU_erro: 0.0232517118601836JP_erro: 0.024162563242653545loss: 0.04389476406481839\n",
            "Epoch: 3307NA_erro: 0.2367327749267105 \n",
            "EU_erro: 0.08519414783448212JP_erro: 0.020057736903198006loss: 0.11399489964625632\n",
            "Epoch: 3308NA_erro: 0.2194004835084427 \n",
            "EU_erro: 0.15175134636635004JP_erro: 0.21157713823540267loss: 0.19424299861109534\n",
            "Epoch: 3309NA_erro: 0.10861516922943351 \n",
            "EU_erro: 0.2250295461610306JP_erro: 0.06508888662323471loss: 0.13291119605071786\n",
            "Epoch: 3310NA_erro: 0.06202784157538599 \n",
            "EU_erro: 0.03184467092040898JP_erro: 0.09343763654546221loss: 0.06243672019751497\n",
            "Epoch: 3311NA_erro: 0.08791092754334442 \n",
            "EU_erro: 0.028411632822465525JP_erro: 0.019295505544011906loss: 0.04520602928575619\n",
            "Epoch: 3312NA_erro: 0.6726070522337921 \n",
            "EU_erro: 0.321634943171065JP_erro: 0.03506489733392878loss: 0.3431023368539736\n",
            "Epoch: 3313NA_erro: 0.35911367845165637 \n",
            "EU_erro: 0.14349478159764018JP_erro: 0.05635261073593022loss: 0.1863203677096108\n",
            "Epoch: 3314NA_erro: 0.0876364227413207 \n",
            "EU_erro: 0.02365206332169762JP_erro: 0.09297307147536167loss: 0.06808718507604082\n",
            "Epoch: 3315NA_erro: 0.6001612567162329 \n",
            "EU_erro: 0.26574924010639045JP_erro: 0.03145047860552174loss: 0.29912030049996785\n",
            "Epoch: 3316NA_erro: 0.550861210786095 \n",
            "EU_erro: 0.2135516137115715JP_erro: 0.03423974033473998loss: 0.26621753855269087\n",
            "Epoch: 3317NA_erro: 0.7489175574724064 \n",
            "EU_erro: 0.3310794238896333JP_erro: 0.03652640784433646loss: 0.3721744330354439\n",
            "Epoch: 3318NA_erro: 0.8518202729927477 \n",
            "EU_erro: 0.4197786759960559JP_erro: 0.08205479429673779loss: 0.45121795447297797\n",
            "Epoch: 3319NA_erro: 0.4585171485132025 \n",
            "EU_erro: 0.21125569824100465JP_erro: 0.15214993048083875loss: 0.27397425599800523\n",
            "Epoch: 3320NA_erro: 0.06068560966225558 \n",
            "EU_erro: 0.04145529381064481JP_erro: 0.010565284841744474loss: 0.037568726742914484\n",
            "Epoch: 3321NA_erro: 0.051122386326161465 \n",
            "EU_erro: 0.03230602011200069JP_erro: 0.012400347826092742loss: 0.03194291924321374\n",
            "Epoch: 3322NA_erro: 0.1318954959396244 \n",
            "EU_erro: 0.05242075568945833JP_erro: 0.0359536187593327loss: 0.07342328396878502\n",
            "Epoch: 3323NA_erro: 0.4817530535912329 \n",
            "EU_erro: 0.22978699484536813JP_erro: 0.08037964502970378loss: 0.26397326195886894\n",
            "Epoch: 3324NA_erro: 0.1834196452946626 \n",
            "EU_erro: 0.03508746855018675JP_erro: 0.012591250183046326loss: 0.0770327795383542\n",
            "Epoch: 3325NA_erro: 0.04516564136327699 \n",
            "EU_erro: 0.02536150143128033JP_erro: 0.00957774561504985loss: 0.02670162869978321\n",
            "Epoch: 3326NA_erro: 0.047824137894682185 \n",
            "EU_erro: 0.09439911953238554JP_erro: 0.01797885164734005loss: 0.05340070225471674\n",
            "Epoch: 3327NA_erro: 0.5462033988893494 \n",
            "EU_erro: 0.14129046321839325JP_erro: 0.0755893722061039loss: 0.2543611119883929\n",
            "Epoch: 3328NA_erro: 0.23328553798586824 \n",
            "EU_erro: 0.04526769005975058JP_erro: 0.00904648456462594loss: 0.09586657664572545\n",
            "Epoch: 3329NA_erro: 0.7730001257371533 \n",
            "EU_erro: 0.5037542091783627JP_erro: 0.09272683498471282loss: 0.4564937325411065\n",
            "Epoch: 3330NA_erro: 0.5534277775490931 \n",
            "EU_erro: 0.21889984145645022JP_erro: 0.0279643923737282loss: 0.2667639976324037\n",
            "Epoch: 3331NA_erro: 0.18025839975637983 \n",
            "EU_erro: 0.07533930530843809JP_erro: 0.01131584131440451loss: 0.08897119714308155\n",
            "Epoch: 3332NA_erro: 0.5280682098033816 \n",
            "EU_erro: 0.11214137077331543JP_erro: 0.08874018987019856loss: 0.24298325989597527\n",
            "Epoch: 3333NA_erro: 0.11272670686707016 \n",
            "EU_erro: 0.03727535637774209JP_erro: 0.013563607552254846loss: 0.054521893346032434\n",
            "Epoch: 3334NA_erro: 0.2950454497522162 \n",
            "EU_erro: 0.1970681109169657JP_erro: 0.10897746381833572loss: 0.20036368407020272\n",
            "Epoch: 3335NA_erro: 0.12470944907314094 \n",
            "EU_erro: 0.06721680016480674JP_erro: 0.0617254843083463loss: 0.08455058400945147\n",
            "Epoch: 3336NA_erro: 0.06721627342608548 \n",
            "EU_erro: 0.04077305165372153JP_erro: 0.022467566553012346loss: 0.04348562669384387\n",
            "Epoch: 3337NA_erro: 0.1303949189740558 \n",
            "EU_erro: 0.2091882025548654JP_erro: 0.03258541341899902loss: 0.12405618216640266\n",
            "Epoch: 3338NA_erro: 0.22311364033425501 \n",
            "EU_erro: 0.07547575373982274JP_erro: 0.04391374052032944loss: 0.11416770691095396\n",
            "Epoch: 3339NA_erro: 0.10334440904070241 \n",
            "EU_erro: 0.037960186023120734JP_erro: 0.013784820488257002loss: 0.05169647608616555\n",
            "Epoch: 3340NA_erro: 0.22433572961378467 \n",
            "EU_erro: 0.17437178959218105JP_erro: 0.03170680399088897loss: 0.1434714387553607\n",
            "Epoch: 3341NA_erro: 0.09223809537961501 \n",
            "EU_erro: 0.039839518162631246JP_erro: 0.00852535110573436loss: 0.04686765892561092\n",
            "Epoch: 3342NA_erro: 0.20848558854687121 \n",
            "EU_erro: 0.17977660940599072JP_erro: 0.02312416023062181loss: 0.1371287929919339\n",
            "Epoch: 3343NA_erro: 0.275031984314438 \n",
            "EU_erro: 0.17590021902276565JP_erro: 0.05161369955816934loss: 0.16751531482666962\n",
            "Epoch: 3344NA_erro: 5.740685869556989 \n",
            "EU_erro: 2.779705432034278JP_erro: 0.10158072146334389loss: 2.873990849931111\n",
            "Epoch: 3345NA_erro: 0.04789334859034812 \n",
            "EU_erro: 0.056434407714725465JP_erro: 0.03478113070938938loss: 0.04636962746464929\n",
            "Epoch: 3346NA_erro: 0.681978048280228 \n",
            "EU_erro: 0.23926651755044626JP_erro: 0.027225664419721265loss: 0.3161567495774853\n",
            "Epoch: 3347NA_erro: 0.06489894186803537 \n",
            "EU_erro: 0.03564997005832288JP_erro: 0.024045430874639704loss: 0.04153144914050435\n",
            "Epoch: 3348NA_erro: 0.09513549102369205 \n",
            "EU_erro: 0.031182204568108846JP_erro: 0.04972211612287418loss: 0.05867994570916937\n",
            "Epoch: 3349NA_erro: 0.7913909586825112 \n",
            "EU_erro: 0.16247728998346847JP_erro: 0.06013306074364241loss: 0.3380004380100457\n",
            "Epoch: 3350NA_erro: 0.12431183526682299 \n",
            "EU_erro: 0.055326177168262096JP_erro: 0.01483124240424282loss: 0.06482308687165726\n",
            "Epoch: 3351NA_erro: 0.17909876135892647 \n",
            "EU_erro: 0.06940327411474184JP_erro: 0.009796614794768104loss: 0.0860995654911958\n",
            "Epoch: 3352NA_erro: 0.5476949750915054 \n",
            "EU_erro: 0.11121470798817716JP_erro: 0.07264670475508815loss: 0.24385212003722673\n",
            "Epoch: 3353NA_erro: 0.26646364566891695 \n",
            "EU_erro: 0.1509931290796561JP_erro: 0.08846497350884963loss: 0.1686405765917874\n",
            "Epoch: 3354NA_erro: 0.24787189424499984 \n",
            "EU_erro: 0.27077922525331954JP_erro: 0.04658306291861127loss: 0.1884113910586335\n",
            "Epoch: 3355NA_erro: 0.31426917674929594 \n",
            "EU_erro: 0.21256090134613273JP_erro: 0.1410650560098101loss: 0.22263172060944314\n",
            "Epoch: 3356NA_erro: 0.042472546414811484 \n",
            "EU_erro: 0.05926085996997449JP_erro: 0.17403208932211234loss: 0.09192183960315793\n",
            "Epoch: 3357NA_erro: 0.2588367646978807 \n",
            "EU_erro: 0.05013790703559107JP_erro: 0.009485318563705268loss: 0.10615334030269652\n",
            "Epoch: 3358NA_erro: 0.11238315308740897 \n",
            "EU_erro: 0.02082102751547052JP_erro: 0.01613119082857472loss: 0.04977845868398977\n",
            "Epoch: 3359NA_erro: 5.78930936118429 \n",
            "EU_erro: 2.7935897472292877JP_erro: 0.04513361195261164loss: 2.876010865204094\n",
            "Epoch: 3360NA_erro: 0.06189676680306132 \n",
            "EU_erro: 0.037871878738551176JP_erro: 0.013432927833971127loss: 0.037733863952548005\n",
            "Epoch: 3361NA_erro: 0.5468808033669642 \n",
            "EU_erro: 0.1126135042471479JP_erro: 0.07725382960119913loss: 0.24558272472647732\n",
            "Epoch: 3362NA_erro: 0.07536301317141038 \n",
            "EU_erro: 0.03099671860997991JP_erro: 0.02833157315734745loss: 0.04489710164624591\n",
            "Epoch: 3363NA_erro: 1.6428762628126514 \n",
            "EU_erro: 0.34296597621237584JP_erro: 0.09172867434893468loss: 0.6925236901571584\n",
            "Epoch: 3364NA_erro: 0.0993143310842588 \n",
            "EU_erro: 0.03417758747588757JP_erro: 0.009910838548527208loss: 0.047800915185795274\n",
            "Epoch: 3365NA_erro: 5.954745388770288 \n",
            "EU_erro: 2.882871997448825JP_erro: 0.10735262271969817loss: 2.981656688128331\n",
            "Epoch: 3366NA_erro: 0.1642930784890818 \n",
            "EU_erro: 0.14695965966512992JP_erro: 0.09764751722646314loss: 0.13630008512689162\n",
            "Epoch: 3367NA_erro: 0.12291715126629024 \n",
            "EU_erro: 0.031700108864510704JP_erro: 0.06658311500105747loss: 0.07373345914737199\n",
            "Epoch: 3368NA_erro: 0.17576790595239447 \n",
            "EU_erro: 0.05098871020383613JP_erro: 0.014028914215028748loss: 0.08026184961777325\n",
            "Epoch: 3369NA_erro: 0.11250599410182746 \n",
            "EU_erro: 0.23739465447359306JP_erro: 0.1523638880530069loss: 0.167421536852223\n",
            "Epoch: 3370NA_erro: 0.16138644181480705 \n",
            "EU_erro: 0.03393276486285897JP_erro: 0.00874550421108571loss: 0.0680215737616369\n",
            "Epoch: 3371NA_erro: 0.09437416875085165 \n",
            "EU_erro: 0.18571977467499962JP_erro: 0.027378702810568402loss: 0.10249088900957921\n",
            "Epoch: 3372NA_erro: 0.2674238626347032 \n",
            "EU_erro: 0.15374037646508032JP_erro: 0.03944701002549755loss: 0.15353707380073014\n",
            "Epoch: 3373NA_erro: 0.05631499974302543 \n",
            "EU_erro: 0.028407109338183734JP_erro: 0.11072146800137306loss: 0.06514785825744156\n",
            "Epoch: 3374NA_erro: 0.6007182320883108 \n",
            "EU_erro: 0.05445703517558963JP_erro: 0.007999388053435688loss: 0.2210582319156144\n",
            "Epoch: 3375NA_erro: 0.07741315882335338 \n",
            "EU_erro: 0.035371824290401255JP_erro: 0.014510442351185999loss: 0.042431807333184766\n",
            "Epoch: 3376NA_erro: 0.1823234373284865 \n",
            "EU_erro: 0.050432487975719364JP_erro: 0.009898087081983108loss: 0.08088467657104019\n",
            "Epoch: 3377NA_erro: 0.10357896486918132 \n",
            "EU_erro: 0.034235371637714JP_erro: 0.037515142629312916loss: 0.05844316279241281\n",
            "Epoch: 3378NA_erro: 0.17756316088890844 \n",
            "EU_erro: 0.04116970901341401JP_erro: 0.027823485145273136loss: 0.08218545322270357\n",
            "Epoch: 3379NA_erro: 0.6398478958957879 \n",
            "EU_erro: 0.38825275361999984JP_erro: 0.03305257752884266loss: 0.35371776699095736\n",
            "Epoch: 3380NA_erro: 0.18483721932699515 \n",
            "EU_erro: 0.05495087582935659JP_erro: 0.04163556782774223loss: 0.09380788766136465\n",
            "Epoch: 3381NA_erro: 0.11194611704626749 \n",
            "EU_erro: 0.03730454648187918JP_erro: 0.019512874211451803loss: 0.056254512579866155\n",
            "Epoch: 3382NA_erro: 0.09200347486392473 \n",
            "EU_erro: 0.02188604007395663JP_erro: 0.010052882423696593loss: 0.041314132453859315\n",
            "Epoch: 3383NA_erro: 0.08585572242736816 \n",
            "EU_erro: 0.03313097611878269JP_erro: 0.03625596216482709loss: 0.05174755588058354\n",
            "Epoch: 3384NA_erro: 0.12639410736024842 \n",
            "EU_erro: 0.045690749042717985JP_erro: 0.01050274039423743loss: 0.06086253842642141\n",
            "Epoch: 3385NA_erro: 0.20768442819284838 \n",
            "EU_erro: 0.16824827637783316JP_erro: 0.03033231395159581loss: 0.13542167900144592\n",
            "Epoch: 3386NA_erro: 0.17491444136745246 \n",
            "EU_erro: 0.04501856574716494JP_erro: 0.007990629289501398loss: 0.07597454758577568\n",
            "Epoch: 3387NA_erro: 0.06225194117819616 \n",
            "EU_erro: 0.07835082305494205JP_erro: 0.009639378088389257loss: 0.05008071892021238\n",
            "Epoch: 3388NA_erro: 0.17310066740642222 \n",
            "EU_erro: 0.054839867954106294JP_erro: 0.007531414198320966loss: 0.07849064908286398\n",
            "Epoch: 3389NA_erro: 0.09173666784005571 \n",
            "EU_erro: 0.03343171158502268JP_erro: 0.024422531904176224loss: 0.04986363325932229\n",
            "Epoch: 3390NA_erro: 0.5570860796196516 \n",
            "EU_erro: 0.1114732165669286JP_erro: 0.07701900578284449loss: 0.24852612221887868\n",
            "Epoch: 3391NA_erro: 0.10366750317950581 \n",
            "EU_erro: 0.033803496711937955JP_erro: 0.01071785194005153loss: 0.04939628663913224\n",
            "Epoch: 3392NA_erro: 0.07772144421126491 \n",
            "EU_erro: 0.035615676595259084JP_erro: 0.017722447713216145loss: 0.04368652669034263\n",
            "Epoch: 3393NA_erro: 0.22956537645916605 \n",
            "EU_erro: 0.042006123897641204JP_erro: 0.012633496707724047loss: 0.09473500325698261\n",
            "Epoch: 3394NA_erro: 0.10362627894379371 \n",
            "EU_erro: 0.025756292102872863JP_erro: 0.01109448514243429loss: 0.04682568616645281\n",
            "Epoch: 3395NA_erro: 0.7847102113472398 \n",
            "EU_erro: 0.16387795293053917JP_erro: 0.05599351816399153loss: 0.33486055773358014\n",
            "Epoch: 3396NA_erro: 0.06770098394201707 \n",
            "EU_erro: 0.22367122561432595JP_erro: 0.05769785060438999loss: 0.11635669442110283\n",
            "Epoch: 3397NA_erro: 0.10990518008091653 \n",
            "EU_erro: 0.05416465822116349JP_erro: 0.010526214921197225loss: 0.05819869133853173\n",
            "Epoch: 3398NA_erro: 0.07114968096563058 \n",
            "EU_erro: 0.04696582176888636JP_erro: 0.038839426151541775loss: 0.05231831886971644\n",
            "Epoch: 3399NA_erro: 0.09156568105830702 \n",
            "EU_erro: 0.04510424857915834JP_erro: 0.04693990067918171loss: 0.06120328293290249\n",
            "Epoch: 3400NA_erro: 0.12946870899939722 \n",
            "EU_erro: 0.052030802697174305JP_erro: 0.05107855611993361loss: 0.07752602414567342\n",
            "Epoch: 3401NA_erro: 0.08801456569701202 \n",
            "EU_erro: 0.011723479328229446JP_erro: 0.040200361909792405loss: 0.046646142190741015\n",
            "Epoch: 3402NA_erro: 0.5906757827877074 \n",
            "EU_erro: 0.12773831685384116JP_erro: 0.07799384205840355loss: 0.26546931081964065\n",
            "Epoch: 3403NA_erro: 0.06741041822950969 \n",
            "EU_erro: 0.07000629754029503JP_erro: 0.009905817203743512loss: 0.04910751368648322\n",
            "Epoch: 3404NA_erro: 0.798879298128823 \n",
            "EU_erro: 0.2640175080114557JP_erro: 0.053774857705877734loss: 0.37222389102906217\n",
            "Epoch: 3405NA_erro: 0.5216232565946357 \n",
            "EU_erro: 0.21521032318588376JP_erro: 0.039250531861948414loss: 0.2586947115816811\n",
            "Epoch: 3406NA_erro: 0.18162030582280123 \n",
            "EU_erro: 0.22379141445307768JP_erro: 0.12905931287957717loss: 0.17815702645353568\n",
            "Epoch: 3407NA_erro: 0.06082656771637673 \n",
            "EU_erro: 0.2100225382072981JP_erro: 0.059768802435823186loss: 0.1102059756138528\n",
            "Epoch: 3408NA_erro: 0.10270704594693443 \n",
            "EU_erro: 0.029249549374099848JP_erro: 0.01345583865808886loss: 0.04847081132637438\n",
            "Epoch: 3409NA_erro: 0.15740061915198036 \n",
            "EU_erro: 0.06390676479931026JP_erro: 0.011312252329301465loss: 0.07753988569097002\n",
            "Epoch: 3410NA_erro: 0.12366696845653445 \n",
            "EU_erro: 0.08736157602117967JP_erro: 0.012324455865593843loss: 0.07445099741913551\n",
            "Epoch: 3411NA_erro: 0.09185563686282136 \n",
            "EU_erro: 0.030225635498993157JP_erro: 0.009482296746830607loss: 0.043854519378307255\n",
            "Epoch: 3412NA_erro: 0.06956844366798105 \n",
            "EU_erro: 0.07129432619080063JP_erro: 0.027045364989790807loss: 0.05596938059311504\n",
            "Epoch: 3413NA_erro: 0.06837150847264963 \n",
            "EU_erro: 0.019343216982922812JP_erro: 0.017387875059778377loss: 0.03503420094186945\n",
            "Epoch: 3414NA_erro: 0.07208951221880062 \n",
            "EU_erro: 0.09227027264676353JP_erro: 0.012459763029749079loss: 0.05893984968348067\n",
            "Epoch: 3415NA_erro: 0.06396465061246887 \n",
            "EU_erro: 0.03468003845954126JP_erro: 0.03164881421614063loss: 0.043431169302888616\n",
            "Epoch: 3416NA_erro: 0.09544227474419646 \n",
            "EU_erro: 0.08540788362192553JP_erro: 0.008125131328900656loss: 0.06299176419428153\n",
            "Epoch: 3417NA_erro: 0.08509463118028271 \n",
            "EU_erro: 0.21002229794051297JP_erro: 0.05606332028559012loss: 0.11706009391666383\n",
            "Epoch: 3418NA_erro: 0.06460428699966549 \n",
            "EU_erro: 0.038992379062859586JP_erro: 0.009488312657489333loss: 0.037694993869278784\n",
            "Epoch: 3419NA_erro: 0.43333057285279264 \n",
            "EU_erro: 0.2216763459434805JP_erro: 0.13880840567655342loss: 0.26460512663966923\n",
            "Epoch: 3420NA_erro: 0.22734684537547503 \n",
            "EU_erro: 0.1871931460476661JP_erro: 0.010429293841354607loss: 0.14165642649628396\n",
            "Epoch: 3421NA_erro: 0.09000799452611642 \n",
            "EU_erro: 0.043095539706621985JP_erro: 0.009674639549366263loss: 0.047592728636985604\n",
            "Epoch: 3422NA_erro: 0.19074194191038146 \n",
            "EU_erro: 0.06464874097543169JP_erro: 0.006816060275070427loss: 0.08740224579507991\n",
            "Epoch: 3423NA_erro: 0.09504568669223046 \n",
            "EU_erro: 0.022096467572589252JP_erro: 0.010472126016321108loss: 0.04253809766251911\n",
            "Epoch: 3424NA_erro: 0.1903722267742305 \n",
            "EU_erro: 0.086789842723876JP_erro: 0.008030196492986162loss: 0.09506409482438435\n",
            "Epoch: 3425NA_erro: 0.06948400375454925 \n",
            "EU_erro: 0.03158055996710016JP_erro: 0.010243635769038237loss: 0.0371027339336484\n",
            "Epoch: 3426NA_erro: 0.18527792405712512 \n",
            "EU_erro: 0.11111603226772575JP_erro: 0.012266112621440443loss: 0.1028866915739784\n",
            "Epoch: 3427NA_erro: 0.15476828397706496 \n",
            "EU_erro: 0.04782355108926462JP_erro: 0.012075771657071372loss: 0.07155587968900222\n",
            "Epoch: 3428NA_erro: 0.12034355207931163 \n",
            "EU_erro: 0.02782003593075183JP_erro: 0.046056555223095326loss: 0.06474004697430041\n",
            "Epoch: 3429NA_erro: 0.6142542713372282 \n",
            "EU_erro: 0.11914130329161651JP_erro: 0.07453483666560447loss: 0.2693101232366044\n",
            "Epoch: 3430NA_erro: 0.2513191127037817 \n",
            "EU_erro: 0.09107219156368758JP_erro: 0.008820618769919226loss: 0.11707065641417984\n",
            "Epoch: 3431NA_erro: 0.07676648077114608 \n",
            "EU_erro: 0.041451500367748646JP_erro: 0.010779550833295482loss: 0.042999174243720004\n",
            "Epoch: 3432NA_erro: 0.09062491646108702 \n",
            "EU_erro: 0.04143753255060477JP_erro: 0.021966196769891783loss: 0.05134288192719452\n",
            "Epoch: 3433NA_erro: 0.6787116767824158 \n",
            "EU_erro: 0.6262015557104303JP_erro: 0.09269049001294513loss: 0.46586787053780965\n",
            "Epoch: 3434NA_erro: 0.05983801313148913 \n",
            "EU_erro: 0.024998682413914407JP_erro: 0.03166345893874649loss: 0.03883338713830756\n",
            "Epoch: 3435NA_erro: 0.7113353226536004 \n",
            "EU_erro: 0.2163704975630886JP_erro: 0.11784130288648975loss: 0.34851569537968596\n",
            "Epoch: 3436NA_erro: 0.5446675766346066 \n",
            "EU_erro: 0.15513553175815317JP_erro: 0.07329514322354812loss: 0.25769943414732466\n",
            "Epoch: 3437NA_erro: 0.09313667467398237 \n",
            "EU_erro: 0.04849875620169233JP_erro: 0.009775978419207788loss: 0.05047047323034715\n",
            "Epoch: 3438NA_erro: 0.6817365986432216 \n",
            "EU_erro: 0.34208271854607636JP_erro: 0.05145065082136051loss: 0.3584233365317648\n",
            "Epoch: 3439NA_erro: 0.6491474772608558 \n",
            "EU_erro: 0.160574709722238JP_erro: 0.07111752218054246loss: 0.29361323792805044\n",
            "Epoch: 3440NA_erro: 0.44387676919153496 \n",
            "EU_erro: 0.2153978236885958JP_erro: 0.15430900477623755loss: 0.27119453563246615\n",
            "Epoch: 3441NA_erro: 0.21548572436783664 \n",
            "EU_erro: 0.07543852162915607JP_erro: 0.03484105647996415loss: 0.10858843492907147\n",
            "Epoch: 3442NA_erro: 0.1291162653486858 \n",
            "EU_erro: 0.06284144497657007JP_erro: 0.012653303700824117loss: 0.06820367750271346\n",
            "Epoch: 3443NA_erro: 0.052411907403044 \n",
            "EU_erro: 0.018931469020917434JP_erro: 0.010258645512336908loss: 0.02720067205355149\n",
            "Epoch: 3444NA_erro: 0.06501128969266433 \n",
            "EU_erro: 0.031688132489374445JP_erro: 0.01424522362938223loss: 0.0369815516841504\n",
            "Epoch: 3445NA_erro: 0.30376972154129384 \n",
            "EU_erro: 0.21690337232841078JP_erro: 0.14181710952936216loss: 0.22083007088003231\n",
            "Epoch: 3446NA_erro: 0.08464525836382726 \n",
            "EU_erro: 0.06333156149516735JP_erro: 0.014832149180330972loss: 0.054269654806270155\n",
            "Epoch: 3447NA_erro: 0.5241121617398521 \n",
            "EU_erro: 0.20980481953583946JP_erro: 0.050579489663589834loss: 0.2614988282669422\n",
            "Epoch: 3448NA_erro: 5.813299371290577 \n",
            "EU_erro: 2.782376015833182JP_erro: 0.04677396874095118loss: 2.8808162009069163\n",
            "Epoch: 3449NA_erro: 0.12882264085518297 \n",
            "EU_erro: 0.04066858642785124JP_erro: 0.010532910047575485loss: 0.06000804808712745\n",
            "Epoch: 3450NA_erro: 0.052687913872474845 \n",
            "EU_erro: 0.022819869278013244JP_erro: 0.02623290516609369loss: 0.03391356431236563\n",
            "Epoch: 3451NA_erro: 0.0567129185033399 \n",
            "EU_erro: 0.029729001743848935JP_erro: 0.008594014847925466loss: 0.03167864657187647\n",
            "Epoch: 3452NA_erro: 0.06231271019277647 \n",
            "EU_erro: 0.1939746390941531JP_erro: 0.06663221259449803loss: 0.10763986166133437\n",
            "Epoch: 3453NA_erro: 0.08560449578041254 \n",
            "EU_erro: 0.039871284203935965JP_erro: 0.010097521220066751loss: 0.04519110502198685\n",
            "Epoch: 3454NA_erro: 0.7909482763719189 \n",
            "EU_erro: 0.4913669778395069JP_erro: 0.050671687421872634loss: 0.4443290252094121\n",
            "Epoch: 3455NA_erro: 0.7239155436671058 \n",
            "EU_erro: 0.3295090771460718JP_erro: 0.036459997180820436loss: 0.3632948749749235\n",
            "Epoch: 3456NA_erro: 0.646146951719772 \n",
            "EU_erro: 0.05730554576992065JP_erro: 0.028790138488592104loss: 0.24408092794492264\n",
            "Epoch: 3457NA_erro: 0.07750671501307524 \n",
            "EU_erro: 0.07927357688430668JP_erro: 0.01750819673833921loss: 0.058096162108487864\n",
            "Epoch: 3458NA_erro: 0.8575051329856695 \n",
            "EU_erro: 0.2733090127161307JP_erro: 0.14166856920996376loss: 0.424160920372305\n",
            "Epoch: 3459NA_erro: 5.954809262771015 \n",
            "EU_erro: 2.9530553300251334JP_erro: 0.2119560204735098loss: 3.0399405309396195\n",
            "Epoch: 3460NA_erro: 0.10576316552568776 \n",
            "EU_erro: 0.04064160261967385JP_erro: 0.02470690612645112loss: 0.057037227837614314\n",
            "Epoch: 3461NA_erro: 0.09299151657163635 \n",
            "EU_erro: 0.03858061492905136JP_erro: 0.006628492197325063loss: 0.0460668774538262\n",
            "Epoch: 3462NA_erro: 0.5883554340332977 \n",
            "EU_erro: 0.0891161796658538JP_erro: 0.01308606923088547loss: 0.23018591163694396\n",
            "Epoch: 3463NA_erro: 0.2461279455081437 \n",
            "EU_erro: 0.17664085062899332JP_erro: 0.05990179472191389loss: 0.16089021697524905\n",
            "Epoch: 3464NA_erro: 0.060418677884478904 \n",
            "EU_erro: 0.02705219411110693JP_erro: 0.07119481415711632loss: 0.05288856436115827\n",
            "Epoch: 3465NA_erro: 0.690225039341653 \n",
            "EU_erro: 0.3105358375135318JP_erro: 0.047556854957757994loss: 0.34943924393764764\n",
            "Epoch: 3466NA_erro: 0.10266909303591233 \n",
            "EU_erro: 0.04944178023079569JP_erro: 0.018267775921858558loss: 0.056792885758156\n",
            "Epoch: 3467NA_erro: 0.12553936751313913 \n",
            "EU_erro: 0.025922251764193986JP_erro: 0.016030923340671747loss: 0.0558308483094208\n",
            "Epoch: 3468NA_erro: 0.30689771785292513 \n",
            "EU_erro: 0.27363250421923263JP_erro: 0.10742120964582576loss: 0.2293171623880549\n",
            "Epoch: 3469NA_erro: 0.7123618902162064 \n",
            "EU_erro: 0.4611281091852706JP_erro: 0.0989838256392368loss: 0.4241579632426417\n",
            "Epoch: 3470NA_erro: 0.05933890509050946 \n",
            "EU_erro: 0.21375846493151762JP_erro: 0.05671260430831318loss: 0.10993666427080022\n",
            "Epoch: 3471NA_erro: 0.106157101401987 \n",
            "EU_erro: 0.03593103368152944JP_erro: 0.007852594866309055loss: 0.04998024581938751\n",
            "Epoch: 3472NA_erro: 0.05363730497138445 \n",
            "EU_erro: 0.03388800824335379JP_erro: 0.06394328073013661loss: 0.050489532855129984\n",
            "Epoch: 3473NA_erro: 0.07864171220350635 \n",
            "EU_erro: 0.06752107494561248JP_erro: 0.015779302794803943loss: 0.05398070165353228\n",
            "Epoch: 3474NA_erro: 0.05860405836918557 \n",
            "EU_erro: 0.02676350663798724JP_erro: 0.06244672823321912loss: 0.04927143108013064\n",
            "Epoch: 3475NA_erro: 0.10201340497926224 \n",
            "EU_erro: 0.05210700423218483JP_erro: 0.014069666003071985loss: 0.05606335724971091\n",
            "Epoch: 3476NA_erro: 0.08984308834223784 \n",
            "EU_erro: 0.02472745124683824JP_erro: 0.009616328071254168loss: 0.04139562447865804\n",
            "Epoch: 3477NA_erro: 0.05297727363054142 \n",
            "EU_erro: 0.03798063873320587JP_erro: 0.011666924223419308loss: 0.03420828155768934\n",
            "Epoch: 3478NA_erro: 0.1670205500698829 \n",
            "EU_erro: 0.027611248714979306JP_erro: 0.01693883838579636loss: 0.07052354572355285\n",
            "Epoch: 3479NA_erro: 0.1458290473435276 \n",
            "EU_erro: 0.0634912326354389JP_erro: 0.06517470807068107loss: 0.09149834167125613\n",
            "Epoch: 3480NA_erro: 0.594016341275947 \n",
            "EU_erro: 0.2184486943621968JP_erro: 0.24176954298980477loss: 0.35141150156656903\n",
            "Epoch: 3481NA_erro: 0.06297528743743896 \n",
            "EU_erro: 0.03468032262121984JP_erro: 0.011220471572506335loss: 0.036292027595431306\n",
            "Epoch: 3482NA_erro: 0.08361264716747195 \n",
            "EU_erro: 0.033683751442635704JP_erro: 0.02808865188628204loss: 0.048461690429569215\n",
            "Epoch: 3483NA_erro: 0.1167888974034509 \n",
            "EU_erro: 0.027799583220666692JP_erro: 0.09428820868795232loss: 0.07962556772453841\n",
            "Epoch: 3484NA_erro: 0.19044986990995186 \n",
            "EU_erro: 0.15532046325447024JP_erro: 0.03952454226885655loss: 0.12843162514442621\n",
            "Epoch: 3485NA_erro: 0.1351194141447082 \n",
            "EU_erro: 0.19606699315152426JP_erro: 0.03566069889438245loss: 0.12228238490200782\n",
            "Epoch: 3486NA_erro: 0.19970455835031908 \n",
            "EU_erro: 0.028664275657298954JP_erro: 0.1319992357446242loss: 0.12012270069861597\n",
            "Epoch: 3487NA_erro: 0.0678347062694934 \n",
            "EU_erro: 0.08209490960882615JP_erro: 0.09925494822420815loss: 0.08306152136750923\n",
            "Epoch: 3488NA_erro: 0.1479804608248925 \n",
            "EU_erro: 0.030594211678172268JP_erro: 0.014305676138678263loss: 0.06429345108741938\n",
            "Epoch: 3489NA_erro: 0.10091436001681542 \n",
            "EU_erro: 0.24158246757448182JP_erro: 0.06231109763300696loss: 0.13493597969528318\n",
            "Epoch: 3490NA_erro: 0.09706130323483962 \n",
            "EU_erro: 0.03147005572799565JP_erro: 0.09222257044888282loss: 0.07358464621758276\n",
            "Epoch: 3491NA_erro: 0.10240108467811762 \n",
            "EU_erro: 0.049643474955891456JP_erro: 0.05032988943794901loss: 0.067458148150481\n",
            "Epoch: 3492NA_erro: 0.10090233743652817 \n",
            "EU_erro: 0.02883295680201331JP_erro: 0.025660744471143384loss: 0.051798681880152506\n",
            "Epoch: 3493NA_erro: 0.5664765187936236 \n",
            "EU_erro: 0.09533366491628248JP_erro: 0.09182905965997268loss: 0.2512130811232929\n",
            "Epoch: 3494NA_erro: 0.30520888262016826 \n",
            "EU_erro: 0.3490428407062856JP_erro: 0.10026514992233394loss: 0.2515056336572928\n",
            "Epoch: 3495NA_erro: 0.1368933407835258 \n",
            "EU_erro: 0.023065837778786357JP_erro: 0.008084547034529753loss: 0.056014573851297066\n",
            "Epoch: 3496NA_erro: 0.6510522384052129 \n",
            "EU_erro: 0.4813053071960922JP_erro: 0.13114128001900607loss: 0.4211663090905478\n",
            "Epoch: 3497NA_erro: 0.06351224211759346 \n",
            "EU_erro: 0.20513833955276845JP_erro: 0.05733299162960792loss: 0.10866118955981824\n",
            "Epoch: 3498NA_erro: 0.1092333387034808 \n",
            "EU_erro: 0.027735951796982638JP_erro: 0.044226614079734154loss: 0.060398634089980016\n",
            "Epoch: 3499NA_erro: 0.1876912930215052 \n",
            "EU_erro: 0.15412799147672432JP_erro: 0.04818638627843339loss: 0.13000190720077634\n",
            "Epoch: 3500NA_erro: 0.1282517022864763 \n",
            "EU_erro: 0.0337190591087637JP_erro: 0.030232293661250624loss: 0.06406768809917361\n",
            "Epoch: 3501NA_erro: 0.06582227788230245 \n",
            "EU_erro: 0.05476839320604191JP_erro: 0.009689604820207108loss: 0.0434267613314843\n",
            "Epoch: 3502NA_erro: 0.09248030278109765 \n",
            "EU_erro: 0.021486072115195813JP_erro: 0.00703986243222111loss: 0.0403354149456172\n",
            "Epoch: 3503NA_erro: 0.11180522829987281 \n",
            "EU_erro: 0.05418128283448922JP_erro: 0.010296091321826906loss: 0.05876086941061094\n",
            "Epoch: 3504NA_erro: 0.12256817300190297 \n",
            "EU_erro: 0.07785226947577424JP_erro: 0.09875920391822046loss: 0.09972655495931936\n",
            "Epoch: 3505NA_erro: 0.13473694638688435 \n",
            "EU_erro: 0.06438301053158073JP_erro: 0.017622301513834517loss: 0.07224742294281952\n",
            "Epoch: 3506NA_erro: 0.08426962896834972 \n",
            "EU_erro: 0.030771269816760868JP_erro: 0.09149099505224893loss: 0.06884396538253902\n",
            "Epoch: 3507NA_erro: 0.12247334155001381 \n",
            "EU_erro: 0.01973357080489166JP_erro: 0.0824060273724933loss: 0.07487098838007727\n",
            "Epoch: 3508NA_erro: 0.04623614078344301 \n",
            "EU_erro: 0.023985761542652927JP_erro: 0.010070078825765803loss: 0.02676399410233017\n",
            "Epoch: 3509NA_erro: 0.08064590683279112 \n",
            "EU_erro: 0.032967687115188715JP_erro: 0.02875138391820035loss: 0.04745499570240346\n",
            "Epoch: 3510NA_erro: 0.09794437608053518 \n",
            "EU_erro: 0.1819985227067341JP_erro: 0.013038243434225867loss: 0.09766039922255879\n",
            "Epoch: 3511NA_erro: 0.3202011418897052 \n",
            "EU_erro: 0.2031916795774948JP_erro: 0.1454094029212183loss: 0.22293408711751303\n",
            "Epoch: 3512NA_erro: 0.06987233956654866 \n",
            "EU_erro: 0.017998021009356477JP_erro: 0.035162512646165loss: 0.041010957355647124\n",
            "Epoch: 3513NA_erro: 0.6573972775954608 \n",
            "EU_erro: 0.4568563136019448JP_erro: 0.04896475363147351loss: 0.38773946983869684\n",
            "Epoch: 3514NA_erro: 0.07584656393805216 \n",
            "EU_erro: 0.018904726634653965JP_erro: 0.008536918565284374loss: 0.03442940323851829\n",
            "Epoch: 3515NA_erro: 0.286174873973048 \n",
            "EU_erro: 0.06818656311478725JP_erro: 0.01208316448122956loss: 0.12214819590250652\n",
            "Epoch: 3516NA_erro: 6.299715855324915 \n",
            "EU_erro: 2.9924383828806325JP_erro: 0.14502155688381935loss: 3.1457253389580306\n",
            "Epoch: 3517NA_erro: 0.7657229992770409 \n",
            "EU_erro: 0.3466062397919884JP_erro: 0.03252534672271374loss: 0.381618248399838\n",
            "Epoch: 3518NA_erro: 0.3265773048696592 \n",
            "EU_erro: 0.16155570052390875JP_erro: 0.04777121913525485loss: 0.1786347322685774\n",
            "Epoch: 3519NA_erro: 0.1263574389524238 \n",
            "EU_erro: 0.024613710336906965JP_erro: 0.01545958625253781loss: 0.05547691685284755\n",
            "Epoch: 3520NA_erro: 0.12232749961143316 \n",
            "EU_erro: 0.029866147873013518JP_erro: 0.011417374823444574loss: 0.054537002430405726\n",
            "Epoch: 3521NA_erro: 0.20891331887060358 \n",
            "EU_erro: 0.07791788079017817JP_erro: 0.01078645157259564loss: 0.09920587835385818\n",
            "Epoch: 3522NA_erro: 5.730625714442526 \n",
            "EU_erro: 2.7863285153411157JP_erro: 0.04628113997999088loss: 2.8544122858564984\n",
            "Epoch: 3523NA_erro: 0.08617574854414592 \n",
            "EU_erro: 0.06581088369206865JP_erro: 0.09074100228243095loss: 0.08090921150621518\n",
            "Epoch: 3524NA_erro: 0.12001188226448473 \n",
            "EU_erro: 0.08141086083049923JP_erro: 0.008263597192690354loss: 0.0698954513830732\n",
            "Epoch: 3525NA_erro: 0.608370396517968 \n",
            "EU_erro: 0.05568978860396748JP_erro: 0.009791136018989621loss: 0.2246171189833057\n",
            "Epoch: 3526NA_erro: 0.057569705238638 \n",
            "EU_erro: 0.03603706526201825JP_erro: 0.011554442403852477loss: 0.035053741100222566\n",
            "Epoch: 3527NA_erro: 0.13340454692988432 \n",
            "EU_erro: 0.05427318487980569JP_erro: 0.013100129458331323loss: 0.06692594798036323\n",
            "Epoch: 3528NA_erro: 0.08987209593602853 \n",
            "EU_erro: 0.18751393916995027JP_erro: 0.020592009374337604loss: 0.09932602098745894\n",
            "Epoch: 3529NA_erro: 0.6894231397052144 \n",
            "EU_erro: 0.45079379118690194JP_erro: 0.054477032764937525loss: 0.3982312919557557\n",
            "Epoch: 3530NA_erro: 5.870241860086604 \n",
            "EU_erro: 2.928868670796239JP_erro: 0.07980354996614678loss: 2.9596379006555837\n",
            "Epoch: 3531NA_erro: 0.11127905328144398 \n",
            "EU_erro: 0.048531612684560375JP_erro: 0.04117322522540425loss: 0.06699463193730791\n",
            "Epoch: 3532NA_erro: 0.060571570729100424 \n",
            "EU_erro: 0.04059304562649985JP_erro: 0.011440910572229429loss: 0.03753517949303915\n",
            "Epoch: 3533NA_erro: 0.0644072035486384 \n",
            "EU_erro: 0.0393276759820391JP_erro: 0.02166193354037381loss: 0.04179893538009289\n",
            "Epoch: 3534NA_erro: 0.11065155036689699 \n",
            "EU_erro: 0.18388990283936493JP_erro: 0.0077133487130320345loss: 0.10075160699297292\n",
            "Epoch: 3535NA_erro: 0.6311057334722474 \n",
            "EU_erro: 0.14773018600404725JP_erro: 0.03944932490356209loss: 0.2727617773898812\n",
            "Epoch: 3536NA_erro: 0.10383415591809177 \n",
            "EU_erro: 0.03348083690155384JP_erro: 0.036205891952958216loss: 0.05784029646437298\n",
            "Epoch: 3537NA_erro: 0.08602026821106903 \n",
            "EU_erro: 0.03033410671145417JP_erro: 0.06478109563043875loss: 0.06037849326466405\n",
            "Epoch: 3538NA_erro: 0.7055757951366809 \n",
            "EU_erro: 0.5468938147374826JP_erro: 0.12714636418246483loss: 0.45987199443255283\n",
            "Epoch: 3539NA_erro: 0.2508132956748785 \n",
            "EU_erro: 0.06229955096577489JP_erro: 0.011302485715511233loss: 0.10813845220462297\n",
            "Epoch: 3540NA_erro: 0.0815223261367443 \n",
            "EU_erro: 0.0396018573480059JP_erro: 0.0125037410924601loss: 0.044542643450951394\n",
            "Epoch: 3541NA_erro: 0.07652856582819029 \n",
            "EU_erro: 0.03130546835965888JP_erro: 0.007170532331910244loss: 0.038334856661715246\n",
            "Epoch: 3542NA_erro: 0.19720378772232883 \n",
            "EU_erro: 0.03232191237368325JP_erro: 0.014085179382516431loss: 0.08120363072831502\n",
            "Epoch: 3543NA_erro: 0.05516342414441959 \n",
            "EU_erro: 0.028631490792414938JP_erro: 0.02054126456726429loss: 0.03477873266205307\n",
            "Epoch: 3544NA_erro: 0.1710469223732172 \n",
            "EU_erro: 0.14945012654444967JP_erro: 0.04693779834481173loss: 0.1224782855011696\n",
            "Epoch: 3545NA_erro: 0.24780818658281667 \n",
            "EU_erro: 0.1515311603398286JP_erro: 0.030221354591754054loss: 0.1431869074355724\n",
            "Epoch: 3546NA_erro: 0.1021318565043368 \n",
            "EU_erro: 0.03522512293601221JP_erro: 0.012679560933002206loss: 0.05001218281975088\n",
            "Epoch: 3547NA_erro: 0.3605969746907552 \n",
            "EU_erro: 0.2078205670497214JP_erro: 0.15500149061513502loss: 0.24113967437152714\n",
            "Epoch: 3548NA_erro: 0.07935484250386556 \n",
            "EU_erro: 0.05506232265354127JP_erro: 0.013489407855410909loss: 0.0493021944696589\n",
            "Epoch: 3549NA_erro: 0.5383383211239364 \n",
            "EU_erro: 0.29753317204556723JP_erro: 0.13460923534955166loss: 0.323493573092675\n",
            "Epoch: 3550NA_erro: 0.10553477346435074 \n",
            "EU_erro: 0.20224972288737925JP_erro: 0.062188127244165704loss: 0.12332420940547026\n",
            "Epoch: 3551NA_erro: 0.039856470832528995 \n",
            "EU_erro: 0.031665672165478845JP_erro: 0.02546941356141438loss: 0.03233052270356999\n",
            "Epoch: 3552NA_erro: 0.11180133782615957 \n",
            "EU_erro: 0.059968459513760355JP_erro: 0.01350117515223895loss: 0.061756986980290376\n",
            "Epoch: 3553NA_erro: 0.25730352993159333 \n",
            "EU_erro: 0.07666581822920215JP_erro: 0.011472991963689642loss: 0.11514745017354802\n",
            "Epoch: 3554NA_erro: 0.09254306785820067 \n",
            "EU_erro: 0.090759558271068JP_erro: 0.02203584410423456loss: 0.06844616213510203\n",
            "Epoch: 3555NA_erro: 0.07672961830168731 \n",
            "EU_erro: 0.03314533667970997JP_erro: 0.012964731039002884loss: 0.04094656123671421\n",
            "Epoch: 3556NA_erro: 5.698201379110647 \n",
            "EU_erro: 2.7844596093939256JP_erro: 0.04613869412000789loss: 2.842933595642563\n",
            "Epoch: 3557NA_erro: 0.2588742278343023 \n",
            "EU_erro: 0.028945362383081007JP_erro: 0.011973130379536356loss: 0.09993091110111207\n",
            "Epoch: 3558NA_erro: 0.21450580552566884 \n",
            "EU_erro: 0.1650197931038317JP_erro: 0.04678954449734946loss: 0.1421050507892934\n",
            "Epoch: 3559NA_erro: 0.07719790288644243 \n",
            "EU_erro: 0.01856971734253935JP_erro: 0.024146701014319132loss: 0.03997143848921902\n",
            "Epoch: 3560NA_erro: 0.13656734496124032 \n",
            "EU_erro: 0.034980311874271364JP_erro: 0.02150665650996127loss: 0.06435144317242526\n",
            "Epoch: 3561NA_erro: 0.04401095615800961 \n",
            "EU_erro: 0.051616989364919735JP_erro: 0.0956987133321836loss: 0.06377555603204771\n",
            "Epoch: 3562NA_erro: 0.06208515906518744 \n",
            "EU_erro: 0.06423265897026358JP_erro: 0.01442961337030396loss: 0.046915809313456215\n",
            "Epoch: 3563NA_erro: 0.05234651787336483 \n",
            "EU_erro: 0.017537880082463108JP_erro: 0.037919215915739074loss: 0.035934538342231925\n",
            "Epoch: 3564NA_erro: 0.12583249299101126 \n",
            "EU_erro: 0.02960596666779629JP_erro: 0.02675362797670586loss: 0.060730701269105423\n",
            "Epoch: 3565NA_erro: 0.0866083026856415 \n",
            "EU_erro: 0.03299175075782362JP_erro: 0.008974393555360246loss: 0.04285815150238747\n",
            "Epoch: 3566NA_erro: 0.34978053366491035 \n",
            "EU_erro: 0.391636005667753JP_erro: 0.19580833671628967loss: 0.31240832898043847\n",
            "Epoch: 3567NA_erro: 0.13743532720462295 \n",
            "EU_erro: 0.2160207430521647JP_erro: 0.06856502026550529loss: 0.14067369838093602\n",
            "Epoch: 3568NA_erro: 0.2263176533602929 \n",
            "EU_erro: 0.044018575387407645JP_erro: 0.045134905696839325loss: 0.10515705559604852\n",
            "Epoch: 3569NA_erro: 0.20756348158962043 \n",
            "EU_erro: 0.06652829258940941JP_erro: 0.011827480192332304loss: 0.09530642235925955\n",
            "Epoch: 3570NA_erro: 0.5967904246130655 \n",
            "EU_erro: 0.04743357037389001JP_erro: 0.010481926128845806loss: 0.21823531897493112\n",
            "Epoch: 3571NA_erro: 0.15207578045453213 \n",
            "EU_erro: 0.08420559787010962JP_erro: 0.02447195062341616loss: 0.08691778478696364\n",
            "Epoch: 3572NA_erro: 0.6723469726799071 \n",
            "EU_erro: 0.32299732977105666JP_erro: 0.0666099370912064loss: 0.3539847588354303\n",
            "Epoch: 3573NA_erro: 0.10542479596396749 \n",
            "EU_erro: 0.04041769245798273JP_erro: 0.09368268094321554loss: 0.07984172466189363\n",
            "Epoch: 3574NA_erro: 0.09678584660670554 \n",
            "EU_erro: 0.11996949365896772JP_erro: 0.02260879259700923loss: 0.07978805275850517\n",
            "Epoch: 3575NA_erro: 0.18738624661467795 \n",
            "EU_erro: 0.022880862387575845JP_erro: 0.009626635285310967loss: 0.07329791553260744\n",
            "Epoch: 3576NA_erro: 0.3201277311458144 \n",
            "EU_erro: 0.2151840047318806JP_erro: 0.029300416624823283loss: 0.18820404082305672\n",
            "Epoch: 3577NA_erro: 0.08588830629984538 \n",
            "EU_erro: 0.03634084795796594JP_erro: 0.01582093594610229loss: 0.046016698659852494\n",
            "Epoch: 3578NA_erro: 0.06499014159505681 \n",
            "EU_erro: 0.03672650156095046JP_erro: 0.008608982429023861loss: 0.03677520641060763\n",
            "Epoch: 3579NA_erro: 0.6215142834094144 \n",
            "EU_erro: 0.04655594973601112JP_erro: 0.011162625033726064loss: 0.22641096928322962\n",
            "Epoch: 3580NA_erro: 0.15952707261078117 \n",
            "EU_erro: 0.08024845936501673JP_erro: 0.015852146370466367loss: 0.08520922919576482\n",
            "Epoch: 3581NA_erro: 0.6742416795834091 \n",
            "EU_erro: 0.49834554509599077JP_erro: 0.049889351046362586loss: 0.40749220885047616\n",
            "Epoch: 3582NA_erro: 0.07152799950089565 \n",
            "EU_erro: 0.047520394473112834JP_erro: 0.021610978499863497loss: 0.04688645518103311\n",
            "Epoch: 3583NA_erro: 0.15521748121394668 \n",
            "EU_erro: 0.026083544705265253JP_erro: 0.01108153268348339loss: 0.06412752376970395\n",
            "Epoch: 3584NA_erro: 0.6352366218271182 \n",
            "EU_erro: 0.24372058321339216JP_erro: 0.041463647701943565loss: 0.306806952454323\n",
            "Epoch: 3585NA_erro: 0.09744179341220116 \n",
            "EU_erro: 0.07195311461308206JP_erro: 0.117424162783364loss: 0.09560634923535724\n",
            "Epoch: 3586NA_erro: 0.10204573934392412 \n",
            "EU_erro: 0.11828070463136185JP_erro: 0.00817139770171439loss: 0.07616595704426136\n",
            "Epoch: 3587NA_erro: 1.2105901111928068 \n",
            "EU_erro: 0.6392190622728925JP_erro: 0.0788193987321484loss: 0.6428762184557064\n",
            "Epoch: 3588NA_erro: 0.11469422384750011 \n",
            "EU_erro: 0.029065449570500574JP_erro: 0.009663269616836724loss: 0.051140988519949505\n",
            "Epoch: 3589NA_erro: 0.059379450110502024 \n",
            "EU_erro: 0.025859964910403703JP_erro: 0.043518372284349544loss: 0.04291926243508509\n",
            "Epoch: 3590NA_erro: 0.19936463629552562 \n",
            "EU_erro: 0.07852758548056432JP_erro: 0.008160600713057112loss: 0.09535093640172204\n",
            "Epoch: 3591NA_erro: 0.15267579130424086 \n",
            "EU_erro: 0.2131587590358054JP_erro: 0.05227852237316989loss: 0.13937103655911232\n",
            "Epoch: 3592NA_erro: 0.3423155185788177 \n",
            "EU_erro: 0.2086451072101445JP_erro: 0.14759317849033562loss: 0.23285126501275588\n",
            "Epoch: 3593NA_erro: 0.13670997102131216 \n",
            "EU_erro: 0.058777276859727015JP_erro: 0.01728361313657243loss: 0.07092362226441849\n",
            "Epoch: 3594NA_erro: 1.1679183605105379 \n",
            "EU_erro: 0.32644190529520195JP_erro: 0.03520839205083921loss: 0.5098562462385311\n",
            "Epoch: 3595NA_erro: 0.26985434598700947 \n",
            "EU_erro: 0.07589502390040907JP_erro: 0.033254750477251155loss: 0.12633472450019778\n",
            "Epoch: 3596NA_erro: 5.727344483368157 \n",
            "EU_erro: 2.7978004041568254JP_erro: 0.045429306436878765loss: 2.8568581278009932\n",
            "Epoch: 3597NA_erro: 0.2611492400945619 \n",
            "EU_erro: 0.1790977078814839JP_erro: 0.055665503176607824loss: 0.1653041580850764\n",
            "Epoch: 3598NA_erro: 5.741671185160792 \n",
            "EU_erro: 2.7849978069926418JP_erro: 0.05288611548815587loss: 2.8598516301591266\n",
            "Epoch: 3599NA_erro: 0.06691275640975597 \n",
            "EU_erro: 0.022989754066910853JP_erro: 0.02376292334046475loss: 0.03788847793904386\n",
            "Epoch: 3600NA_erro: 0.2300339151722516 \n",
            "EU_erro: 0.1369565956352293JP_erro: 0.059319720711818964loss: 0.1421034151269484\n",
            "Epoch: 3601NA_erro: 0.054830321969912034 \n",
            "EU_erro: 0.024299683958985085JP_erro: 0.010999467714812404loss: 0.030043157496193584\n",
            "Epoch: 3602NA_erro: 0.060491968494977136 \n",
            "EU_erro: 0.03482271996579429JP_erro: 0.02331362676250842loss: 0.039542774821436685\n",
            "Epoch: 3603NA_erro: 0.7264900946801947 \n",
            "EU_erro: 0.10700825572937958JP_erro: 0.07173205068869185loss: 0.30174349629601765\n",
            "Epoch: 3604NA_erro: 0.35989062730656113 \n",
            "EU_erro: 0.1610569066779558JP_erro: 0.04585249941478404loss: 0.18893334292626196\n",
            "Epoch: 3605NA_erro: 0.053955048553703366 \n",
            "EU_erro: 0.03299686566803806JP_erro: 0.014162917238797328loss: 0.033704943435136664\n",
            "Epoch: 3606NA_erro: 0.02966880567314089 \n",
            "EU_erro: 0.016074368889017622JP_erro: 0.014375913743824922loss: 0.020039698412252026\n",
            "Epoch: 3607NA_erro: 0.03938876843267633 \n",
            "EU_erro: 0.02791600633961286JP_erro: 0.012531722931898841loss: 0.026612166286438935\n",
            "Epoch: 3608NA_erro: 0.07801430169926134 \n",
            "EU_erro: 0.02664931291757628JP_erro: 0.05354803199915923loss: 0.05273721476857977\n",
            "Epoch: 3609NA_erro: 0.17386140749436016 \n",
            "EU_erro: 0.05004327888636626JP_erro: 0.01936021008232767loss: 0.08108829343041708\n",
            "Epoch: 3610NA_erro: 0.08863512859788052 \n",
            "EU_erro: 0.03231089244517245JP_erro: 0.011154952668404394loss: 0.04403365674869034\n",
            "Epoch: 3611NA_erro: 0.26889399964680044 \n",
            "EU_erro: 0.20324276399242786JP_erro: 0.042681292045948116loss: 0.17160600470018017\n",
            "Epoch: 3612NA_erro: 0.06576037684152293 \n",
            "EU_erro: 0.04736438740131467JP_erro: 0.03444362287373506loss: 0.049189463142276735\n",
            "Epoch: 3613NA_erro: 0.6282229016917621 \n",
            "EU_erro: 0.2719155577726142JP_erro: 0.02866477698318718loss: 0.3096010888269705\n",
            "Epoch: 3614NA_erro: 0.04799823890360751 \n",
            "EU_erro: 0.027849517127340153JP_erro: 0.020740152329437492loss: 0.032195970993633416\n",
            "Epoch: 3615NA_erro: 0.13851278512052786 \n",
            "EU_erro: 0.028390588686447733JP_erro: 0.008331122555473977loss: 0.05841149840244027\n",
            "Epoch: 3616NA_erro: 0.1809026474176451 \n",
            "EU_erro: 0.07962658423786015JP_erro: 0.019293437863505163loss: 0.09327421816744545\n",
            "Epoch: 3617NA_erro: 0.11664355448050093 \n",
            "EU_erro: 0.20108298737873403JP_erro: 0.09608866632446762loss: 0.13793841812961785\n",
            "Epoch: 3618NA_erro: 0.07648179697435956 \n",
            "EU_erro: 0.03708083269207976JP_erro: 0.009608693247617678loss: 0.04105711168096971\n",
            "Epoch: 3619NA_erro: 0.7180819400521212 \n",
            "EU_erro: 0.315816088240276JP_erro: 0.037154844102933425loss: 0.35701759101808533\n",
            "Epoch: 3620NA_erro: 0.0635716342186743 \n",
            "EU_erro: 0.03553927175758421JP_erro: 0.0355699057726897loss: 0.04489360853683117\n",
            "Epoch: 3621NA_erro: 0.2020075709320778 \n",
            "EU_erro: 0.089164325433184JP_erro: 0.008351864047752794loss: 0.09984125462613365\n",
            "Epoch: 3622NA_erro: 0.022894726243129996 \n",
            "EU_erro: 0.02969944199850393JP_erro: 0.029855555342149363loss: 0.02748324427493783\n",
            "Epoch: 3623NA_erro: 0.26289610899696053 \n",
            "EU_erro: 0.3940916431042575JP_erro: 0.06963097309881403loss: 0.2422062555948893\n",
            "Epoch: 3624NA_erro: 0.22101341291915538 \n",
            "EU_erro: 0.21749718244685684JP_erro: 0.025035909904065984loss: 0.15451551407806632\n",
            "Epoch: 3625NA_erro: 0.09696286778117336 \n",
            "EU_erro: 0.028936888358389683JP_erro: 0.006604578143866487loss: 0.04416810911755229\n",
            "Epoch: 3626NA_erro: 0.1146265425423319 \n",
            "EU_erro: 0.03472529409467712JP_erro: 0.0052016465238822526loss: 0.051517828490382944\n",
            "Epoch: 3627NA_erro: 0.24718449097271114 \n",
            "EU_erro: 0.04344070142553758JP_erro: 0.06101384643436402loss: 0.11721300524334576\n",
            "Epoch: 3628NA_erro: 0.2306325860725817 \n",
            "EU_erro: 0.18212220465490062JP_erro: 0.2209268244662026loss: 0.21122721738593522\n",
            "Epoch: 3629NA_erro: 0.07772624030593754 \n",
            "EU_erro: 0.023981328158415565JP_erro: 0.023541194061900295loss: 0.04174959289935208\n",
            "Epoch: 3630NA_erro: 0.6179207055143607 \n",
            "EU_erro: 0.06099759146224621JP_erro: 0.011662929788116337loss: 0.23019376651261203\n",
            "Epoch: 3631NA_erro: 0.18320723097453745 \n",
            "EU_erro: 0.09649503138638282JP_erro: 0.011383514533671297loss: 0.09702858998793965\n",
            "Epoch: 3632NA_erro: 0.13018831726192504 \n",
            "EU_erro: 0.05991053211596585JP_erro: 0.01212855526643206loss: 0.06740913372631221\n",
            "Epoch: 3633NA_erro: 5.751251693843871 \n",
            "EU_erro: 2.7769041431042574JP_erro: 0.0887628950813944loss: 2.872306468874909\n",
            "Epoch: 3634NA_erro: 0.10345531988513562 \n",
            "EU_erro: 0.22505597550739614JP_erro: 0.0561457918595898loss: 0.12821903524472733\n",
            "Epoch: 3635NA_erro: 0.05421889844790909 \n",
            "EU_erro: 0.08383797120678332JP_erro: 0.012382789868717046loss: 0.05014655201934105\n",
            "Epoch: 3636NA_erro: 0.03301241370134576 \n",
            "EU_erro: 0.031016087809274364JP_erro: 0.052962132202562434loss: 0.03899687944456588\n",
            "Epoch: 3637NA_erro: 0.676858665407166 \n",
            "EU_erro: 0.31041004861048027JP_erro: 0.03710654816886251loss: 0.34145843151003813\n",
            "Epoch: 3638NA_erro: 0.5374927298967228 \n",
            "EU_erro: 0.22066720696382744JP_erro: 0.043595534886500635loss: 0.26725183161654215\n",
            "Epoch: 3639NA_erro: 0.08263993632885837 \n",
            "EU_erro: 0.020423180373140084JP_erro: 0.010966206936873207loss: 0.038009771080904226\n",
            "Epoch: 3640NA_erro: 0.12631359026413555 \n",
            "EU_erro: 0.032613570838011516JP_erro: 0.016073622675829156loss: 0.058333596517873364\n",
            "Epoch: 3641NA_erro: 0.064041628394016 \n",
            "EU_erro: 0.030019484287084536JP_erro: 0.012345134980918826loss: 0.0354687488356302\n",
            "Epoch: 3642NA_erro: 0.7097920706105787 \n",
            "EU_erro: 0.4570866000744723JP_erro: 0.05786230859830398loss: 0.40824697923290637\n",
            "Epoch: 3643NA_erro: 0.23189895836881888 \n",
            "EU_erro: 0.08711228074953538JP_erro: 0.013234808232433112loss: 0.11074868283530538\n",
            "Epoch: 3644NA_erro: 0.2428815715996794 \n",
            "EU_erro: 0.16911948374075483JP_erro: 0.05912449470786161loss: 0.15704184539558352\n",
            "Epoch: 3645NA_erro: 5.673341972883358 \n",
            "EU_erro: 2.794964221096778JP_erro: 0.04908629628114922loss: 2.8391310226085573\n",
            "Epoch: 3646NA_erro: 0.5673819179682769 \n",
            "EU_erro: 0.11004661404809286JP_erro: 0.10605913724086082loss: 0.26116256566010704\n",
            "Epoch: 3647NA_erro: 0.12304262597431508 \n",
            "EU_erro: 0.07177520167920017JP_erro: 0.09969649388808613loss: 0.09817143743352373\n",
            "Epoch: 3648NA_erro: 0.17886599829030592 \n",
            "EU_erro: 0.05277769972187604JP_erro: 0.019075515889382177loss: 0.08357306783513506\n",
            "Epoch: 3649NA_erro: 6.87710961630178 \n",
            "EU_erro: 3.226992880651193JP_erro: 0.08054747137912484loss: 3.3948833258577094\n",
            "Epoch: 3650NA_erro: 0.35071047701576885 \n",
            "EU_erro: 0.2404503304828969JP_erro: 0.15511836192404577loss: 0.24875971698021704\n",
            "Epoch: 3651NA_erro: 0.2515227110810982 \n",
            "EU_erro: 0.06345401438631752JP_erro: 0.020462223725725513loss: 0.11181299076523891\n",
            "Epoch: 3652NA_erro: 0.05962308063063511 \n",
            "EU_erro: 0.048457014468289164JP_erro: 0.007635713663212088loss: 0.03857193298118059\n",
            "Epoch: 3653NA_erro: 5.994964126468629 \n",
            "EU_erro: 2.8210709446160367JP_erro: 0.04366628421369449loss: 2.953233940656795\n",
            "Epoch: 3654NA_erro: 0.21021560181018917 \n",
            "EU_erro: 0.11170884435491045JP_erro: 0.005706423936888229loss: 0.10921029157416764\n",
            "Epoch: 3655NA_erro: 0.2978894692058711 \n",
            "EU_erro: 0.04046040912007177JP_erro: 0.009218361950659937loss: 0.11585608933323113\n",
            "Epoch: 3656NA_erro: 0.04943268705708112 \n",
            "EU_erro: 0.05592903887578683JP_erro: 0.008405585621678553loss: 0.03792243872502053\n",
            "Epoch: 3657NA_erro: 0.046509836995324426 \n",
            "EU_erro: 0.10563981625460839JP_erro: 0.02663102953933006loss: 0.05959356940069864\n",
            "Epoch: 3658NA_erro: 0.3504343550334605 \n",
            "EU_erro: 0.1609553292740223JP_erro: 0.04236607126487318loss: 0.1845852718796841\n",
            "Epoch: 3659NA_erro: 0.1735585974168408 \n",
            "EU_erro: 0.04705696142921152JP_erro: 0.037720614625502004loss: 0.08611205012299293\n",
            "Epoch: 3660NA_erro: 0.08869045464567436 \n",
            "EU_erro: 0.035864204861396964JP_erro: 0.00855231643185135loss: 0.04436899063199065\n",
            "Epoch: 3661NA_erro: 0.20091478214707487 \n",
            "EU_erro: 0.18968072048453397JP_erro: 0.014909741490386253loss: 0.1351684100868166\n",
            "Epoch: 3662NA_erro: 0.7590927449307701 \n",
            "EU_erro: 0.49552207769349566JP_erro: 0.05310561305792757loss: 0.435906824215438\n",
            "Epoch: 3663NA_erro: 0.04669144172077031 \n",
            "EU_erro: 0.03645592419676078JP_erro: 0.03785395113996757loss: 0.0403337746627571\n",
            "Epoch: 3664NA_erro: 0.5287060996358709 \n",
            "EU_erro: 0.10254138199857964JP_erro: 0.07259890090587527loss: 0.23461548856986586\n",
            "Epoch: 3665NA_erro: 0.10124142779860386 \n",
            "EU_erro: 0.03696408382681913JP_erro: 0.03504712452260099loss: 0.05775088025617969\n",
            "Epoch: 3666NA_erro: 0.07145118805789208 \n",
            "EU_erro: 0.035379247147907585JP_erro: 0.01161026400189067loss: 0.03948023614957351\n",
            "Epoch: 3667NA_erro: 0.199201458184294 \n",
            "EU_erro: 0.15477522399074348JP_erro: 0.04817566668340402loss: 0.13405077217161193\n",
            "Epoch: 3668NA_erro: 0.6676133473714193 \n",
            "EU_erro: 0.31915598137434137JP_erro: 0.10014386140098867loss: 0.36230438439420953\n",
            "Epoch: 3669NA_erro: 0.09195388749588368 \n",
            "EU_erro: 0.04749179810516594JP_erro: 0.030558264070703077loss: 0.056667988614518515\n",
            "Epoch: 3670NA_erro: 0.16151437463686447 \n",
            "EU_erro: 0.036800824856573294JP_erro: 0.045618710591811544loss: 0.08131130721217902\n",
            "Epoch: 3671NA_erro: 0.1440983594850052 \n",
            "EU_erro: 0.07024250751317934JP_erro: 0.01903671626896821loss: 0.07779252621554589\n",
            "Epoch: 3672NA_erro: 0.23144989974739016 \n",
            "EU_erro: 0.061582654945610105JP_erro: 0.010780506124792174loss: 0.1012710268183272\n",
            "Epoch: 3673NA_erro: 0.22826063540554786 \n",
            "EU_erro: 0.17494438230529313JP_erro: 0.02846681794454885loss: 0.14389060264409975\n",
            "Epoch: 3674NA_erro: 0.2897892996322277 \n",
            "EU_erro: 0.3884149152179097JP_erro: 0.18638379814088807loss: 0.2881960166517154\n",
            "Epoch: 3675NA_erro: 0.09573333023130431 \n",
            "EU_erro: 0.024202107921127203JP_erro: 0.05207759465358054loss: 0.057337681452433266\n",
            "Epoch: 3676NA_erro: 0.09999859240628028 \n",
            "EU_erro: 0.02644622279692066JP_erro: 0.00812821090221405loss: 0.04485767941142237\n",
            "Epoch: 3677NA_erro: 5.739321449930354 \n",
            "EU_erro: 2.811756873315619JP_erro: 0.1344878451768742loss: 2.8951887382093324\n",
            "Epoch: 3678NA_erro: 0.6897860534431398 \n",
            "EU_erro: 0.1550843549329181JP_erro: 0.07207607576089312loss: 0.30564881110376163\n",
            "Epoch: 3679NA_erro: 0.1406741234683251 \n",
            "EU_erro: 0.013801053512928098JP_erro: 0.015028226514195287loss: 0.05650113257326821\n",
            "Epoch: 3680NA_erro: 0.12251073075819385 \n",
            "EU_erro: 0.019729763500450193JP_erro: 0.055425578309584035loss: 0.06588869778684868\n",
            "Epoch: 3681NA_erro: 0.13594218002733335 \n",
            "EU_erro: 0.038866774980411974JP_erro: 0.07597454758577568loss: 0.0835944977841636\n",
            "Epoch: 3682NA_erro: 1.199565710023392 \n",
            "EU_erro: 0.5252324333486631JP_erro: 0.0569071464760359loss: 0.5939017525014951\n",
            "Epoch: 3683NA_erro: 0.21994993668193966 \n",
            "EU_erro: 0.2170708937238353JP_erro: 0.021546245083328364loss: 0.15285569568013035\n",
            "Epoch: 3684NA_erro: 0.26719268902327664 \n",
            "EU_erro: 0.20275084547294203JP_erro: 0.011929732191470243loss: 0.16062442646470182\n",
            "Epoch: 3685NA_erro: 1.0211016041363856 \n",
            "EU_erro: 0.6064843022546103JP_erro: 0.20036821217499962loss: 0.6093180826468061\n",
            "Epoch: 3686NA_erro: 0.06801492021989453 \n",
            "EU_erro: 0.028033845646436826JP_erro: 0.09380513383436573loss: 0.06328464016433834\n",
            "Epoch: 3687NA_erro: 6.468265740446342 \n",
            "EU_erro: 3.0804863271787184JP_erro: 0.07154540036075799loss: 3.2067658180414242\n",
            "Epoch: 3688NA_erro: 0.27481753696766936 \n",
            "EU_erro: 0.038374824117320454JP_erro: 0.016140446875446527loss: 0.1097776076590368\n",
            "Epoch: 3689NA_erro: 0.7599106869956319 \n",
            "EU_erro: 0.5303261261577754JP_erro: 0.04818891370019247loss: 0.44614189354948297\n",
            "Epoch: 3690NA_erro: 0.05907527698102848 \n",
            "EU_erro: 0.02344472232715104JP_erro: 0.060161795727042264loss: 0.04756060219550318\n",
            "Epoch: 3691NA_erro: 0.20398339559865553 \n",
            "EU_erro: 0.05349677662516749JP_erro: 0.05750256453373635loss: 0.10499425636705502\n",
            "Epoch: 3692NA_erro: 0.08040120435315509 \n",
            "EU_erro: 0.02877277690310811JP_erro: 0.09431745654852815loss: 0.06783048311869304\n",
            "Epoch: 3693NA_erro: 0.565942830817644 \n",
            "EU_erro: 0.23871000422987826JP_erro: 0.02863564925600392loss: 0.27776285659435185\n",
            "Epoch: 3694NA_erro: 0.21584691927414532 \n",
            "EU_erro: 0.05009978316551031JP_erro: 0.09571813797765924loss: 0.12055494988611502\n",
            "Epoch: 3695NA_erro: 0.6697032248326974 \n",
            "EU_erro: 0.3667985191640928JP_erro: 0.06347255651340929loss: 0.366658092469208\n",
            "Epoch: 3696NA_erro: 0.08505206699519194 \n",
            "EU_erro: 0.06197336570237034JP_erro: 0.02069475114807602loss: 0.0559067264083744\n",
            "Epoch: 3697NA_erro: 0.06216124046680539 \n",
            "EU_erro: 0.033657483814298644JP_erro: 0.011757762395134267loss: 0.035858828892079433\n",
            "Epoch: 3698NA_erro: 0.4908356925313787 \n",
            "EU_erro: 0.3475323019101638JP_erro: 0.15313266783721688loss: 0.33050023308096005\n",
            "Epoch: 3699NA_erro: 0.07732303567634996 \n",
            "EU_erro: 0.055984360303065576JP_erro: 0.01009533918181131loss: 0.04780091056528017\n",
            "Epoch: 3700NA_erro: 0.10743934978810392 \n",
            "EU_erro: 0.02614688272624053JP_erro: 0.025477573391078978loss: 0.05302127017531284\n",
            "Epoch: 3701NA_erro: 0.11915820513584817 \n",
            "EU_erro: 0.029073431510333866JP_erro: 0.03550538489984911loss: 0.0612456761589346\n",
            "Epoch: 3702NA_erro: 0.6591280855873759 \n",
            "EU_erro: 0.3205320077349049JP_erro: 0.15561456828154335loss: 0.3784249180047087\n",
            "Epoch: 3703NA_erro: 0.09958962137384932 \n",
            "EU_erro: 0.02577009820198828JP_erro: 0.09205673092095427loss: 0.07247215093568314\n",
            "Epoch: 3704NA_erro: 5.7161375533702765 \n",
            "EU_erro: 2.7807658587315287JP_erro: 0.04721936329390652loss: 2.8480408542840054\n",
            "Epoch: 3705NA_erro: 0.04225792348846909 \n",
            "EU_erro: 0.029022437195445217JP_erro: 0.007645689355310544loss: 0.02630868392397267\n",
            "Epoch: 3706NA_erro: 0.1069873525190723 \n",
            "EU_erro: 0.049698126408480855JP_erro: 0.013300790343173715loss: 0.056662092837252355\n",
            "Epoch: 3707NA_erro: 0.06186240865278614 \n",
            "EU_erro: 0.06585087425024934JP_erro: 0.012295218401176984loss: 0.04666950351508089\n",
            "Epoch: 3708NA_erro: 0.13085480808287628 \n",
            "EU_erro: 0.04523982835370441JP_erro: 0.01084461346153141loss: 0.06231308445449947\n",
            "Epoch: 3709NA_erro: 0.10161410006441811 \n",
            "EU_erro: 0.0671635487282923JP_erro: 0.019524242988852566loss: 0.06276729957077855\n",
            "Epoch: 3710NA_erro: 0.09801801785018092 \n",
            "EU_erro: 0.028845998205879863JP_erro: 0.01003222756607588loss: 0.04563208236250767\n",
            "Epoch: 3711NA_erro: 0.20936786666396975 \n",
            "EU_erro: 0.03026606038559315JP_erro: 0.04316585008488145loss: 0.09426659391831982\n",
            "Epoch: 3712NA_erro: 0.0458196336908858 \n",
            "EU_erro: 0.07935970328574957JP_erro: 0.1790801684061686loss: 0.10141984436863152\n",
            "Epoch: 3713NA_erro: 0.322904438935509 \n",
            "EU_erro: 0.047033766443415205JP_erro: 0.022329600282417712loss: 0.13075593830079071\n",
            "Epoch: 3714NA_erro: 0.2161052245502324 \n",
            "EU_erro: 0.23760845494824787JP_erro: 0.02441610245741615loss: 0.15937657319298087\n",
            "Epoch: 3715NA_erro: 0.0600331190020539 \n",
            "EU_erro: 0.03400471552397854JP_erro: 0.02809979425844296loss: 0.040712541387986766\n",
            "Epoch: 3716NA_erro: 0.07756989131602206 \n",
            "EU_erro: 0.21288138027339018JP_erro: 0.057046233221542loss: 0.11583248774210612\n",
            "Epoch: 3717NA_erro: 0.7137053881504739 \n",
            "EU_erro: 0.4557951845863993JP_erro: 0.06301013536231462loss: 0.41083690732024436\n",
            "Epoch: 3718NA_erro: 0.7413394691408143 \n",
            "EU_erro: 0.32063754029976305JP_erro: 0.0935440932133401loss: 0.38517367932223534\n",
            "Epoch: 3719NA_erro: 0.09143655614335408 \n",
            "EU_erro: 0.07100870442944904JP_erro: 0.031068743199341057loss: 0.06450466407361881\n",
            "Epoch: 3720NA_erro: 0.22084417269211407 \n",
            "EU_erro: 0.04230633724567502JP_erro: 0.0130791095800178loss: 0.09207654368969821\n",
            "Epoch: 3721NA_erro: 0.11228821998418764 \n",
            "EU_erro: 0.10399887728136639JP_erro: 0.008396059852237849loss: 0.07489439128905304\n",
            "Epoch: 3722NA_erro: 0.16121755274691324 \n",
            "EU_erro: 0.06922076376833657JP_erro: 0.008991130793741507loss: 0.07980981538462084\n",
            "Epoch: 3723NA_erro: 0.06677424260812212 \n",
            "EU_erro: 0.04468860090240952JP_erro: 0.029390761556551438loss: 0.04695120707962864\n",
            "Epoch: 3724NA_erro: 0.17923146255256595 \n",
            "EU_erro: 0.19577699114185895JP_erro: 0.01685433031976685loss: 0.1306209268496018\n",
            "Epoch: 3725NA_erro: 0.034285790698472846 \n",
            "EU_erro: 0.020560541356256765JP_erro: 0.03146902766338615loss: 0.02877178811287695\n",
            "Epoch: 3726NA_erro: 0.08288105328877766 \n",
            "EU_erro: 0.18768014833908672JP_erro: 0.01935334515201953loss: 0.09663818418517593\n",
            "Epoch: 3727NA_erro: 0.1290856775387313 \n",
            "EU_erro: 0.05322155102278835JP_erro: 0.009575731070466744loss: 0.06396098654399547\n",
            "Epoch: 3728NA_erro: 0.06832410660825035 \n",
            "EU_erro: 0.035389282906702325JP_erro: 0.023890500382859576loss: 0.042534632276194966\n",
            "Epoch: 3729NA_erro: 0.06856193838193435 \n",
            "EU_erro: 0.03855385290559872JP_erro: 0.014395582121472026loss: 0.04050379575685013\n",
            "Epoch: 3730NA_erro: 0.7845438728036807 \n",
            "EU_erro: 0.47142217325609787JP_erro: 0.08584773817727732loss: 0.4472713322602501\n",
            "Epoch: 3731NA_erro: 0.15295037927553634 \n",
            "EU_erro: 0.04179984100105227JP_erro: 0.033750046593274255loss: 0.07616675639337347\n",
            "Epoch: 3732NA_erro: 0.7081300898115764 \n",
            "EU_erro: 0.32784288243729937JP_erro: 0.03761694412822871loss: 0.35786333010178206\n",
            "Epoch: 3733NA_erro: 0.17874880354533823 \n",
            "EU_erro: 0.029920420443364817JP_erro: 0.008994964088580405loss: 0.0725547380225603\n",
            "Epoch: 3734NA_erro: 0.09339062742484633 \n",
            "EU_erro: 0.03280709880267003JP_erro: 0.009761208942694258loss: 0.04531965237255244\n",
            "Epoch: 3735NA_erro: 0.09727506674537363 \n",
            "EU_erro: 0.024516497009484342JP_erro: 0.01615128429361092loss: 0.04598094973453255\n",
            "Epoch: 3736NA_erro: 0.5749362753343212 \n",
            "EU_erro: 0.2367250309434048JP_erro: 0.08153301800868308loss: 0.29773148455361065\n",
            "Epoch: 3737NA_erro: 0.2502330883528835 \n",
            "EU_erro: 0.04499601763348247JP_erro: 0.01606715626494829loss: 0.10376542113548101\n",
            "Epoch: 3738NA_erro: 0.08306392403536064 \n",
            "EU_erro: 0.21539102229037027JP_erro: 0.06795377694359121loss: 0.12213624725046084\n",
            "Epoch: 3739NA_erro: 0.13802335243816524 \n",
            "EU_erro: 0.02131913059441618JP_erro: 0.014906723138897918loss: 0.05808307218921277\n",
            "Epoch: 3740NA_erro: 5.693315609480983 \n",
            "EU_erro: 2.7848756775375483JP_erro: 0.05906956602436628loss: 2.8457538102024285\n",
            "Epoch: 3741NA_erro: 0.1478727473769077 \n",
            "EU_erro: 0.029880896557209102JP_erro: 0.009802517502806907loss: 0.06251872047897457\n",
            "Epoch: 3742NA_erro: 0.0808471210243166 \n",
            "EU_erro: 0.022493667842805848JP_erro: 0.0196518107902172loss: 0.040997534759284915\n",
            "Epoch: 3743NA_erro: 5.696434198423874 \n",
            "EU_erro: 2.7789833009705065JP_erro: 0.06308845309324043loss: 2.8461689912071524\n",
            "Epoch: 3744NA_erro: 0.05830517572950023 \n",
            "EU_erro: 0.03073720506919447JP_erro: 0.10811058125754659loss: 0.0657176509384037\n",
            "Epoch: 3745NA_erro: 0.0668065584907236 \n",
            "EU_erro: 0.022966903309489407JP_erro: 0.0226813231327737loss: 0.03748493139133897\n",
            "Epoch: 3746NA_erro: 0.09886949561363043 \n",
            "EU_erro: 0.04091570543688397JP_erro: 0.012161953504695448loss: 0.05064905151840328\n",
            "Epoch: 3747NA_erro: 0.05664349526397942 \n",
            "EU_erro: 0.03249097471089326JP_erro: 0.009299790443376053loss: 0.03281142360480257\n",
            "Epoch: 3748NA_erro: 0.7263399464215419 \n",
            "EU_erro: 0.16046592431475026JP_erro: 0.07654742215030877loss: 0.3211177781570789\n",
            "Epoch: 3749NA_erro: 0.08666140164515769 \n",
            "EU_erro: 0.0337614985399468JP_erro: 0.018243050390435743loss: 0.04622197890466498\n",
            "Epoch: 3750NA_erro: 0.0854773484459219 \n",
            "EU_erro: 0.06455139134281365JP_erro: 0.00921778611896574loss: 0.05308217780534611\n",
            "Epoch: 3751NA_erro: 0.3170239278512408 \n",
            "EU_erro: 0.21482674650443617JP_erro: 0.15482956124830616loss: 0.22889342418936795\n",
            "Epoch: 3752NA_erro: 0.3789055624673533 \n",
            "EU_erro: 0.1391144131505212JP_erro: 0.053538000860879585loss: 0.19051932549291803\n",
            "Epoch: 3753NA_erro: 0.33532039139621944 \n",
            "EU_erro: 0.21326704542766245JP_erro: 0.17536976540735524loss: 0.24131907973178598\n",
            "Epoch: 3754NA_erro: 0.8131070100059805 \n",
            "EU_erro: 0.341933013856873JP_erro: 0.049848690513492555loss: 0.4016295884006707\n",
            "Epoch: 3755NA_erro: 0.17399044924004134 \n",
            "EU_erro: 0.02455383539199829JP_erro: 0.009384355688279913loss: 0.06930954234544622\n",
            "Epoch: 3756NA_erro: 0.08207983748857366 \n",
            "EU_erro: 0.03247901681781739JP_erro: 0.012440798125525778loss: 0.042333214782005135\n",
            "Epoch: 3757NA_erro: 0.48703929250554523 \n",
            "EU_erro: 0.23086351941722308JP_erro: 0.15638902205829472loss: 0.2914306174877078\n",
            "Epoch: 3758NA_erro: 0.11995775755061659 \n",
            "EU_erro: 0.07048836050107497JP_erro: 0.1073831458424413loss: 0.09927642437838768\n",
            "Epoch: 3759NA_erro: 0.25278213412262673 \n",
            "EU_erro: 0.1604568496230961JP_erro: 0.05936186905055083loss: 0.1575336345406466\n",
            "Epoch: 3760NA_erro: 0.3150136341420255 \n",
            "EU_erro: 0.19350866938746253JP_erro: 0.15452154847078545loss: 0.22101461425308108\n",
            "Epoch: 3761NA_erro: 0.12303231298461441 \n",
            "EU_erro: 0.02554434907528781JP_erro: 0.10834256807963054loss: 0.0856397503106169\n",
            "Epoch: 3762NA_erro: 0.06216401277586471 \n",
            "EU_erro: 0.030210898366085318JP_erro: 0.046461118284121965loss: 0.046278677245443184\n",
            "Epoch: 3763NA_erro: 0.20375647286111995 \n",
            "EU_erro: 0.05571220734322718JP_erro: 0.010716039543004loss: 0.09006156477817269\n",
            "Epoch: 3764NA_erro: 0.12495978858119758 \n",
            "EU_erro: 0.08846421574437341JP_erro: 0.031814944836520406loss: 0.0817463194677072\n",
            "Epoch: 3765NA_erro: 0.08449302163234977 \n",
            "EU_erro: 0.015160920777062112JP_erro: 0.03537949203520782loss: 0.04501114519991616\n",
            "Epoch: 3766NA_erro: 0.02896219491958618 \n",
            "EU_erro: 0.042284098706504174JP_erro: 0.013022367344346157loss: 0.02808955288672632\n",
            "Epoch: 3767NA_erro: 0.09353387263394142 \n",
            "EU_erro: 0.04739221214324005JP_erro: 0.04044698652370956loss: 0.06045768889345864\n",
            "Epoch: 3768NA_erro: 0.7812299654465313 \n",
            "EU_erro: 0.31629440396331077JP_erro: 0.06551274033479912loss: 0.38767907046532446\n",
            "Epoch: 3769NA_erro: 0.13925488605055697 \n",
            "EU_erro: 0.040555845859438874JP_erro: 0.0172872194486071loss: 0.06569932135500649\n",
            "Epoch: 3770NA_erro: 0.4169194642887559 \n",
            "EU_erro: 0.13344562330911325JP_erro: 0.0578454760617988loss: 0.20273685455322266\n",
            "Epoch: 3771NA_erro: 0.1747331323549729 \n",
            "EU_erro: 0.021558494068855464JP_erro: 0.05985246610271838loss: 0.08538138034731843\n",
            "Epoch: 3772NA_erro: 0.3066636980041977 \n",
            "EU_erro: 0.21516541177912277JP_erro: 0.14376778011174166loss: 0.2218656392060509\n",
            "Epoch: 3773NA_erro: 0.13092156528502472 \n",
            "EU_erro: 0.04322631876598033JP_erro: 0.01571652847667073loss: 0.06328813789426818\n",
            "Epoch: 3774NA_erro: 0.09357170541157094 \n",
            "EU_erro: 0.03499126942582833JP_erro: 0.007496665614519933loss: 0.045353215794230614\n",
            "Epoch: 3775NA_erro: 0.32022627749184307 \n",
            "EU_erro: 0.05471210609110751JP_erro: 0.01101472812105519loss: 0.12865103492441105\n",
            "Epoch: 3776NA_erro: 0.34746044365934625 \n",
            "EU_erro: 0.12667643007381943JP_erro: 0.05134374134300291loss: 0.17516019732453103\n",
            "Epoch: 3777NA_erro: 0.2373496321744697 \n",
            "EU_erro: 0.07389777852583301JP_erro: 0.006522360698197239loss: 0.10592325713283332\n",
            "Epoch: 3778NA_erro: 0.06502984106078628 \n",
            "EU_erro: 0.025495390097300213JP_erro: 0.009116654594739279loss: 0.03321396056995835\n",
            "Epoch: 3779NA_erro: 0.24388270784718122 \n",
            "EU_erro: 0.22393128668615062JP_erro: 0.0358257159706234loss: 0.16787992891415146\n",
            "Epoch: 3780NA_erro: 0.08611778880274573 \n",
            "EU_erro: 0.021091579466827156JP_erro: 0.06245499433473099loss: 0.05655478599459626\n",
            "Epoch: 3781NA_erro: 0.1257242620453354 \n",
            "EU_erro: 0.09799190269884213JP_erro: 0.014472811721092048loss: 0.0793963254884232\n",
            "Epoch: 3782NA_erro: 0.15602498091468514 \n",
            "EU_erro: 0.05879519059676533JP_erro: 0.015922053608783456loss: 0.0769140738849492\n",
            "Epoch: 3783NA_erro: 0.05555141341778659 \n",
            "EU_erro: 0.040610811507055JP_erro: 0.012646058733149092loss: 0.03626942865608274\n",
            "Epoch: 3784NA_erro: 0.09561077568882195 \n",
            "EU_erro: 0.022528797619102536JP_erro: 0.012189799038938774loss: 0.043443127195964486\n",
            "Epoch: 3785NA_erro: 0.13411428577216097 \n",
            "EU_erro: 0.08738091749738353JP_erro: 0.022809281367664188loss: 0.08143483206283214\n",
            "Epoch: 3786NA_erro: 0.24291273235350616 \n",
            "EU_erro: 0.022499196289121642JP_erro: 0.012576129547385283loss: 0.09266270223514055\n",
            "Epoch: 3787NA_erro: 0.07193428139353908 \n",
            "EU_erro: 0.03981013630711755JP_erro: 0.032108807286550835loss: 0.04795107268547827\n",
            "Epoch: 3788NA_erro: 0.23389101028442383 \n",
            "EU_erro: 0.3581391194070032JP_erro: 0.06625531717788341loss: 0.21942850231200225\n",
            "Epoch: 3789NA_erro: 0.729584065518638 \n",
            "EU_erro: 0.17806661221408104JP_erro: 0.04308920960093653loss: 0.31691329423771347\n",
            "Epoch: 3790NA_erro: 0.5035272125125856 \n",
            "EU_erro: 0.20959399467290835JP_erro: 0.05002658958582915loss: 0.25438262510669324\n",
            "Epoch: 3791NA_erro: 0.7080392320026723 \n",
            "EU_erro: 0.32511836798616156JP_erro: 0.03783734731895979loss: 0.3569983327111533\n",
            "Epoch: 3792NA_erro: 0.17614285151163736 \n",
            "EU_erro: 0.03196255874264148JP_erro: 0.007576866782912912loss: 0.07189409677372423\n",
            "Epoch: 3793NA_erro: 0.07741001225257105 \n",
            "EU_erro: 0.04498647626980331JP_erro: 0.025996884634328442loss: 0.049464457718900935\n",
            "Epoch: 3794NA_erro: 0.11860454729361128 \n",
            "EU_erro: 0.02590922884238783JP_erro: 0.006176216542258743loss: 0.050229998522026593\n",
            "Epoch: 3795NA_erro: 0.1084565007409384 \n",
            "EU_erro: 0.050478175629016964JP_erro: 0.015323849380478378loss: 0.05808617717535921\n",
            "Epoch: 3796NA_erro: 0.21988977757535239 \n",
            "EU_erro: 0.06200095941854078JP_erro: 0.03680725199307582loss: 0.10623266530591388\n",
            "Epoch: 3797NA_erro: 0.7780625099359557 \n",
            "EU_erro: 0.051879296007082444JP_erro: 0.007574825670367988loss: 0.2791722061098084\n",
            "Epoch: 3798NA_erro: 0.07879456808400709 \n",
            "EU_erro: 0.06148234356281369JP_erro: 0.04266430703244468loss: 0.06098041238710862\n",
            "Epoch: 3799NA_erro: 0.08986394534739413 \n",
            "EU_erro: 0.03350462793379791JP_erro: 0.02646783294603806loss: 0.04994546720223834\n",
            "Epoch: 3800NA_erro: 0.15469221181647722 \n",
            "EU_erro: 0.07381059864694758JP_erro: 0.02240890680357467loss: 0.08363725603088852\n",
            "Epoch: 3801NA_erro: 0.11305560437283775 \n",
            "EU_erro: 0.01611559543498727JP_erro: 0.07344019967456196loss: 0.06753713585609614\n",
            "Epoch: 3802NA_erro: 0.13760677603788155 \n",
            "EU_erro: 0.0788728950559631JP_erro: 0.008220133162284083loss: 0.07489994052768678\n",
            "Epoch: 3803NA_erro: 0.09473161179890004 \n",
            "EU_erro: 0.05471917547920878JP_erro: 0.03512625084366909loss: 0.06152567937392597\n",
            "Epoch: 3804NA_erro: 0.16435856042906297 \n",
            "EU_erro: 0.04773045233053754JP_erro: 0.00914924539798914loss: 0.07374608239462209\n",
            "Epoch: 3805NA_erro: 0.9352717288704806 \n",
            "EU_erro: 0.4897358251172443JP_erro: 0.17634051714756693loss: 0.5337827519852986\n",
            "Epoch: 3806NA_erro: 0.7041063974069994 \n",
            "EU_erro: 0.257243648055912JP_erro: 0.08550707684006802loss: 0.3489523895027101\n",
            "Epoch: 3807NA_erro: 0.03778985535451608 \n",
            "EU_erro: 0.04281416881916135JP_erro: 0.038951727771019753loss: 0.03985191962515661\n",
            "Epoch: 3808NA_erro: 0.19318822742432587 \n",
            "EU_erro: 0.07822749226592308JP_erro: 0.010638137658437094loss: 0.09401795475981957\n",
            "Epoch: 3809NA_erro: 0.6805063587750575 \n",
            "EU_erro: 0.46266988266346065JP_erro: 0.08049853088319764loss: 0.4078915692115015\n",
            "Epoch: 3810NA_erro: 0.04291405511456867 \n",
            "EU_erro: 0.03040909536125124JP_erro: 0.012827923362569292loss: 0.028717028078182723\n",
            "Epoch: 3811NA_erro: 0.11669426925422609 \n",
            "EU_erro: 0.04026244315066079JP_erro: 0.012915185255597728loss: 0.0566239689671716\n",
            "Epoch: 3812NA_erro: 0.16122350397036056 \n",
            "EU_erro: 0.04279670789260273JP_erro: 0.009598894867786142loss: 0.07120637468589369\n",
            "Epoch: 3813NA_erro: 0.22335797317268313 \n",
            "EU_erro: 0.09299346642900808JP_erro: 0.010309524314348088loss: 0.10888700337373008\n",
            "Epoch: 3814NA_erro: 0.13096083966336508 \n",
            "EU_erro: 0.06769292114316955JP_erro: 0.017210746458334515loss: 0.07195483344469884\n",
            "Epoch: 3815NA_erro: 0.0535179478253505 \n",
            "EU_erro: 0.03266659817954366JP_erro: 0.013864127009413963loss: 0.03334955882656482\n",
            "Epoch: 3816NA_erro: 0.08908303209053453 \n",
            "EU_erro: 0.10154449662496877JP_erro: 0.022945408673249474loss: 0.07119097912958426\n",
            "Epoch: 3817NA_erro: 0.07477161496184594 \n",
            "EU_erro: 0.04859523717747178JP_erro: 0.023798515168271323loss: 0.04905512628629226\n",
            "Epoch: 3818NA_erro: 0.5288994959158491 \n",
            "EU_erro: 0.20285151725591616JP_erro: 0.03163123315618944loss: 0.2544607487759849\n",
            "Epoch: 3819NA_erro: 0.15793853951979053 \n",
            "EU_erro: 0.060935588770134504JP_erro: 0.010383572689322539loss: 0.07641923982043598\n",
            "Epoch: 3820NA_erro: 0.1027144757352134 \n",
            "EU_erro: 0.034326451231342876JP_erro: 0.011695838251779245loss: 0.049578917118930074\n",
            "Epoch: 3821NA_erro: 0.10284620662068211 \n",
            "EU_erro: 0.21378609561180884JP_erro: 0.06392465543377307loss: 0.12685232384260312\n",
            "Epoch: 3822NA_erro: 0.08362259975699492 \n",
            "EU_erro: 0.033941809521165JP_erro: 0.026450616906779682loss: 0.04800501257874245\n",
            "Epoch: 3823NA_erro: 0.08849009986995726 \n",
            "EU_erro: 0.03340115611867387JP_erro: 0.014228938623916271loss: 0.04537340282469757\n",
            "Epoch: 3824NA_erro: 0.5835978190104166 \n",
            "EU_erro: 0.11491152667259985JP_erro: 0.125643439995226loss: 0.274717604467111\n",
            "Epoch: 3825NA_erro: 0.661774199138316 \n",
            "EU_erro: 0.0880411891050117JP_erro: 0.021049763805182406loss: 0.2569550876469575\n",
            "Epoch: 3826NA_erro: 0.2583868374196134 \n",
            "EU_erro: 0.05824724833170573JP_erro: 0.009788850019144457loss: 0.10880765064742214\n",
            "Epoch: 3827NA_erro: 0.9703708619110344 \n",
            "EU_erro: 0.6216674627259721JP_erro: 0.10306602300599564loss: 0.5650348071904145\n",
            "Epoch: 3828NA_erro: 0.07932914319888566 \n",
            "EU_erro: 0.05725364814433016JP_erro: 0.010567136513170346loss: 0.04904997903247212\n",
            "Epoch: 3829NA_erro: 0.2719565140184506 \n",
            "EU_erro: 0.11077577753584514JP_erro: 0.010366235361542813loss: 0.13103285501169604\n",
            "Epoch: 3830NA_erro: 0.0603835989338483 \n",
            "EU_erro: 0.07613672304523084JP_erro: 0.0197880836420281loss: 0.05210280418395996\n",
            "Epoch: 3831NA_erro: 0.2253841244897177 \n",
            "EU_erro: 0.14352446378663528JP_erro: 0.042690861132717875loss: 0.13719981955003369\n",
            "Epoch: 3832NA_erro: 0.6059246285017147 \n",
            "EU_erro: 0.061712805614915JP_erro: 0.03564751194429028loss: 0.23442830226218053\n",
            "Epoch: 3833NA_erro: 0.25505775629087934 \n",
            "EU_erro: 0.10309992834579113JP_erro: 0.00944568378518718loss: 0.12253445248271144\n",
            "Epoch: 3834NA_erro: 0.08275583732959836 \n",
            "EU_erro: 0.03040386493815932JP_erro: 0.01189242730769076loss: 0.04168404627215955\n",
            "Epoch: 3835NA_erro: 0.06208385145941446 \n",
            "EU_erro: 0.029654955679132032JP_erro: 0.007973048229550207loss: 0.03323728724043499\n",
            "Epoch: 3836NA_erro: 0.49316713052202565 \n",
            "EU_erro: 0.2296108608097993JP_erro: 0.028881028641101925loss: 0.2505530128183291\n",
            "Epoch: 3837NA_erro: 0.09335417156071626 \n",
            "EU_erro: 0.09437101755955422JP_erro: 0.022606523924095685loss: 0.07011057332504628\n",
            "Epoch: 3838NA_erro: 0.1091390709544337 \n",
            "EU_erro: 0.19154748251271803JP_erro: 0.011637246654939282loss: 0.10410793067872987\n",
            "Epoch: 3839NA_erro: 0.08308771968811982 \n",
            "EU_erro: 0.049630694611127985JP_erro: 0.02707223328509072loss: 0.05326355150503705\n",
            "Epoch: 3840NA_erro: 1.2355147960574129 \n",
            "EU_erro: 0.6221258178237796JP_erro: 0.09690589683000432loss: 0.6515155466952065\n",
            "Epoch: 3841NA_erro: 0.12307838876118031 \n",
            "EU_erro: 0.044173071550768474JP_erro: 0.00722286139809808loss: 0.058158109354418376\n",
            "Epoch: 3842NA_erro: 0.14408367548802103 \n",
            "EU_erro: 0.19220169200453646JP_erro: 0.02017139695411505loss: 0.11881893919419872\n",
            "Epoch: 3843NA_erro: 0.06775328355242116 \n",
            "EU_erro: 0.04829723243565522JP_erro: 0.04501553468926008loss: 0.053688689719798954\n",
            "Epoch: 3844NA_erro: 0.06547445012617481 \n",
            "EU_erro: 0.027214036893474965JP_erro: 0.015553084685820942loss: 0.036080528137295745\n",
            "Epoch: 3845NA_erro: 1.1140912817430126 \n",
            "EU_erro: 0.6308836530345355JP_erro: 0.12307607850363088loss: 0.6226836803347565\n",
            "Epoch: 3846NA_erro: 0.11340887047523676 \n",
            "EU_erro: 0.24503761483717335JP_erro: 0.06999183532803557loss: 0.1428127843280171\n",
            "Epoch: 3847NA_erro: 0.4931305360424426 \n",
            "EU_erro: 0.20812809005264163JP_erro: 0.03992588483085928loss: 0.24706151134283968\n",
            "Epoch: 3848NA_erro: 0.06883321806441905 \n",
            "EU_erro: 0.020690775194833446JP_erro: 0.014694043139154596loss: 0.034739345081092776\n",
            "Epoch: 3849NA_erro: 0.07065499013708543 \n",
            "EU_erro: 0.06412024183790813JP_erro: 0.10465911192487376loss: 0.07981144180593565\n",
            "Epoch: 3850NA_erro: 0.08542130159777264 \n",
            "EU_erro: 0.03575061411820641JP_erro: 0.010357347800750141loss: 0.043843097464982855\n",
            "Epoch: 3851NA_erro: 0.5262387815371964 \n",
            "EU_erro: 0.10906039282333019JP_erro: 0.10358986928481465loss: 0.24629637252452763\n",
            "Epoch: 3852NA_erro: 0.8893123892850654 \n",
            "EU_erro: 0.7600829397985177JP_erro: 0.21896870561348375loss: 0.6227879930836285\n",
            "Epoch: 3853NA_erro: 0.18345686816429907 \n",
            "EU_erro: 0.18560418786928634JP_erro: 0.06305034308470497loss: 0.14403713765994522\n",
            "Epoch: 3854NA_erro: 0.043614418931709704 \n",
            "EU_erro: 0.02943812183631483JP_erro: 0.025483626265858494loss: 0.03284538901129434\n",
            "Epoch: 3855NA_erro: 0.13459072556606558 \n",
            "EU_erro: 0.22178673929022263JP_erro: 0.010730405879575152loss: 0.12236930603204771\n",
            "Epoch: 3856NA_erro: 0.7681034147277359 \n",
            "EU_erro: 0.3623804565547973JP_erro: 0.035797378351522045loss: 0.3887604188549426\n",
            "Epoch: 3857NA_erro: 0.21583348281623782 \n",
            "EU_erro: 0.037711424420970356JP_erro: 0.014317270166190096loss: 0.08928739747335744\n",
            "Epoch: 3858NA_erro: 0.8250365146370822 \n",
            "EU_erro: 0.6533019487247911JP_erro: 0.1316535842511081loss: 0.5366640312727108\n",
            "Epoch: 3859NA_erro: 0.1883476464323295 \n",
            "EU_erro: 0.17914171366728554JP_erro: 0.04307208597198013loss: 0.13685381689737008\n",
            "Epoch: 3860NA_erro: 0.0714559933935949 \n",
            "EU_erro: 0.03099243770274081JP_erro: 0.009337030639944151loss: 0.037261822889017505\n",
            "Epoch: 3861NA_erro: 0.46290756196014643 \n",
            "EU_erro: 0.4522261139034301JP_erro: 0.09759436282076576loss: 0.33757601597512416\n",
            "Epoch: 3862NA_erro: 0.33078419145687604 \n",
            "EU_erro: 0.1976899398389713JP_erro: 0.13982694278391758loss: 0.22276700929153798\n",
            "Epoch: 3863NA_erro: 0.07686198219772457 \n",
            "EU_erro: 0.07448396017385084JP_erro: 0.00786977047606032loss: 0.05307190640028133\n",
            "Epoch: 3864NA_erro: 0.276659884194071 \n",
            "EU_erro: 0.3900029308112093JP_erro: 0.07188451844592426loss: 0.24618243062218956\n",
            "Epoch: 3865NA_erro: 0.6634760272595309 \n",
            "EU_erro: 0.32390723856844644JP_erro: 0.04628053469251293loss: 0.34455462019572886\n",
            "Epoch: 3866NA_erro: 0.09494571722755137 \n",
            "EU_erro: 0.05809731261674748JP_erro: 0.00932798540407373loss: 0.05412366963172144\n",
            "Epoch: 3867NA_erro: 0.08897111397380977 \n",
            "EU_erro: 0.0373997498852338JP_erro: 0.013434972411902377loss: 0.04660194696382035\n",
            "Epoch: 3868NA_erro: 0.07869549499925717 \n",
            "EU_erro: 0.2072433168573897JP_erro: 0.058156311974044916loss: 0.1146983730700589\n",
            "Epoch: 3869NA_erro: 0.3585778835207917 \n",
            "EU_erro: 0.27132926985274913JP_erro: 0.08883749791818071loss: 0.23958156275194745\n",
            "Epoch: 3870NA_erro: 0.22685599881549215 \n",
            "EU_erro: 0.17665728118068488JP_erro: 0.019803587780442347loss: 0.14110562413237815\n",
            "Epoch: 3871NA_erro: 0.05917362002439277 \n",
            "EU_erro: 0.01765688144883444JP_erro: 0.030690219051154086loss: 0.03584024055983669\n",
            "Epoch: 3872NA_erro: 0.10360731634982796 \n",
            "EU_erro: 0.03305759771849758JP_erro: 0.015848732964937075loss: 0.0508378873499789\n",
            "Epoch: 3873NA_erro: 0.698338368142298 \n",
            "EU_erro: 0.40269674256790516JP_erro: 0.04134416118148686loss: 0.38079306136730107\n",
            "Epoch: 3874NA_erro: 0.19732155541116878 \n",
            "EU_erro: 0.14629701311274093JP_erro: 0.044493947842324424loss: 0.1293708557306334\n",
            "Epoch: 3875NA_erro: 0.17256088035051212 \n",
            "EU_erro: 0.1551427027975866JP_erro: 0.0595484454502431loss: 0.1290840233943259\n",
            "Epoch: 3876NA_erro: 0.07473653601121533 \n",
            "EU_erro: 0.036018955153088235JP_erro: 0.011679304893626723loss: 0.040811600611191386\n",
            "Epoch: 3877NA_erro: 0.6800256773482921 \n",
            "EU_erro: 0.05269034426341685JP_erro: 0.01554186838541844loss: 0.24941928626955018\n",
            "Epoch: 3878NA_erro: 0.5649399202923442 \n",
            "EU_erro: 0.29584808867107065JP_erro: 0.1209784940231678loss: 0.32725552255793133\n",
            "Epoch: 3879NA_erro: 0.07292796475018641 \n",
            "EU_erro: 0.04286518392636794JP_erro: 0.016098891117776088loss: 0.043964011724605116\n",
            "Epoch: 3880NA_erro: 0.07907426634500193 \n",
            "EU_erro: 0.19920040470685146JP_erro: 0.038316882857980655loss: 0.10553052259045978\n",
            "Epoch: 3881NA_erro: 0.230744716733001 \n",
            "EU_erro: 0.06412146165389424JP_erro: 0.022397725157035413loss: 0.10575464529584544\n",
            "Epoch: 3882NA_erro: 0.10236825129782506 \n",
            "EU_erro: 0.21064983781918076JP_erro: 0.05796303582745929loss: 0.12366037036097327\n",
            "Epoch: 3883NA_erro: 0.1956592788992002 \n",
            "EU_erro: 0.1599003363025281JP_erro: 0.05396434503008229loss: 0.13650799906531044\n",
            "Epoch: 3884NA_erro: 5.719544166742369 \n",
            "EU_erro: 2.7830156429793482JP_erro: 0.049320958381475405loss: 2.850627159887506\n",
            "Epoch: 3885NA_erro: 0.06517287834670192 \n",
            "EU_erro: 0.06069955437682396JP_erro: 0.12358912201814873loss: 0.08315385774124501\n",
            "Epoch: 3886NA_erro: 0.136836286663085 \n",
            "EU_erro: 0.031345867833425835JP_erro: 0.009020210583080618loss: 0.05906745444896609\n",
            "Epoch: 3887NA_erro: 0.211691653081613 \n",
            "EU_erro: 0.153744987739149JP_erro: 0.043734242749768636loss: 0.13639029606368192\n",
            "Epoch: 3888NA_erro: 0.05419706651406695 \n",
            "EU_erro: 0.023571500020433764JP_erro: 0.029516150785046956loss: 0.035761571669763376\n",
            "Epoch: 3889NA_erro: 0.08016920829004096 \n",
            "EU_erro: 0.22507155588430952JP_erro: 0.03519193146579949loss: 0.11347756829372672\n",
            "Epoch: 3890NA_erro: 0.9104393064513687 \n",
            "EU_erro: 0.6307503604149634JP_erro: 0.21285607833270878loss: 0.584681976673215\n",
            "Epoch: 3891NA_erro: 0.04071584505628246 \n",
            "EU_erro: 0.01764299333557602JP_erro: 0.012003172968709192loss: 0.023454002631727117\n",
            "Epoch: 3892NA_erro: 0.32076192456622454 \n",
            "EU_erro: 0.19916429076083872JP_erro: 0.1407685113507648loss: 0.2202316032823666\n",
            "Epoch: 3893NA_erro: 0.21670062412587246 \n",
            "EU_erro: 0.07967775182206502JP_erro: 0.010228474703870078loss: 0.10220229163650395\n",
            "Epoch: 3894NA_erro: 0.09989068489666134 \n",
            "EU_erro: 0.07832144581994345JP_erro: 0.01524525903916174loss: 0.06448579851047014\n",
            "Epoch: 3895NA_erro: 0.08908591529195622 \n",
            "EU_erro: 0.032683735670045365JP_erro: 0.07366114808607471loss: 0.0651435981425204\n",
            "Epoch: 3896NA_erro: 0.08295327193977296 \n",
            "EU_erro: 0.0643039673797844JP_erro: 0.02164129600968472loss: 0.05629951177641403\n",
            "Epoch: 3897NA_erro: 0.8107720604238584 \n",
            "EU_erro: 0.6492253236992415JP_erro: 0.10246004245077917loss: 0.5208191391109496\n",
            "Epoch: 3898NA_erro: 0.05656027978704881 \n",
            "EU_erro: 0.024171000303224076JP_erro: 0.009382323816765186loss: 0.03003786931666293\n",
            "Epoch: 3899NA_erro: 0.22069973539012347 \n",
            "EU_erro: 0.11191517807716547JP_erro: 0.010339724001034285loss: 0.11431822481081468\n",
            "Epoch: 3900NA_erro: 0.25917354480240695 \n",
            "EU_erro: 0.12033625166545543JP_erro: 0.054348896640215735loss: 0.14461958131124808\n",
            "Epoch: 3901NA_erro: 0.0520418549692908 \n",
            "EU_erro: 0.02789050109626711JP_erro: 0.040876186171243355loss: 0.04026951715927715\n",
            "Epoch: 3902NA_erro: 0.5350433024325112 \n",
            "EU_erro: 0.11691184931023177JP_erro: 0.07368113181387731loss: 0.2418787350026212\n",
            "Epoch: 3903NA_erro: 0.06713185661522918 \n",
            "EU_erro: 0.08142820624418037JP_erro: 0.023146948611089426loss: 0.05723566972008047\n",
            "Epoch: 3904NA_erro: 0.6769091583961664 \n",
            "EU_erro: 0.3387362457985102JP_erro: 0.04816971083944158loss: 0.3546050167823023\n",
            "Epoch: 3905NA_erro: 0.1281722201857456 \n",
            "EU_erro: 0.036212825035864066JP_erro: 0.024232631043870318loss: 0.06287256414576094\n",
            "Epoch: 3906NA_erro: 0.09733827539192613 \n",
            "EU_erro: 0.04233139891957128JP_erro: 0.04491205825362095loss: 0.061527250349059585\n",
            "Epoch: 3907NA_erro: 0.20938183910162875 \n",
            "EU_erro: 0.09670023770295372JP_erro: 0.09624398031900096loss: 0.13410869494889133\n",
            "Epoch: 3908NA_erro: 0.1099632599557093 \n",
            "EU_erro: 0.028920266055321508JP_erro: 0.01982262661290723loss: 0.05290205626524696\n",
            "Epoch: 3909NA_erro: 0.2677603839903839 \n",
            "EU_erro: 0.12340922688328942JP_erro: 0.05494781704836114loss: 0.14870580776717313\n",
            "Epoch: 3910NA_erro: 0.06857987522154815 \n",
            "EU_erro: 0.035770456920298496JP_erro: 0.025787938010785008loss: 0.043379419533781306\n",
            "Epoch: 3911NA_erro: 0.6302148981611858 \n",
            "EU_erro: 0.06663027659867161JP_erro: 0.011611527712770211loss: 0.2361522534096888\n",
            "Epoch: 3912NA_erro: 0.20078254300494527 \n",
            "EU_erro: 0.04906038443247477JP_erro: 0.02210259899612545loss: 0.09064851805221202\n",
            "Epoch: 3913NA_erro: 0.09109444396440373 \n",
            "EU_erro: 0.07037708463594895JP_erro: 0.015162854462630989loss: 0.05887812884279\n",
            "Epoch: 3914NA_erro: 0.10537567988846654 \n",
            "EU_erro: 0.19775976506314535JP_erro: 0.01866206295730532loss: 0.10726584020511125\n",
            "Epoch: 3915NA_erro: 0.27127572732378347 \n",
            "EU_erro: 0.16471402589664902JP_erro: 0.07653436919515447loss: 0.17084134641543838\n",
            "Epoch: 3916NA_erro: 0.05928988142531048 \n",
            "EU_erro: 0.21765986154245776JP_erro: 0.09886152060456978loss: 0.12527042581129444\n",
            "Epoch: 3917NA_erro: 0.12152994325918745 \n",
            "EU_erro: 0.02758870522181193JP_erro: 0.026423697785813677loss: 0.05851411542227102\n",
            "Epoch: 3918NA_erro: 0.14263428458871769 \n",
            "EU_erro: 0.23524326871531878JP_erro: 0.055043604946875754loss: 0.14430706815202107\n",
            "Epoch: 3919NA_erro: 5.87246621301932 \n",
            "EU_erro: 2.7995773433714874JP_erro: 0.04508723122204921loss: 2.905710205551266\n",
            "Epoch: 3920NA_erro: 0.05737607331238976 \n",
            "EU_erro: 0.03812139348466267JP_erro: 0.022383708824482998loss: 0.03929372443709263\n",
            "Epoch: 3921NA_erro: 0.035165987273519356 \n",
            "EU_erro: 0.08437543876411378JP_erro: 0.018729177094245143loss: 0.04609020181404528\n",
            "Epoch: 3922NA_erro: 0.16040059023125228 \n",
            "EU_erro: 0.24578268213789592JP_erro: 0.09945434193278468loss: 0.16854587451432101\n",
            "Epoch: 3923NA_erro: 0.14342369959335918 \n",
            "EU_erro: 0.018184305161468742JP_erro: 0.006763206202854481loss: 0.05612374276153801\n",
            "Epoch: 3924NA_erro: 0.09562888810800951 \n",
            "EU_erro: 0.04919242489245511JP_erro: 0.020112737204677374loss: 0.05497802135556243\n",
            "Epoch: 3925NA_erro: 0.08434616318044737 \n",
            "EU_erro: 0.1914190137109091JP_erro: 0.009336292512657107loss: 0.09503383045048677\n",
            "Epoch: 3926NA_erro: 0.07098563419756039 \n",
            "EU_erro: 0.060352498246717824JP_erro: 0.011675513760988103loss: 0.04767121270645496\n",
            "Epoch: 3927NA_erro: 0.19018844116565792 \n",
            "EU_erro: 0.05335239938987318JP_erro: 0.01160985508630442loss: 0.08505023727121279\n",
            "Epoch: 3928NA_erro: 0.5586844082026519 \n",
            "EU_erro: 0.13769120208976804JP_erro: 0.16027149303938992loss: 0.285549052925997\n",
            "Epoch: 3929NA_erro: 0.7306925455729166 \n",
            "EU_erro: 0.6391508265059124JP_erro: 0.09200130322182826loss: 0.48728155535320905\n",
            "Epoch: 3930NA_erro: 0.059794353884319924 \n",
            "EU_erro: 0.02693191748256831JP_erro: 0.02651675264964732loss: 0.03774767929269362\n",
            "Epoch: 3931NA_erro: 0.1557604656663052 \n",
            "EU_erro: 0.09965467822644138JP_erro: 0.01003292064334071loss: 0.08848269780476888\n",
            "Epoch: 3932NA_erro: 0.06540317868077478 \n",
            "EU_erro: 0.028848763584166534JP_erro: 0.019581361796504767loss: 0.03794443237689114\n",
            "Epoch: 3933NA_erro: 0.15306712121002433 \n",
            "EU_erro: 0.09442810864411583JP_erro: 0.0087270423654438loss: 0.08540743081144585\n",
            "Epoch: 3934NA_erro: 0.6607098542442618 \n",
            "EU_erro: 0.07675978102425272JP_erro: 0.011743601671485015loss: 0.24973773217016412\n",
            "Epoch: 3935NA_erro: 0.10602750519449397 \n",
            "EU_erro: 0.03174032120741615JP_erro: 0.009670631252517995loss: 0.04914615505425505\n",
            "Epoch: 3936NA_erro: 0.19933051841203556 \n",
            "EU_erro: 0.07670991180479064JP_erro: 0.022505427053732464loss: 0.09951528652693874\n",
            "Epoch: 3937NA_erro: 0.0781049839285917 \n",
            "EU_erro: 0.026596264783726183JP_erro: 0.024754839350086773loss: 0.04315203012422074\n",
            "Epoch: 3938NA_erro: 0.14341730480046236 \n",
            "EU_erro: 0.02630847138027812JP_erro: 0.01950633387232936loss: 0.06307737309803334\n",
            "Epoch: 3939NA_erro: 0.11048275370930516 \n",
            "EU_erro: 0.050714182299236917JP_erro: 0.029444846996041232loss: 0.06354726100152777\n",
            "Epoch: 3940NA_erro: 0.04420918087626612 \n",
            "EU_erro: 0.028324291225551634JP_erro: 0.011706918246986331loss: 0.02808013165643973\n",
            "Epoch: 3941NA_erro: 0.09241735288339067 \n",
            "EU_erro: 0.033047809157260626JP_erro: 0.09541085524152415loss: 0.07362534371457359\n",
            "Epoch: 3942NA_erro: 0.0522956413816112 \n",
            "EU_erro: 0.05858013334200364JP_erro: 0.03853879926740661loss: 0.0498048695482949\n",
            "Epoch: 3943NA_erro: 0.07858621057613875 \n",
            "EU_erro: 0.04085258458011834JP_erro: 0.012332787809445876loss: 0.043923859448395955\n",
            "Epoch: 3944NA_erro: 0.8630272769188696 \n",
            "EU_erro: 0.45677532521329184JP_erro: 0.07025587004284527loss: 0.46335283175919406\n",
            "Epoch: 3945NA_erro: 0.4026541968648748 \n",
            "EU_erro: 0.270122114078019JP_erro: 0.06430719711983851loss: 0.24569450422774913\n",
            "Epoch: 3946NA_erro: 0.1864635667135549 \n",
            "EU_erro: 0.21535590637561886JP_erro: 0.07974343706471052loss: 0.16052096389060796\n",
            "Epoch: 3947NA_erro: 0.2684681914573492 \n",
            "EU_erro: 0.06748352401940398JP_erro: 0.04788043887116188loss: 0.1279440683911937\n",
            "Epoch: 3948NA_erro: 0.07116783496945403 \n",
            "EU_erro: 0.03805096297301063JP_erro: 0.023174641668334488loss: 0.044131149617276454\n",
            "Epoch: 3949NA_erro: 0.25190564089043194 \n",
            "EU_erro: 0.02859419399453688JP_erro: 0.00987407079962797loss: 0.0967912988145222\n",
            "Epoch: 3950NA_erro: 0.658791933872903 \n",
            "EU_erro: 0.3380710394807564JP_erro: 0.03594102554543074loss: 0.3442680373672367\n",
            "Epoch: 3951NA_erro: 0.23572391317796337 \n",
            "EU_erro: 0.15815646149391352JP_erro: 0.05562077659045079loss: 0.14983371246692745\n",
            "Epoch: 3952NA_erro: 0.053071148635805115 \n",
            "EU_erro: 0.038807433704997216JP_erro: 0.06481765314590099loss: 0.05223208157591117\n",
            "Epoch: 3953NA_erro: 0.09122906729232433 \n",
            "EU_erro: 0.20977140397064445JP_erro: 0.05745888680450676loss: 0.11948645576950191\n",
            "Epoch: 3954NA_erro: 0.10818899139877437 \n",
            "EU_erro: 0.045556047166040704JP_erro: 0.011400217695753704loss: 0.055048419523608776\n",
            "Epoch: 3955NA_erro: 0.08250465688779372 \n",
            "EU_erro: 0.026731470296549244JP_erro: 0.018774853196254996loss: 0.04267032756361851\n",
            "Epoch: 3956NA_erro: 0.5978592791298564 \n",
            "EU_erro: 0.04996841730073441JP_erro: 0.008967603130858074loss: 0.2189317784568136\n",
            "Epoch: 3957NA_erro: 0.06798112115194631 \n",
            "EU_erro: 0.05803606768911199JP_erro: 0.011164564494938814loss: 0.04572725111199904\n",
            "Epoch: 3958NA_erro: 0.08291537447493205 \n",
            "EU_erro: 0.04017337348109992JP_erro: 0.0827120163644007loss: 0.06860025169313416\n",
            "Epoch: 3959NA_erro: 0.04831068737562313 \n",
            "EU_erro: 0.04094692163689192JP_erro: 0.013260181791098544loss: 0.034172597319580784\n",
            "Epoch: 3960NA_erro: 0.7062078816022059 \n",
            "EU_erro: 0.35364509552948237JP_erro: 0.035424639088238855loss: 0.36509251409722854\n",
            "Epoch: 3961NA_erro: 0.12710639672685964 \n",
            "EU_erro: 0.2451222257096638JP_erro: 0.011077262172403261loss: 0.12776862743288972\n",
            "Epoch: 3962NA_erro: 0.32091532566750697 \n",
            "EU_erro: 0.2068781113439752JP_erro: 0.0499003016671469loss: 0.19256458726040152\n",
            "Epoch: 3963NA_erro: 0.09954126306282457 \n",
            "EU_erro: 0.07863265599391257JP_erro: 0.09560962980107744loss: 0.09126118911329166\n",
            "Epoch: 3964NA_erro: 0.12742115545642468 \n",
            "EU_erro: 0.030112758625385372JP_erro: 0.058789428814437036loss: 0.07210777711498645\n",
            "Epoch: 3965NA_erro: 0.12906103171119393 \n",
            "EU_erro: 0.02716358317885288JP_erro: 0.008797858112542203loss: 0.055007495621378105\n",
            "Epoch: 3966NA_erro: 0.0727093265962231 \n",
            "EU_erro: 0.022379869176435842JP_erro: 0.02306680808695712loss: 0.03938533538995787\n",
            "Epoch: 3967NA_erro: 0.06275276343027751 \n",
            "EU_erro: 0.020187806713488674JP_erro: 0.04637090734733167loss: 0.04310382429019425\n",
            "Epoch: 3968NA_erro: 0.08885314298230548 \n",
            "EU_erro: 0.1929890647415043JP_erro: 0.038472069788348766loss: 0.10677142660747203\n",
            "Epoch: 3969NA_erro: 0.2111048846281776 \n",
            "EU_erro: 0.1806484636410262JP_erro: 0.023005875044090802loss: 0.13825309368991112\n",
            "Epoch: 3970NA_erro: 0.11185777279757714 \n",
            "EU_erro: 0.02466240363527638JP_erro: 0.02078871625338414loss: 0.05243629910225092\n",
            "Epoch: 3971NA_erro: 0.08881828581639963 \n",
            "EU_erro: 0.04674306673596996JP_erro: 0.01461994393851406loss: 0.05006043023841326\n",
            "Epoch: 3972NA_erro: 0.056006053621454756 \n",
            "EU_erro: 0.033633598061495046JP_erro: 0.022057706071424855loss: 0.0372324548950491\n",
            "Epoch: 3973NA_erro: 0.09839893311493156 \n",
            "EU_erro: 0.03896537215210671JP_erro: 0.009131568462349648loss: 0.04883196002753206\n",
            "Epoch: 3974NA_erro: 0.7629407099051069 \n",
            "EU_erro: 0.22831325383149376JP_erro: 0.11753365975017696loss: 0.3695958898973095\n",
            "Epoch: 3975NA_erro: 0.06140401659085769 \n",
            "EU_erro: 0.08612605028374251JP_erro: 0.10093331336975098loss: 0.08282112520794536\n",
            "Epoch: 3976NA_erro: 0.06719405798949013 \n",
            "EU_erro: 0.07130429264186888JP_erro: 0.01882652903712073loss: 0.05244163117667501\n",
            "Epoch: 3977NA_erro: 0.647501317105552 \n",
            "EU_erro: 0.4513084056765534JP_erro: 0.04880800727725953loss: 0.3825392094693443\n",
            "Epoch: 3978NA_erro: 0.25044335875400275 \n",
            "EU_erro: 0.05772767602935318JP_erro: 0.03721484842226487loss: 0.11512863543606544\n",
            "Epoch: 3979NA_erro: 0.27532228203706965 \n",
            "EU_erro: 0.16383359598559002JP_erro: 0.04122548787168754loss: 0.1601271296656409\n",
            "Epoch: 3980NA_erro: 0.057949922805608706 \n",
            "EU_erro: 0.036286069441211315JP_erro: 0.012463518353395684loss: 0.035566505073576934\n",
            "Epoch: 3981NA_erro: 0.21148324936859367 \n",
            "EU_erro: 0.0673316338265589JP_erro: 0.020719549452611643loss: 0.09984480318172957\n",
            "Epoch: 3982NA_erro: 0.06451147009235944 \n",
            "EU_erro: 0.030030231605204502JP_erro: 0.0187236324761265loss: 0.037755111391230146\n",
            "Epoch: 3983NA_erro: 0.08006038591843243 \n",
            "EU_erro: 0.02695624449456385JP_erro: 0.012156027694081152loss: 0.03972421782885411\n",
            "Epoch: 3984NA_erro: 0.06713899993157202 \n",
            "EU_erro: 0.08142132167668306JP_erro: 0.00931693717490795loss: 0.052625754082849786\n",
            "Epoch: 3985NA_erro: 0.04136463930440504 \n",
            "EU_erro: 0.030038786488910053JP_erro: 0.012575255114902822loss: 0.027992896331373113\n",
            "Epoch: 3986NA_erro: 0.053847681644350984 \n",
            "EU_erro: 0.02649146226025367JP_erro: 0.04373702429985815loss: 0.04135872504507848\n",
            "Epoch: 3987NA_erro: 0.07561280283817025 \n",
            "EU_erro: 0.057275235190872076JP_erro: 0.018148696006730546loss: 0.050345580707224766\n",
            "Epoch: 3988NA_erro: 0.06246213303055874 \n",
            "EU_erro: 0.02853113089420999JP_erro: 0.01102920881537504loss: 0.03400748783303786\n",
            "Epoch: 3989NA_erro: 0.161108323769976 \n",
            "EU_erro: 0.20101094430731248JP_erro: 0.012370522401129553loss: 0.124829933624859\n",
            "Epoch: 3990NA_erro: 0.6890517981477486 \n",
            "EU_erro: 0.3196990212728811JP_erro: 0.034375786781311035loss: 0.3477089164793029\n",
            "Epoch: 3991NA_erro: 0.6054369978202406 \n",
            "EU_erro: 0.06160702354224153JP_erro: 0.009995658499325892loss: 0.2256799113842868\n",
            "Epoch: 3992NA_erro: 0.14583777087603428 \n",
            "EU_erro: 0.03419765899347704JP_erro: 0.00915873247061589loss: 0.06306472674820775\n",
            "Epoch: 3993NA_erro: 0.14882607053416644 \n",
            "EU_erro: 0.11385660762934721JP_erro: 0.013675370881723803loss: 0.09211935738260432\n",
            "Epoch: 3994NA_erro: 0.37076033363046573 \n",
            "EU_erro: 0.15507892120716182JP_erro: 0.05542628062787906loss: 0.1937551831090173\n",
            "Epoch: 3995NA_erro: 0.13025890025057535 \n",
            "EU_erro: 0.04976175090139227JP_erro: 0.007631866506827894loss: 0.062550846920457\n",
            "Epoch: 3996NA_erro: 0.13271139573681262 \n",
            "EU_erro: 0.02165977113930754JP_erro: 0.04226595856422602loss: 0.06554570771002954\n",
            "Epoch: 3997NA_erro: 0.08273726285890091 \n",
            "EU_erro: 0.030379161354183226JP_erro: 0.009958359391190285loss: 0.041024929793306096\n",
            "Epoch: 3998NA_erro: 0.10763939036879429 \n",
            "EU_erro: 0.03445255663967872JP_erro: 0.05780680697093638loss: 0.06663291953330816\n",
            "Epoch: 3999NA_erro: 0.07596832375193752 \n",
            "EU_erro: 0.041026292845260263JP_erro: 0.030701555484949156loss: 0.049232059670973195\n",
            "Epoch: 4000NA_erro: 0.06204872630363287 \n",
            "EU_erro: 0.024822077085805493JP_erro: 0.021479728147965067loss: 0.03611684307571529\n",
            "Epoch: 4001NA_erro: 0.07835027783416039 \n",
            "EU_erro: 0.2079413473144058JP_erro: 0.1488311808238658loss: 0.1450409445651742\n",
            "Epoch: 4002NA_erro: 0.059305429458618164 \n",
            "EU_erro: 0.05237198615258978JP_erro: 0.08479455644770185loss: 0.06549065889314164\n",
            "Epoch: 4003NA_erro: 0.05978790826575701 \n",
            "EU_erro: 0.028325180674708166JP_erro: 0.010901316422824712loss: 0.03300480371297792\n",
            "Epoch: 4004NA_erro: 0.3212775370871374 \n",
            "EU_erro: 0.05173329235047333JP_erro: 0.03306812556215035loss: 0.1353596624477889\n",
            "Epoch: 4005NA_erro: 0.23660894512206085 \n",
            "EU_erro: 0.03974803196367367JP_erro: 0.010840193938839343loss: 0.09573239688725435\n",
            "Epoch: 4006NA_erro: 0.1618695443914842 \n",
            "EU_erro: 0.21799481192300485JP_erro: 0.055265606835831045loss: 0.14504331950993501\n",
            "Epoch: 4007NA_erro: 0.309668455937112 \n",
            "EU_erro: 0.12798197509706483JP_erro: 0.05130576994992042loss: 0.16298539878786072\n",
            "Epoch: 4008NA_erro: 0.14818272849386052 \n",
            "EU_erro: 0.04292639188988264JP_erro: 0.011335679495981497loss: 0.06748160188512285\n",
            "Epoch: 4009NA_erro: 0.224003089490787 \n",
            "EU_erro: 0.24764434311741082JP_erro: 0.043039428171261336loss: 0.1715622761452845\n",
            "Epoch: 4010NA_erro: 0.6279606042906295 \n",
            "EU_erro: 0.23976590282233187JP_erro: 0.059439687765845954loss: 0.30905542447585466\n",
            "Epoch: 4011NA_erro: 0.17495281012483346 \n",
            "EU_erro: 0.09211262991262037JP_erro: 0.09460703347080437loss: 0.1205575004104496\n",
            "Epoch: 4012NA_erro: 0.08963964706243471 \n",
            "EU_erro: 0.055860507395840434JP_erro: 0.07156176160472308loss: 0.0723539720209994\n",
            "Epoch: 4013NA_erro: 0.128499380377836 \n",
            "EU_erro: 0.042334337567174155JP_erro: 0.008765987532083378loss: 0.05986657253531522\n",
            "Epoch: 4014NA_erro: 0.16801316608754238 \n",
            "EU_erro: 0.06715832292571548JP_erro: 0.09209221647691357loss: 0.10908790337022885\n",
            "Epoch: 4015NA_erro: 0.09950193323830302 \n",
            "EU_erro: 0.09737224541893301JP_erro: 0.10509449382160985loss: 0.10065623032030209\n",
            "Epoch: 4016NA_erro: 0.06368379260218421 \n",
            "EU_erro: 0.08148830990458644JP_erro: 0.10302451229834741loss: 0.08273220801538275\n",
            "Epoch: 4017NA_erro: 0.11141256768574086 \n",
            "EU_erro: 0.22196185681246972JP_erro: 0.07772743239883305loss: 0.13703396154004474\n",
            "Epoch: 4018NA_erro: 0.06040268628172172 \n",
            "EU_erro: 0.10434877964877343JP_erro: 0.007659397268480109loss: 0.057470290235770764\n",
            "Epoch: 4019NA_erro: 0.09006773778634478 \n",
            "EU_erro: 0.02524806316508803JP_erro: 0.03539072450741317loss: 0.05023551079653954\n",
            "Epoch: 4020NA_erro: 0.15709056410678598 \n",
            "EU_erro: 0.04129181998644688JP_erro: 0.00931462402953658loss: 0.06923234277917434\n",
            "Epoch: 4021NA_erro: 0.6877053430838178 \n",
            "EU_erro: 0.4948160260222679JP_erro: 0.06180575651715892loss: 0.41477573010348534\n",
            "Epoch: 4022NA_erro: 0.2041899695876957 \n",
            "EU_erro: 0.2502705884534259JP_erro: 0.0365382571553075loss: 0.16366627789282984\n",
            "Epoch: 4023NA_erro: 0.17740530561107073 \n",
            "EU_erro: 0.048749493074047474JP_erro: 0.06444339142289272loss: 0.09686606798985208\n",
            "Epoch: 4024NA_erro: 0.0805219199306281 \n",
            "EU_erro: 0.04234211851460065JP_erro: 0.014394653397937154loss: 0.04575289959131285\n",
            "Epoch: 4025NA_erro: 0.5434872752936312 \n",
            "EU_erro: 0.220675505408945JP_erro: 0.035976132219151934loss: 0.26671298714571223\n",
            "Epoch: 4026NA_erro: 0.10210177695104318 \n",
            "EU_erro: 0.05023715107939964JP_erro: 0.03291303566259931loss: 0.061750656874604924\n",
            "Epoch: 4027NA_erro: 0.35613825154858963 \n",
            "EU_erro: 0.19788658896157907JP_erro: 0.14146674511044524loss: 0.23183055626329527\n",
            "Epoch: 4028NA_erro: 0.8336753253788911 \n",
            "EU_erro: 0.4858418028484019JP_erro: 0.05759029887443365loss: 0.459035829056141\n",
            "Epoch: 4029NA_erro: 0.11937878852666811 \n",
            "EU_erro: 0.06914703882941904JP_erro: 0.00950139275816984loss: 0.06600907144620437\n",
            "Epoch: 4030NA_erro: 0.13422273850256158 \n",
            "EU_erro: 0.19348691600237705JP_erro: 0.04744107871092567loss: 0.1250502490258032\n",
            "Epoch: 4031NA_erro: 0.342704972555471 \n",
            "EU_erro: 0.46243039212485615JP_erro: 0.06275726381198381loss: 0.28929754745128544\n",
            "Epoch: 4032NA_erro: 0.07452350716258205 \n",
            "EU_erro: 0.0249732118244319JP_erro: 0.0342039798581323loss: 0.04456690577573554\n",
            "Epoch: 4033NA_erro: 0.0755836427673813 \n",
            "EU_erro: 0.030102487220320592JP_erro: 0.0172264227109362loss: 0.04097085128458895\n",
            "Epoch: 4034NA_erro: 0.0404827308285144 \n",
            "EU_erro: 0.035090527331182196JP_erro: 0.009124895283418108loss: 0.02823271954706473\n",
            "Epoch: 4035NA_erro: 0.0822868920111841 \n",
            "EU_erro: 0.03702679114748341JP_erro: 0.01472626430119655loss: 0.04467998364175013\n",
            "Epoch: 4036NA_erro: 0.08408598197523014 \n",
            "EU_erro: 0.056355586347653884JP_erro: 0.01389859258666519loss: 0.05144672338352647\n",
            "Epoch: 4037NA_erro: 0.20310525746308555 \n",
            "EU_erro: 0.0443696190220441JP_erro: 0.024152617583903233loss: 0.09054250495378362\n",
            "Epoch: 4038NA_erro: 0.10671142459839814 \n",
            "EU_erro: 0.07453709147697271JP_erro: 0.009842070267181988loss: 0.063696868659914\n",
            "Epoch: 4039NA_erro: 0.06382636321607486 \n",
            "EU_erro: 0.022803505723790606JP_erro: 0.013748649940934292loss: 0.03345950629359992\n",
            "Epoch: 4040NA_erro: 0.26047590167023416 \n",
            "EU_erro: 0.1382249824760496JP_erro: 0.05501602047173552loss: 0.15123897744703663\n",
            "Epoch: 4041NA_erro: 0.133662131405616 \n",
            "EU_erro: 0.017529170411501743JP_erro: 0.07429238437682159loss: 0.07516123065652773\n",
            "Epoch: 4042NA_erro: 0.21114874255749605 \n",
            "EU_erro: 0.08657176365223966JP_erro: 0.010150351034578427loss: 0.10262361792630928\n",
            "Epoch: 4043NA_erro: 5.826932330464208 \n",
            "EU_erro: 2.8087713552075764JP_erro: 0.046065154001694315loss: 2.8939233824264172\n",
            "Epoch: 4044NA_erro: 0.19000776054323182 \n",
            "EU_erro: 0.053126142006511834JP_erro: 0.0206605639568595loss: 0.08793149807656458\n",
            "Epoch: 4045NA_erro: 0.08637615876604421 \n",
            "EU_erro: 0.014603337807248729JP_erro: 0.0261482180551041loss: 0.04237590834151867\n",
            "Epoch: 4046NA_erro: 0.08611064548640288 \n",
            "EU_erro: 0.1885766576426898JP_erro: 0.014438344988712045loss: 0.09637523067089938\n",
            "Epoch: 4047NA_erro: 0.30225639195405235 \n",
            "EU_erro: 0.21284543266592099JP_erro: 0.14444533244583957loss: 0.2198490615963012\n",
            "Epoch: 4048NA_erro: 0.06596924722656723 \n",
            "EU_erro: 0.04787910354229831JP_erro: 0.009267893872519797loss: 0.041038745133451716\n",
            "Epoch: 4049NA_erro: 0.3352991370267646 \n",
            "EU_erro: 0.05209544370340746JP_erro: 0.019853209802346637loss: 0.13574925503989524\n",
            "Epoch: 4050NA_erro: 0.1101999042570129 \n",
            "EU_erro: 0.02299984296162923JP_erro: 0.008217869109885638loss: 0.04713920659797136\n",
            "Epoch: 4051NA_erro: 0.2990451524423998 \n",
            "EU_erro: 0.13302509174790494JP_erro: 0.06866967493249465loss: 0.16691331715546837\n",
            "Epoch: 4052NA_erro: 0.669133607731309 \n",
            "EU_erro: 0.46467888262844825JP_erro: 0.061935805535131644loss: 0.3985827837803567\n",
            "Epoch: 4053NA_erro: 0.05216370719347813 \n",
            "EU_erro: 0.04104956637981326JP_erro: 0.03380709609319998loss: 0.04234012707259304\n",
            "Epoch: 4054NA_erro: 0.3159389200136643 \n",
            "EU_erro: 0.09079196656397147JP_erro: 0.01203585387200348loss: 0.1395889308101447\n",
            "Epoch: 4055NA_erro: 0.34252695334974187 \n",
            "EU_erro: 0.1743716602177583JP_erro: 0.04143185855806336loss: 0.18611015275467274\n",
            "Epoch: 4056NA_erro: 0.6480263554772665 \n",
            "EU_erro: 0.4434591485548389JP_erro: 0.10164203569870586loss: 0.3977092107137044\n",
            "Epoch: 4057NA_erro: 0.09940072547557742 \n",
            "EU_erro: 0.1178578535715739JP_erro: 0.019805814868720004loss: 0.07902147233948227\n",
            "Epoch: 4058NA_erro: 0.13895626215971718 \n",
            "EU_erro: 0.07042336371517921JP_erro: 0.013998549344927766loss: 0.07445940213610036\n",
            "Epoch: 4059NA_erro: 0.10234274605447932 \n",
            "EU_erro: 0.027253318202587987JP_erro: 0.028994866581850274loss: 0.052863645923230076\n",
            "Epoch: 4060NA_erro: 5.745340983072917 \n",
            "EU_erro: 2.785049261048783JP_erro: 0.05918077720228092loss: 2.8631905252619307\n",
            "Epoch: 4061NA_erro: 0.5950233917827754 \n",
            "EU_erro: 0.09793872981108437JP_erro: 0.026923575142557308loss: 0.23996192355488621\n",
            "Epoch: 4062NA_erro: 0.06760912810185159 \n",
            "EU_erro: 0.03685405781102735JP_erro: 0.03972027190895968loss: 0.048061151837193686\n",
            "Epoch: 4063NA_erro: 0.1157497897628666 \n",
            "EU_erro: 0.03669244143389916JP_erro: 0.02664913733800252loss: 0.059697129005609556\n",
            "Epoch: 4064NA_erro: 0.17683624297149422 \n",
            "EU_erro: 0.10462165802948234JP_erro: 0.08897897809050805loss: 0.12347895969716154\n",
            "Epoch: 4065NA_erro: 0.05818300468977108 \n",
            "EU_erro: 0.07124740485997164JP_erro: 0.010532900806545287loss: 0.04665444063585858\n",
            "Epoch: 4066NA_erro: 0.7712654734766761 \n",
            "EU_erro: 0.21173904108446698JP_erro: 0.19847733105799947loss: 0.3938272757123607\n",
            "Epoch: 4067NA_erro: 0.08842851764471955 \n",
            "EU_erro: 0.034886891989744914JP_erro: 0.011664799941602603loss: 0.044993407042451604\n",
            "Epoch: 4068NA_erro: 0.5896437814993452 \n",
            "EU_erro: 0.1053664296172386JP_erro: 0.07320905378622602loss: 0.2560730867607649\n",
            "Epoch: 4069NA_erro: 0.0620220844135728 \n",
            "EU_erro: 0.028761955656746562JP_erro: 0.018965896478919095loss: 0.03658331179803656\n",
            "Epoch: 4070NA_erro: 0.3309023287869239 \n",
            "EU_erro: 0.14321095259614694JP_erro: 0.053219402483267376loss: 0.1757775720699813\n",
            "Epoch: 4071NA_erro: 0.0850718150767245 \n",
            "EU_erro: 0.08585709934086763JP_erro: 0.011104862819346346loss: 0.06067793036616126\n",
            "Epoch: 4072NA_erro: 0.1133915343025858 \n",
            "EU_erro: 0.09348039479218712JP_erro: 0.021094719106836836loss: 0.07598888042361238\n",
            "Epoch: 4073NA_erro: 1.1319140500800555 \n",
            "EU_erro: 0.5312830533168112JP_erro: 0.16173190848771915loss: 0.6083097014316293\n",
            "Epoch: 4074NA_erro: 0.062327495841092845 \n",
            "EU_erro: 0.02721984257069669JP_erro: 0.010021808304527932loss: 0.03318971672723459\n",
            "Epoch: 4075NA_erro: 6.331199823423874 \n",
            "EU_erro: 3.2578667929006175JP_erro: 0.14304569525312083loss: 3.244037776030311\n",
            "Epoch: 4076NA_erro: 0.5422573311384334 \n",
            "EU_erro: 0.22363320801609246JP_erro: 0.0317037914150445loss: 0.2658647714659225\n",
            "Epoch: 4077NA_erro: 0.08451294529345608 \n",
            "EU_erro: 0.01817007397496423JP_erro: 0.04035625808922819loss: 0.04767975603887277\n",
            "Epoch: 4078NA_erro: 0.5903058458668317 \n",
            "EU_erro: 0.04597922628240068JP_erro: 0.03447694602862809loss: 0.2235873540242513\n",
            "Epoch: 4079NA_erro: 0.19473820693733157 \n",
            "EU_erro: 0.15535637389781864JP_erro: 0.04955524159956348loss: 0.133216612098753\n",
            "Epoch: 4080NA_erro: 0.10893934456877007 \n",
            "EU_erro: 0.08227125618808953JP_erro: 0.02950400807136713loss: 0.07357153781624727\n",
            "Epoch: 4081NA_erro: 0.11031789373057757 \n",
            "EU_erro: 0.03511882105539011JP_erro: 0.015246786119401917loss: 0.05356116812358531\n",
            "Epoch: 4082NA_erro: 0.7830365498860677 \n",
            "EU_erro: 0.31397545984549113JP_erro: 0.047964915748714476loss: 0.3816589458968288\n",
            "Epoch: 4083NA_erro: 0.3320538350778033 \n",
            "EU_erro: 0.21938153939653737JP_erro: 0.15657421230345733loss: 0.2360032140746597\n",
            "Epoch: 4084NA_erro: 0.6938828608786413 \n",
            "EU_erro: 0.3210624059041341JP_erro: 0.03678791975790216loss: 0.35057770189388776\n",
            "Epoch: 4085NA_erro: 0.19617847693982973 \n",
            "EU_erro: 0.1620067552078602JP_erro: 0.04184465537699618loss: 0.13334328814070354\n",
            "Epoch: 4086NA_erro: 0.23406137791714926 \n",
            "EU_erro: 0.023652941219566403JP_erro: 0.011717620515084081loss: 0.0898106430852136\n",
            "Epoch: 4087NA_erro: 0.10481996129649554 \n",
            "EU_erro: 0.04423839639323626JP_erro: 0.02114492562390113loss: 0.05673442700112513\n",
            "Epoch: 4088NA_erro: 0.057884755060654276 \n",
            "EU_erro: 0.027969130711962085JP_erro: 0.01347337235776029loss: 0.033109086428501806\n",
            "Epoch: 4089NA_erro: 0.4297008810117263 \n",
            "EU_erro: 0.3292022010152654JP_erro: 0.17307242681813795loss: 0.31065852143043693\n",
            "Epoch: 4090NA_erro: 0.5624795514483785 \n",
            "EU_erro: 0.10701930800149607JP_erro: 0.06991317567899245loss: 0.2464706953181777\n",
            "Epoch: 4091NA_erro: 0.07807045019874277 \n",
            "EU_erro: 0.04700918992360433JP_erro: 0.013218031142109125loss: 0.046099230300548466\n",
            "Epoch: 4092NA_erro: 0.10713124460028123 \n",
            "EU_erro: 0.20121424697166265JP_erro: 0.04828034445296887loss: 0.11887528175531431\n",
            "Epoch: 4093NA_erro: 0.10120952776236128 \n",
            "EU_erro: 0.03407357968101206JP_erro: 0.018484230882437655loss: 0.05125578521758087\n",
            "Epoch: 4094NA_erro: 0.11469195055407147 \n",
            "EU_erro: 0.04211673902910809JP_erro: 0.013031365797501202loss: 0.056613355643989505\n",
            "Epoch: 4095NA_erro: 0.08551618849584298 \n",
            "EU_erro: 0.026072917520537857JP_erro: 0.013483701519263808loss: 0.04169093546017196\n",
            "Epoch: 4096NA_erro: 0.06381448387175567 \n",
            "EU_erro: 0.03395211558009303JP_erro: 0.05166592524033184loss: 0.049810839253802634\n",
            "Epoch: 4097NA_erro: 0.7192178474840267 \n",
            "EU_erro: 0.46221215595570647JP_erro: 0.043052772218866865loss: 0.40816092675970506\n",
            "Epoch: 4098NA_erro: 0.11333533959795339 \n",
            "EU_erro: 0.211174395657325JP_erro: 0.06380281707113103loss: 0.12943753900454025\n",
            "Epoch: 4099NA_erro: 0.07642802341963893 \n",
            "EU_erro: 0.03540056158405866JP_erro: 0.008620944365050441loss: 0.040149845818216484\n",
            "Epoch: 4100NA_erro: 0.09892976561258006 \n",
            "EU_erro: 0.020352301671523458JP_erro: 0.0113683049530946loss: 0.043550129084624065\n",
            "Epoch: 4101NA_erro: 0.233424375223559 \n",
            "EU_erro: 0.039175078850383904JP_erro: 0.07582362770110138loss: 0.11614103649937829\n",
            "Epoch: 4102NA_erro: 5.829373145288275 \n",
            "EU_erro: 2.823864545008933JP_erro: 0.058149108591005784loss: 2.9037957598072612\n",
            "Epoch: 4103NA_erro: 0.200538857038631 \n",
            "EU_erro: 0.033751742322315544JP_erro: 0.00689796179301979loss: 0.08039619571478791\n",
            "Epoch: 4104NA_erro: 0.7017463122227395 \n",
            "EU_erro: 0.45714751694553585JP_erro: 0.060633430185244065loss: 0.40650911109392035\n",
            "Epoch: 4105NA_erro: 0.6275888191637142 \n",
            "EU_erro: 0.25895612184391464JP_erro: 0.027545812980149142loss: 0.3046969110651534\n",
            "Epoch: 4106NA_erro: 0.08088776307512624 \n",
            "EU_erro: 0.03356819467027058JP_erro: 0.016806623501371045loss: 0.04375418951345045\n",
            "Epoch: 4107NA_erro: 0.07106636383736781 \n",
            "EU_erro: 0.04166118396345035JP_erro: 0.015580560578856357loss: 0.04276936830476273\n",
            "Epoch: 4108NA_erro: 0.06650316160778667 \n",
            "EU_erro: 0.033034638378971304JP_erro: 0.01062477050825607loss: 0.0367208622222723\n",
            "Epoch: 4109NA_erro: 0.6251000988391019 \n",
            "EU_erro: 0.04335271757702495JP_erro: 0.03369256276492925loss: 0.23404845895693283\n",
            "Epoch: 4110NA_erro: 0.052464049915934716 \n",
            "EU_erro: 0.027406192565149114JP_erro: 0.018265273912932523loss: 0.032711839953134224\n",
            "Epoch: 4111NA_erro: 0.07042109966278076 \n",
            "EU_erro: 0.021845395712889442JP_erro: 0.009940474532371344loss: 0.034068991509518885\n",
            "Epoch: 4112NA_erro: 0.07504884586777798 \n",
            "EU_erro: 0.10678414226502411JP_erro: 0.09081745332525681loss: 0.09088347124498944\n",
            "Epoch: 4113NA_erro: 0.09450759998587675 \n",
            "EU_erro: 0.031002531217974284JP_erro: 0.031307924163433934loss: 0.05227268204208492\n",
            "Epoch: 4114NA_erro: 0.20550535630810168 \n",
            "EU_erro: 0.07455849370291066JP_erro: 0.010668827119723771loss: 0.0969108962273413\n",
            "Epoch: 4115NA_erro: 0.09919064913609231 \n",
            "EU_erro: 0.02808580795923869JP_erro: 0.042301014412281124loss: 0.05652582460595656\n",
            "Epoch: 4116NA_erro: 0.15781267668849738 \n",
            "EU_erro: 0.06942379382229591JP_erro: 0.02804153880407644loss: 0.08509267208188079\n",
            "Epoch: 4117NA_erro: 0.07233220477436864 \n",
            "EU_erro: 0.0811065906702086JP_erro: 0.04443849242010782loss: 0.06595909595489502\n",
            "Epoch: 4118NA_erro: 0.17762147178945614 \n",
            "EU_erro: 0.02961648296016131JP_erro: 0.01086048724115357loss: 0.07269948027854742\n",
            "Epoch: 4119NA_erro: 0.0544297926185667 \n",
            "EU_erro: 0.03038575251897176JP_erro: 0.015733269758002703loss: 0.033516271631847054\n",
            "Epoch: 4120NA_erro: 0.17181782759437264 \n",
            "EU_erro: 0.02950487210769062JP_erro: 0.01360423112100409loss: 0.07164231566495674\n",
            "Epoch: 4121NA_erro: 0.2661268840464511 \n",
            "EU_erro: 0.08798105772151503JP_erro: 0.008572865017624788loss: 0.12089360591977141\n",
            "Epoch: 4122NA_erro: 0.13268963311069695 \n",
            "EU_erro: 0.026456526545591132JP_erro: 0.028109289416971134loss: 0.06241848302441974\n",
            "Epoch: 4123NA_erro: 0.703817559767139 \n",
            "EU_erro: 0.4721406448719113JP_erro: 0.04649862762569457loss: 0.4074856292369754\n",
            "Epoch: 4124NA_erro: 0.09846967320109523 \n",
            "EU_erro: 0.07901997529259024JP_erro: 0.00786322205103645loss: 0.06178429422452468\n",
            "Epoch: 4125NA_erro: 0.24085612260093986 \n",
            "EU_erro: 0.22825128348298776JP_erro: 0.05975926569265912loss: 0.17628889675288237\n",
            "Epoch: 4126NA_erro: 0.11146774587705154 \n",
            "EU_erro: 0.06607162860012794JP_erro: 0.09269465771756431loss: 0.09007802305295486\n",
            "Epoch: 4127NA_erro: 0.3253602611926175 \n",
            "EU_erro: 0.20727414493412935JP_erro: 0.14867918435917343loss: 0.22710450859956963\n",
            "Epoch: 4128NA_erro: 0.09889876195626665 \n",
            "EU_erro: 0.10874598525291265JP_erro: 0.011031543330628743loss: 0.072892100311989\n",
            "Epoch: 4129NA_erro: 0.0809348091598629 \n",
            "EU_erro: 0.026951129584349403JP_erro: 0.008702547859775927loss: 0.03886282906051754\n",
            "Epoch: 4130NA_erro: 0.1037895494653273 \n",
            "EU_erro: 0.047701195228931514JP_erro: 0.013130355712979339loss: 0.054873703986175304\n",
            "Epoch: 4131NA_erro: 0.08314523586007051 \n",
            "EU_erro: 0.045044745585715126JP_erro: 0.025794235772864764loss: 0.05132806855578755\n",
            "Epoch: 4132NA_erro: 0.06848399029221645 \n",
            "EU_erro: 0.02754941467166871JP_erro: 0.010256166605986366loss: 0.035429860270300574\n",
            "Epoch: 4133NA_erro: 0.10513855505359265 \n",
            "EU_erro: 0.23951962936756221JP_erro: 0.025969314020733502loss: 0.12354251026183136\n",
            "Epoch: 4134NA_erro: 0.22828503172526987 \n",
            "EU_erro: 0.20538971405620723JP_erro: 0.01771512188652689loss: 0.15046330385429915\n",
            "Epoch: 4135NA_erro: 0.21202322124510772 \n",
            "EU_erro: 0.03069366133490274JP_erro: 0.01808416589285976loss: 0.08693367935890375\n",
            "Epoch: 4136NA_erro: 0.5035068822461505 \n",
            "EU_erro: 0.2127928142399751JP_erro: 0.03286300010459368loss: 0.24972089501314385\n",
            "Epoch: 4137NA_erro: 0.10203534318495167 \n",
            "EU_erro: 0.03726036280624626JP_erro: 0.011570058589757876loss: 0.05028858665348023\n",
            "Epoch: 4138NA_erro: 0.16071275223133175 \n",
            "EU_erro: 0.027092369489891584JP_erro: 0.007582544240840646loss: 0.06512922834056292\n",
            "Epoch: 4139NA_erro: 0.13900926870893138 \n",
            "EU_erro: 0.2520795385966929JP_erro: 0.05571189314820046loss: 0.1489335806794869\n",
            "Epoch: 4140NA_erro: 1.1998953560526058 \n",
            "EU_erro: 0.6235482711200566JP_erro: 0.11828611063402752loss: 0.6472433075424313\n",
            "Epoch: 4141NA_erro: 0.08308113083358883 \n",
            "EU_erro: 0.18881672112516654JP_erro: 0.012973799955013186loss: 0.09495722231014754\n",
            "Epoch: 4142NA_erro: 0.46796961348186167 \n",
            "EU_erro: 0.27223378188850345JP_erro: 0.1573911378549975loss: 0.29919816542041394\n",
            "Epoch: 4143NA_erro: 0.657929634863092 \n",
            "EU_erro: 0.34551661143931306JP_erro: 0.15519118124200393loss: 0.38621248200882313\n",
            "Epoch: 4144NA_erro: 0.1455239454905192 \n",
            "EU_erro: 0.052811799123305685JP_erro: 0.013433850781862126loss: 0.07058986397676689\n",
            "Epoch: 4145NA_erro: 0.6081755956013998 \n",
            "EU_erro: 0.08932578471279884JP_erro: 0.09246355803437935loss: 0.26332163995550584\n",
            "Epoch: 4146NA_erro: 0.1867500386496847 \n",
            "EU_erro: 0.1689079380774683JP_erro: 0.04531989263933758loss: 0.13365930365037548\n",
            "Epoch: 4147NA_erro: 0.2147597120713818 \n",
            "EU_erro: 0.03792728102484415JP_erro: 0.012455359678859858loss: 0.08838078772374826\n",
            "Epoch: 4148NA_erro: 0.5231308012969734 \n",
            "EU_erro: 0.2083343683287155JP_erro: 0.026747702166091566loss: 0.2527376293211944\n",
            "Epoch: 4149NA_erro: 0.2107699527296909 \n",
            "EU_erro: 0.034909587959910546JP_erro: 0.01159925331441007loss: 0.08575959723125133\n",
            "Epoch: 4150NA_erro: 0.07825173148813173 \n",
            "EU_erro: 0.07354340349981027JP_erro: 0.01171811491019966loss: 0.054504418557928515\n",
            "Epoch: 4151NA_erro: 0.14846375746320384 \n",
            "EU_erro: 0.09827949279962583JP_erro: 0.006301314678303031loss: 0.08434818696606067\n",
            "Epoch: 4152NA_erro: 0.5849424258683079 \n",
            "EU_erro: 0.24964171786640965JP_erro: 0.028529386649760166loss: 0.2877045232196187\n",
            "Epoch: 4153NA_erro: 5.677629810895106 \n",
            "EU_erro: 2.790881312170694JP_erro: 0.13099928234898767loss: 2.8665033976236978\n",
            "Epoch: 4154NA_erro: 0.14309630837551382 \n",
            "EU_erro: 0.022202508394108263JP_erro: 0.011556008758470994loss: 0.05895161351492239\n",
            "Epoch: 4155NA_erro: 0.08985206138255984 \n",
            "EU_erro: 0.03399029027583987JP_erro: 0.020807872908984042loss: 0.04821674768314805\n",
            "Epoch: 4156NA_erro: 0.0420767346093821 \n",
            "EU_erro: 0.02218175766079925JP_erro: 0.010603616635004679loss: 0.0249540366867716\n",
            "Epoch: 4157NA_erro: 0.06538610125696936 \n",
            "EU_erro: 0.2182247657184453JP_erro: 0.060230091560718624loss: 0.11461366054623626\n",
            "Epoch: 4158NA_erro: 0.25217913842016415 \n",
            "EU_erro: 0.12354666872542033JP_erro: 0.05923044773959374loss: 0.1449854244557462\n",
            "Epoch: 4159NA_erro: 0.06982810737550721 \n",
            "EU_erro: 0.044793722241423854JP_erro: 0.009902820799701898loss: 0.04150821719058724\n",
            "Epoch: 4160NA_erro: 0.08885486181392226 \n",
            "EU_erro: 0.04110916178355845JP_erro: 0.025262855744177058loss: 0.05174229311388592\n",
            "Epoch: 4161NA_erro: 0.1806203154630439 \n",
            "EU_erro: 0.038446183352507365JP_erro: 0.025468727414922197loss: 0.08151174515716789\n",
            "Epoch: 4162NA_erro: 0.04202479077864063 \n",
            "EU_erro: 0.057400460391081584JP_erro: 0.009820550218109013loss: 0.0364152659741483\n",
            "Epoch: 4163NA_erro: 0.21547019943710446 \n",
            "EU_erro: 0.05574158919874088JP_erro: 0.09118321330048317loss: 0.12079832165740258\n",
            "Epoch: 4164NA_erro: 0.6998883476553037 \n",
            "EU_erro: 0.5010322082874387JP_erro: 0.12059731076854144loss: 0.44050597405248837\n",
            "Epoch: 4165NA_erro: 0.5254236487455146 \n",
            "EU_erro: 0.20800451899683753JP_erro: 0.11380675227143043loss: 0.282411630763564\n",
            "Epoch: 4166NA_erro: 0.15641225400821182 \n",
            "EU_erro: 0.14432820238808328JP_erro: 0.01613779585490855loss: 0.10562609332476476\n",
            "Epoch: 4167NA_erro: 0.20198816476866258 \n",
            "EU_erro: 0.15530687894007955JP_erro: 0.042175096134806786loss: 0.13315672098204148\n",
            "Epoch: 4168NA_erro: 0.6156902535017147 \n",
            "EU_erro: 0.336182949154876JP_erro: 0.07507955843164016loss: 0.34231758856958194\n",
            "Epoch: 4169NA_erro: 0.14800721360731495 \n",
            "EU_erro: 0.21405077719873236JP_erro: 0.08841028509213943loss: 0.15015609504640565\n",
            "Epoch: 4170NA_erro: 0.24213739143785581 \n",
            "EU_erro: 0.22992086040881254JP_erro: 0.011534835248030433loss: 0.16119770301404848\n",
            "Epoch: 4171NA_erro: 0.08621661237968031 \n",
            "EU_erro: 0.1920171301494273JP_erro: 0.01165903700414554loss: 0.0966309392175009\n",
            "Epoch: 4172NA_erro: 0.16637417697167212 \n",
            "EU_erro: 0.05930312382158383JP_erro: 0.0161438198514687loss: 0.08060704830080964\n",
            "Epoch: 4173NA_erro: 0.09525933931040209 \n",
            "EU_erro: 0.04603694575701573JP_erro: 0.010380463082661loss: 0.05055891450985458\n",
            "Epoch: 4174NA_erro: 0.6900229195291682 \n",
            "EU_erro: 0.46198911445085394JP_erro: 0.0477791895238004loss: 0.399930384732032\n",
            "Epoch: 4175NA_erro: 0.7001847259758055 \n",
            "EU_erro: 0.34893760385439376JP_erro: 0.05479971105738204loss: 0.3679740521334863\n",
            "Epoch: 4176NA_erro: 0.6981051984683487 \n",
            "EU_erro: 0.6542885211087013JP_erro: 0.13414977132812028loss: 0.4955145000487335\n",
            "Epoch: 4177NA_erro: 5.835248370503271 \n",
            "EU_erro: 2.8236267917840054JP_erro: 0.05298459714697313loss: 2.9039530791053476\n",
            "Epoch: 4178NA_erro: 0.5649630598319594 \n",
            "EU_erro: 0.23794599281725035JP_erro: 0.07379148357598357loss: 0.2922335151554078\n",
            "Epoch: 4179NA_erro: 0.06224731604258219 \n",
            "EU_erro: 0.08235886115436405JP_erro: 0.022184017092682595loss: 0.055596731429876284\n",
            "Epoch: 4180NA_erro: 0.16193960988244346 \n",
            "EU_erro: 0.0346880365711774JP_erro: 0.01037147040514983loss: 0.06899969984394635\n",
            "Epoch: 4181NA_erro: 0.1511399505674377 \n",
            "EU_erro: 0.04319883594217226JP_erro: 0.011197792929272319loss: 0.06851219391637994\n",
            "Epoch: 4182NA_erro: 0.06523917349734047 \n",
            "EU_erro: 0.03394085538479709JP_erro: 0.035803632218708366loss: 0.044994557550711224\n",
            "Epoch: 4183NA_erro: 0.07986891177273536 \n",
            "EU_erro: 0.02909320962521457JP_erro: 0.015774634919425313loss: 0.041578920312630115\n",
            "Epoch: 4184NA_erro: 0.2220162125520928 \n",
            "EU_erro: 0.1178264248278714JP_erro: 0.024825149728346242loss: 0.12155593827713368\n",
            "Epoch: 4185NA_erro: 0.060002840766611026 \n",
            "EU_erro: 0.028468474399211796JP_erro: 0.02080148735711741loss: 0.036424269047818446\n",
            "Epoch: 4186NA_erro: 0.2013948536658472 \n",
            "EU_erro: 0.25268617526505344JP_erro: 0.01600880262463592loss: 0.15669661898945653\n",
            "Epoch: 4187NA_erro: 0.31550473945085394 \n",
            "EU_erro: 0.06527347158091937JP_erro: 0.008140517066615496loss: 0.12963958488878355\n",
            "Epoch: 4188NA_erro: 0.0450999376385711 \n",
            "EU_erro: 0.20594731781833855JP_erro: 0.05964100822921871loss: 0.10356275610221449\n",
            "Epoch: 4189NA_erro: 0.19682599592578504 \n",
            "EU_erro: 0.04983437615771626JP_erro: 0.008957056227580522loss: 0.08520581001459165\n",
            "Epoch: 4190NA_erro: 0.21967738173728765 \n",
            "EU_erro: 0.17654356106307156JP_erro: 0.0485474333282589loss: 0.14825613923775133\n",
            "Epoch: 4191NA_erro: 0.7926354667013006 \n",
            "EU_erro: 0.4659711482912995JP_erro: 0.04775866057521613loss: 0.43545512265937275\n",
            "Epoch: 4192NA_erro: 0.12566011081370274 \n",
            "EU_erro: 0.07066022980120755JP_erro: 0.010435962399771048loss: 0.0689187669014746\n",
            "Epoch: 4193NA_erro: 0.5218902114749879 \n",
            "EU_erro: 0.20686620889708054JP_erro: 0.043140580487805745loss: 0.25729900182679644\n",
            "Epoch: 4194NA_erro: 0.1947539536527885 \n",
            "EU_erro: 0.01630392416503078JP_erro: 0.015479344730229341loss: 0.07551241752713225\n",
            "Epoch: 4195NA_erro: 0.0505343287490135 \n",
            "EU_erro: 0.02550902061684187JP_erro: 0.01777108210001805loss: 0.03127147754033407\n",
            "Epoch: 4196NA_erro: 0.06122614986212679 \n",
            "EU_erro: 0.03289570873097856JP_erro: 0.02591472956561303loss: 0.040012196052906124\n",
            "Epoch: 4197NA_erro: 0.08261103038639986 \n",
            "EU_erro: 0.03569204446881316JP_erro: 0.021080522574195567loss: 0.04646120145339375\n",
            "Epoch: 4198NA_erro: 0.3258065290229265 \n",
            "EU_erro: 0.036244126715401344JP_erro: 0.01047240555748459loss: 0.12417435646057129\n",
            "Epoch: 4199NA_erro: 0.11730878852134527 \n",
            "EU_erro: 0.08240452108457107JP_erro: 0.011403930279635644loss: 0.070372413295184\n",
            "Epoch: 4200NA_erro: 0.08435956267423408 \n",
            "EU_erro: 0.03637538168781487JP_erro: 0.010886399089828018loss: 0.043873782305754436\n",
            "Epoch: 4201NA_erro: 0.6611040396283764 \n",
            "EU_erro: 0.07595448068870131JP_erro: 0.016004617593085118loss: 0.251021052515784\n",
            "Epoch: 4202NA_erro: 0.05805165268654047 \n",
            "EU_erro: 0.01801796315252319JP_erro: 0.01609173856040304loss: 0.030720453391703526\n",
            "Epoch: 4203NA_erro: 1.0190782620925312 \n",
            "EU_erro: 0.48624256784601727JP_erro: 0.2099482772886291loss: 0.5717563259509183\n",
            "Epoch: 4204NA_erro: 0.10877209116322126 \n",
            "EU_erro: 0.044389371724091756JP_erro: 0.025936281958291698loss: 0.059699245201524836\n",
            "Epoch: 4205NA_erro: 0.0782992026602575 \n",
            "EU_erro: 0.03518265809199607JP_erro: 0.014311131811881251loss: 0.042597660722658616\n",
            "Epoch: 4206NA_erro: 0.14315260473147842 \n",
            "EU_erro: 0.0788194079731786JP_erro: 0.10581792787063954loss: 0.10926331660544225\n",
            "Epoch: 4207NA_erro: 0.5124149396438007 \n",
            "EU_erro: 0.2117018182148305JP_erro: 0.029230399649272595loss: 0.25111573611119\n",
            "Epoch: 4208NA_erro: 0.08991041848825854 \n",
            "EU_erro: 0.030627183673917785JP_erro: 0.010110928799754889loss: 0.043549514556115915\n",
            "Epoch: 4209NA_erro: 0.3429711881534074 \n",
            "EU_erro: 0.2597836745801822JP_erro: 0.19466937974441884loss: 0.26580810546875\n",
            "Epoch: 4210NA_erro: 0.8817775489747987 \n",
            "EU_erro: 0.4168496760287026JP_erro: 0.08067046949105669loss: 0.45976590740588286\n",
            "Epoch: 4211NA_erro: 5.784246163774831 \n",
            "EU_erro: 2.811132327530735JP_erro: 0.1723096721856169loss: 2.922562887502271\n",
            "Epoch: 4212NA_erro: 0.05994780119075332 \n",
            "EU_erro: 0.03646796756936598JP_erro: 0.04391936368720476loss: 0.046778376712355504\n",
            "Epoch: 4213NA_erro: 0.038112485131552055 \n",
            "EU_erro: 0.02491637486820073JP_erro: 0.02962241570154826loss: 0.030883760877357898\n",
            "Epoch: 4214NA_erro: 0.06714905417242716 \n",
            "EU_erro: 0.04073702087698057JP_erro: 0.01946316902027574loss: 0.04244975341382877\n",
            "Epoch: 4215NA_erro: 0.5523957762607309 \n",
            "EU_erro: 0.20645171172859134JP_erro: 0.02868284319722375loss: 0.26251011116560113\n",
            "Epoch: 4216NA_erro: 0.05404081455496854 \n",
            "EU_erro: 0.03379610388777977JP_erro: 0.021394026833911273loss: 0.03641031740247741\n",
            "Epoch: 4217NA_erro: 0.07616764815278755 \n",
            "EU_erro: 0.2167466629383176JP_erro: 0.0564546548118887loss: 0.11645631272663441\n",
            "Epoch: 4218NA_erro: 0.09168945541677549 \n",
            "EU_erro: 0.05191227724385816JP_erro: 0.10524249816125678loss: 0.08294807848080184\n",
            "Epoch: 4219NA_erro: 0.053520641585653145 \n",
            "EU_erro: 0.03145073504410973JP_erro: 0.012071316325387289loss: 0.03234756547351216\n",
            "Epoch: 4220NA_erro: 0.13101366139197534 \n",
            "EU_erro: 0.02921323674593785JP_erro: 0.011321247317070184loss: 0.05718271861704745\n",
            "Epoch: 4221NA_erro: 0.7097066834915516 \n",
            "EU_erro: 0.3144439800765163JP_erro: 0.06094342516374218loss: 0.3616980619208757\n",
            "Epoch: 4222NA_erro: 0.09739205818767696 \n",
            "EU_erro: 0.024769220703332JP_erro: 0.009283054937687956loss: 0.04381478294845699\n",
            "Epoch: 4223NA_erro: 0.7677752472633539 \n",
            "EU_erro: 0.3316147013228069JP_erro: 0.03715348567149436loss: 0.37884782451067783\n",
            "Epoch: 4224NA_erro: 0.08551474689513214 \n",
            "EU_erro: 0.08432047311649767JP_erro: 0.1169080142826997loss: 0.09558107501776643\n",
            "Epoch: 4225NA_erro: 0.7782534665839617 \n",
            "EU_erro: 0.5057676078737244JP_erro: 0.06883444712143535loss: 0.4509518867315248\n",
            "Epoch: 4226NA_erro: 0.17523512359737425 \n",
            "EU_erro: 0.25422393813613775JP_erro: 0.022064687669739243loss: 0.1505079195480938\n",
            "Epoch: 4227NA_erro: 0.11373048604920853 \n",
            "EU_erro: 0.02069108245908752JP_erro: 0.01011205042979514loss: 0.048177879910136376\n",
            "Epoch: 4228NA_erro: 0.4859403491944306 \n",
            "EU_erro: 0.21594925444255503JP_erro: 0.042059768077939055loss: 0.24798315624858058\n",
            "Epoch: 4229NA_erro: 0.515704450681228 \n",
            "EU_erro: 0.25845298471376876JP_erro: 0.08213080177011416loss: 0.2854294185490571\n",
            "Epoch: 4230NA_erro: 0.15591035517611246 \n",
            "EU_erro: 0.0751556860384091JP_erro: 0.011168125756951265loss: 0.0807447304097257\n",
            "Epoch: 4231NA_erro: 0.1853281213331592 \n",
            "EU_erro: 0.05442544933437377JP_erro: 0.011265734138414842loss: 0.08367311122805574\n",
            "Epoch: 4232NA_erro: 0.14207831648893135 \n",
            "EU_erro: 0.05982351857562398JP_erro: 0.11160611182220223loss: 0.10450265204259591\n",
            "Epoch: 4233NA_erro: 0.10890332303305929 \n",
            "EU_erro: 0.2753677479056425JP_erro: 0.05632015623787577loss: 0.146863765494768\n",
            "Epoch: 4234NA_erro: 0.10679420574690944 \n",
            "EU_erro: 0.02257554106010023JP_erro: 0.011549236238464829loss: 0.0469729928083198\n",
            "Epoch: 4235NA_erro: 0.27841765751210296 \n",
            "EU_erro: 0.0936856657959694JP_erro: 0.009689293513002321loss: 0.12726421504057656\n",
            "Epoch: 4236NA_erro: 0.13305080029391503 \n",
            "EU_erro: 0.03280568261479223JP_erro: 0.04340829775314922loss: 0.06975492765737135\n",
            "Epoch: 4237NA_erro: 0.14103663060092186 \n",
            "EU_erro: 0.033886333306630455JP_erro: 0.008031880093175311loss: 0.060984954353450804\n",
            "Epoch: 4238NA_erro: 0.5193738050239031 \n",
            "EU_erro: 0.10767465414002884JP_erro: 0.07390733375105747loss: 0.23365196331526883\n",
            "Epoch: 4239NA_erro: 0.24944294330685637 \n",
            "EU_erro: 0.050615021424700125JP_erro: 0.010886426812918612loss: 0.10364813398021136\n",
            "Epoch: 4240NA_erro: 0.08724857670392176 \n",
            "EU_erro: 0.04714546739593033JP_erro: 0.011263958705488101loss: 0.048552672992381016\n",
            "Epoch: 4241NA_erro: 0.1632131347360537 \n",
            "EU_erro: 0.1619777014089185JP_erro: 0.04614090734674025loss: 0.12377724166988402\n",
            "Epoch: 4242NA_erro: 0.22105640219163525 \n",
            "EU_erro: 0.21096397739972256JP_erro: 0.011306731968887093loss: 0.14777569807777108\n",
            "Epoch: 4243NA_erro: 0.5874774622362714 \n",
            "EU_erro: 0.04984823770301287JP_erro: 0.06781609021416006loss: 0.23504728494688523\n",
            "Epoch: 4244NA_erro: 0.6282817485720612 \n",
            "EU_erro: 0.04226961801218432JP_erro: 0.010290866674378861loss: 0.22694741108620814\n",
            "Epoch: 4245NA_erro: 0.11223142461259236 \n",
            "EU_erro: 0.04525703977244769JP_erro: 0.00950921355753906loss: 0.05566590054090633\n",
            "Epoch: 4246NA_erro: 0.1374315383822419 \n",
            "EU_erro: 0.02730711254962655JP_erro: 0.01026996230894281loss: 0.05833620710890423\n",
            "Epoch: 4247NA_erro: 0.09293266969133718 \n",
            "EU_erro: 0.028122388577276423JP_erro: 0.012864229059958644loss: 0.04463976205781449\n",
            "Epoch: 4248NA_erro: 0.715436343998872 \n",
            "EU_erro: 0.0926184099774028JP_erro: 0.017094195120094358loss: 0.27504967164623645\n",
            "Epoch: 4249NA_erro: 0.08535044137821642 \n",
            "EU_erro: 0.03320263106693593JP_erro: 0.01270381863727126loss: 0.04375229972277501\n",
            "Epoch: 4250NA_erro: 0.08550298306369042 \n",
            "EU_erro: 0.03662708193756813JP_erro: 0.012720969989318256loss: 0.04495034384173016\n",
            "Epoch: 4251NA_erro: 0.08101425429647283 \n",
            "EU_erro: 0.02644510925278183JP_erro: 0.010760320249453996loss: 0.03940656203632207\n",
            "Epoch: 4252NA_erro: 0.05571361197981724 \n",
            "EU_erro: 0.08164042650267135JP_erro: 0.008582916370657988loss: 0.04864565161771552\n",
            "Epoch: 4253NA_erro: 0.060594506966051204 \n",
            "EU_erro: 0.018476154222044835JP_erro: 0.01926883477573247loss: 0.03277983314307161\n",
            "Epoch: 4254NA_erro: 0.608749463576679 \n",
            "EU_erro: 0.23414547129194865JP_erro: 0.039447629174520805loss: 0.29411417569300924\n",
            "Epoch: 4255NA_erro: 0.03956439883209938 \n",
            "EU_erro: 0.02713930699252343JP_erro: 0.010442963635274607loss: 0.02571555764176125\n",
            "Epoch: 4256NA_erro: 0.8890614768331365 \n",
            "EU_erro: 0.24923263594161632JP_erro: 0.039050126260565234loss: 0.39244807043740915\n",
            "Epoch: 4257NA_erro: 0.17820859140203904 \n",
            "EU_erro: 0.19790233567703602JP_erro: 0.03959103148112925loss: 0.1385673071986945\n",
            "Epoch: 4258NA_erro: 0.08194504782210948 \n",
            "EU_erro: 0.21080127982206123JP_erro: 0.10987217112105022loss: 0.13420616933541704\n",
            "Epoch: 4259NA_erro: 0.35483393558236054 \n",
            "EU_erro: 0.4572304274684699JP_erro: 0.05629120871078137loss: 0.2894518726555876\n",
            "Epoch: 4260NA_erro: 0.25887160338172616 \n",
            "EU_erro: 0.123108237288719JP_erro: 0.05381126736485681loss: 0.1452637088391208\n",
            "Epoch: 4261NA_erro: 0.6440127173135447 \n",
            "EU_erro: 0.05008396252181179JP_erro: 0.011403267235718956loss: 0.23516664209291915\n",
            "Epoch: 4262NA_erro: 0.1004338356875634 \n",
            "EU_erro: 0.03124575744303622JP_erro: 0.03408323655756869loss: 0.05525427733280862\n",
            "Epoch: 4263NA_erro: 0.3438808751660724 \n",
            "EU_erro: 0.09480676909749822JP_erro: 0.00944267351960027loss: 0.14937678048777026\n",
            "Epoch: 4264NA_erro: 0.06936624992725461 \n",
            "EU_erro: 0.03232455761857735JP_erro: 0.018890939017598942loss: 0.04019391629122948\n",
            "Epoch: 4265NA_erro: 0.269094844197118 \n",
            "EU_erro: 0.037652884804925256JP_erro: 0.02457517524098241loss: 0.11044095652972082\n",
            "Epoch: 4266NA_erro: 0.0536644089129544 \n",
            "EU_erro: 0.031318613725115164JP_erro: 0.021196673082750896loss: 0.0353932311368543\n",
            "Epoch: 4267NA_erro: 0.5722214454828307 \n",
            "EU_erro: 0.16966200614160346JP_erro: 0.07381424423336058loss: 0.2718992565953454\n",
            "Epoch: 4268NA_erro: 0.12098406636437704 \n",
            "EU_erro: 0.016997919757236805JP_erro: 0.007071579380552898loss: 0.0483511908109798\n",
            "Epoch: 4269NA_erro: 0.1180695840554644 \n",
            "EU_erro: 0.04176470198372538JP_erro: 0.022849784803020863loss: 0.06089468797047933\n",
            "Epoch: 4270NA_erro: 0.6449531584747078 \n",
            "EU_erro: 0.46366222145021424JP_erro: 0.06114955558333286loss: 0.3899216097454692\n",
            "Epoch: 4271NA_erro: 0.08595695791318435 \n",
            "EU_erro: 0.031029501164606375JP_erro: 0.009963432716768842loss: 0.042316636373830395\n",
            "Epoch: 4272NA_erro: 0.061843201171520146 \n",
            "EU_erro: 0.033058318518853005JP_erro: 0.008216335098872813loss: 0.03437261710795321\n",
            "Epoch: 4273NA_erro: 0.0766631799150807 \n",
            "EU_erro: 0.026538635409155558JP_erro: 0.007407646770625152loss: 0.03686982300854469\n",
            "Epoch: 4274NA_erro: 0.5274959312852963 \n",
            "EU_erro: 0.20959519600683404JP_erro: 0.11069296866424325loss: 0.28259469557178113\n",
            "Epoch: 4275NA_erro: 0.08815604586933934 \n",
            "EU_erro: 0.031352140182672544JP_erro: 0.022060848021692083loss: 0.04718968110491139\n",
            "Epoch: 4276NA_erro: 0.09928598884464235 \n",
            "EU_erro: 0.1465149535689243JP_erro: 0.01665718680204347loss: 0.08748605269794316\n",
            "Epoch: 4277NA_erro: 0.17509606457495874 \n",
            "EU_erro: 0.07348022257634836JP_erro: 0.060601779656816825loss: 0.10305936946425327\n",
            "Epoch: 4278NA_erro: 0.6697070691012597 \n",
            "EU_erro: 0.31625862269438515JP_erro: 0.036919410376585735loss: 0.3409617076548495\n",
            "Epoch: 4279NA_erro: 0.22300001262694366 \n",
            "EU_erro: 0.15328274216762808JP_erro: 0.12508197348247202loss: 0.16712157301200453\n",
            "Epoch: 4280NA_erro: 0.06601759629656179 \n",
            "EU_erro: 0.031001154304474823JP_erro: 0.036935866341110346loss: 0.0446515351302864\n",
            "Epoch: 4281NA_erro: 0.25790922401487365 \n",
            "EU_erro: 0.1642086524371953JP_erro: 0.05426898021106572loss: 0.15879562658857005\n",
            "Epoch: 4282NA_erro: 0.07543066213297289 \n",
            "EU_erro: 0.02605911142142244JP_erro: 0.014597782792971116loss: 0.038695857506389764\n",
            "Epoch: 4283NA_erro: 0.23157546686571698 \n",
            "EU_erro: 0.05457265432490859JP_erro: 0.02241182003834451loss: 0.10285332221393437\n",
            "Epoch: 4284NA_erro: 0.2864960182544797 \n",
            "EU_erro: 0.04451548868371535JP_erro: 0.006843370407126671loss: 0.11261829110079033\n",
            "Epoch: 4285NA_erro: 0.06861669610637103 \n",
            "EU_erro: 0.03708038450211518JP_erro: 0.010865614857784537loss: 0.03885423028191855\n",
            "Epoch: 4286NA_erro: 0.08166971132736797 \n",
            "EU_erro: 0.037659274977306985JP_erro: 0.02554538176041241loss: 0.04829145679178164\n",
            "Epoch: 4287NA_erro: 0.10015527407328288 \n",
            "EU_erro: 0.06540134433628053JP_erro: 0.015917723031007042loss: 0.06049145099728606\n",
            "Epoch: 4288NA_erro: 0.21768769552541334 \n",
            "EU_erro: 0.14899320380632267JP_erro: 0.04449862380360448loss: 0.1370598456656286\n",
            "Epoch: 4289NA_erro: 5.691337881162185 \n",
            "EU_erro: 2.784456652264262JP_erro: 0.04559492880059767loss: 2.8404632095218627\n",
            "Epoch: 4290NA_erro: 5.717687680739765 \n",
            "EU_erro: 2.7865878556125847JP_erro: 0.04906381285467813loss: 2.8511136077171146\n",
            "Epoch: 4291NA_erro: 1.0732730599336846 \n",
            "EU_erro: 0.24246058722799138JP_erro: 0.028591590334278667loss: 0.44810845870380256\n",
            "Epoch: 4292NA_erro: 0.05419099977774213 \n",
            "EU_erro: 0.03353503323340601JP_erro: 0.011701573466145716loss: 0.03314253664756006\n",
            "Epoch: 4293NA_erro: 0.1520799758822419 \n",
            "EU_erro: 0.04839486854020939JP_erro: 0.01834450073020403loss: 0.07293978402780932\n",
            "Epoch: 4294NA_erro: 0.6443141966827156 \n",
            "EU_erro: 0.11754314104715984JP_erro: 0.071810525517131loss: 0.2778892738874568\n",
            "Epoch: 4295NA_erro: 5.7961072108542275 \n",
            "EU_erro: 2.8697722087534823JP_erro: 0.04505930944930676loss: 2.903646424759266\n",
            "Epoch: 4296NA_erro: 0.23785007092379784 \n",
            "EU_erro: 0.2779499135276144JP_erro: 0.02072024946064912loss: 0.17884006795957108\n",
            "Epoch: 4297NA_erro: 0.052633465722549795 \n",
            "EU_erro: 0.027940203977185628JP_erro: 0.010518339253211207loss: 0.03036400606465894\n",
            "Epoch: 4298NA_erro: 0.201546498971392 \n",
            "EU_erro: 0.17160665157229402JP_erro: 0.019105970859527588loss: 0.13075304585833883\n",
            "Epoch: 4299NA_erro: 0.09345238522965779 \n",
            "EU_erro: 0.09239421334377555JP_erro: 0.009578225571055745loss: 0.06514160670051279\n",
            "Epoch: 4300NA_erro: 0.6480381839959196 \n",
            "EU_erro: 0.054300168687983076JP_erro: 0.011268668165502623loss: 0.2378690335177636\n",
            "Epoch: 4301NA_erro: 0.7186803891677265 \n",
            "EU_erro: 0.47227079554121626JP_erro: 0.05146295987358389loss: 0.41413806205572085\n",
            "Epoch: 4302NA_erro: 0.08521151173022366 \n",
            "EU_erro: 0.050217791121135384JP_erro: 0.032134499660758084loss: 0.055854602377544076\n",
            "Epoch: 4303NA_erro: 0.10283143021339594 \n",
            "EU_erro: 0.03946770531262538JP_erro: 0.007088684527448905loss: 0.04979594271312388\n",
            "Epoch: 4304NA_erro: 0.6031976374544838 \n",
            "EU_erro: 0.05964672842691111JP_erro: 0.06612102652704993loss: 0.2429885088011276\n",
            "Epoch: 4305NA_erro: 0.6834272635999576 \n",
            "EU_erro: 0.31181908393091007JP_erro: 0.07468531298082928loss: 0.35664388375688894\n",
            "Epoch: 4306NA_erro: 0.7486517854439195 \n",
            "EU_erro: 0.26241858800252277JP_erro: 0.11897278386493061loss: 0.37668109864227534\n",
            "Epoch: 4307NA_erro: 0.192342340484146 \n",
            "EU_erro: 0.051979404087214506JP_erro: 0.04245423531347467loss: 0.09559199791546016\n",
            "Epoch: 4308NA_erro: 0.1371063003244326 \n",
            "EU_erro: 0.03551221402116524JP_erro: 0.012724743795025256loss: 0.061781092207561165\n",
            "Epoch: 4309NA_erro: 0.12246873027594514 \n",
            "EU_erro: 0.019774185132610707JP_erro: 0.011024015356403913loss: 0.051088970760966454\n",
            "Epoch: 4310NA_erro: 0.08559517158094303 \n",
            "EU_erro: 0.03898134989331859JP_erro: 0.024969963602317395loss: 0.04984882912894552\n",
            "Epoch: 4311NA_erro: 6.354672927265019 \n",
            "EU_erro: 3.080744188885356JP_erro: 0.07543835991112761loss: 3.17028518795043\n",
            "Epoch: 4312NA_erro: 0.17243013825527456 \n",
            "EU_erro: 0.036128417466037954JP_erro: 0.008368760116340579loss: 0.0723091021988743\n",
            "Epoch: 4313NA_erro: 0.8152782085329987 \n",
            "EU_erro: 0.3528309238049411JP_erro: 0.03208796876345494loss: 0.40006574734236844\n",
            "Epoch: 4314NA_erro: 0.05889921687370123 \n",
            "EU_erro: 0.03180442161338274JP_erro: 0.013155591811320578loss: 0.03461974535801614\n",
            "Epoch: 4315NA_erro: 0.5518396880275519 \n",
            "EU_erro: 0.10692314584125844JP_erro: 0.06789310958034309loss: 0.2422186200932939\n",
            "Epoch: 4316NA_erro: 0.6948198274124501 \n",
            "EU_erro: 0.4975752128187076JP_erro: 0.06086744541345641loss: 0.4177541880644569\n",
            "Epoch: 4317NA_erro: 0.6894451703212058 \n",
            "EU_erro: 0.058492990427238996JP_erro: 0.02146581115648728loss: 0.2564680114272953\n",
            "Epoch: 4318NA_erro: 0.08588467457497767 \n",
            "EU_erro: 0.029987424843071042JP_erro: 0.010285149942072786loss: 0.042052416838416755\n",
            "Epoch: 4319NA_erro: 0.06703631360401478 \n",
            "EU_erro: 0.019851731237514998JP_erro: 0.0407381113185439loss: 0.04254205282344375\n",
            "Epoch: 4320NA_erro: 0.061472455660502114 \n",
            "EU_erro: 0.027029197807459868JP_erro: 0.009571584735729898loss: 0.032691079978795015\n",
            "Epoch: 4321NA_erro: 0.7391463139260462 \n",
            "EU_erro: 0.3940423699312432JP_erro: 0.15920243521993474loss: 0.43079705201378166\n",
            "Epoch: 4322NA_erro: 0.1112471717272618 \n",
            "EU_erro: 0.2609738453414089JP_erro: 0.07026811440785725loss: 0.14749638794004455\n",
            "Epoch: 4323NA_erro: 0.3679310258968856 \n",
            "EU_erro: 0.22307486497154533JP_erro: 0.13582918995110563loss: 0.24227835411249204\n",
            "Epoch: 4324NA_erro: 0.27657684429671414 \n",
            "EU_erro: 0.12605930483618447JP_erro: 0.07176498572031657loss: 0.1581336915955063\n",
            "Epoch: 4325NA_erro: 0.26248562243557716 \n",
            "EU_erro: 0.32022472499876986JP_erro: 0.1035231675288474loss: 0.22874451422876166\n",
            "Epoch: 4326NA_erro: 0.12733813404112823 \n",
            "EU_erro: 0.054075731787570684JP_erro: 0.011562702729720478loss: 0.06432552208272062\n",
            "Epoch: 4327NA_erro: 0.2533915431000466 \n",
            "EU_erro: 0.02584601326506267JP_erro: 0.026030510432960452loss: 0.10175602380619493\n",
            "Epoch: 4328NA_erro: 1.168617425962936 \n",
            "EU_erro: 0.527241913847221JP_erro: 0.06621896296508553loss: 0.5873594727627066\n",
            "Epoch: 4329NA_erro: 0.691377137058465 \n",
            "EU_erro: 0.3163769448450369JP_erro: 0.06742760654567748loss: 0.35839391309161517\n",
            "Epoch: 4330NA_erro: 0.08434895397156708 \n",
            "EU_erro: 0.03892111685848976JP_erro: 0.04305826139080432loss: 0.05544277124626692\n",
            "Epoch: 4331NA_erro: 0.09985105935917345 \n",
            "EU_erro: 0.03326349942259086JP_erro: 0.013057440519332886loss: 0.048723997071731924\n",
            "Epoch: 4332NA_erro: 0.10677162991013638 \n",
            "EU_erro: 0.029080336870149125JP_erro: 0.011387061934138453loss: 0.04907967970352764\n",
            "Epoch: 4333NA_erro: 0.270631239395733 \n",
            "EU_erro: 0.03948186719140341JP_erro: 0.02532069304192713loss: 0.11181126269259194\n",
            "Epoch: 4334NA_erro: 0.3287924167721771 \n",
            "EU_erro: 0.15558403591777004JP_erro: 0.10214760321979374loss: 0.19550804019898407\n",
            "Epoch: 4335NA_erro: 5.7568613688151045 \n",
            "EU_erro: 2.79762770778449JP_erro: 0.05112682664117148loss: 2.868538494257964\n",
            "Epoch: 4336NA_erro: 0.5473784143610518 \n",
            "EU_erro: 0.23257000501765762JP_erro: 0.04118135733197826loss: 0.2737099440522896\n",
            "Epoch: 4337NA_erro: 0.05205814690552941 \n",
            "EU_erro: 0.03317702186200046JP_erro: 0.009056660671566808loss: 0.03143061270085416\n",
            "Epoch: 4338NA_erro: 0.323547503744909 \n",
            "EU_erro: 0.20517059074815855JP_erro: 0.151425091795219loss: 0.22671437078668166\n",
            "Epoch: 4339NA_erro: 0.15040352362995 \n",
            "EU_erro: 0.0343318687852963JP_erro: 0.007978313884069754loss: 0.0642378986343857\n",
            "Epoch: 4340NA_erro: 0.22665313972059145 \n",
            "EU_erro: 0.03453375526176867JP_erro: 0.031250902386598806loss: 0.09747928427171337\n",
            "Epoch: 4341NA_erro: 0.04039045452147491 \n",
            "EU_erro: 0.03164608580197475JP_erro: 0.007091889432234357loss: 0.026376145754673685\n",
            "Epoch: 4342NA_erro: 0.06006967651751614 \n",
            "EU_erro: 0.0734388597251833JP_erro: 0.09563012640605602loss: 0.07637955883676692\n",
            "Epoch: 4343NA_erro: 0.1233166872068893 \n",
            "EU_erro: 0.08729188017142836JP_erro: 0.013239789147709692loss: 0.07461611614670864\n",
            "Epoch: 4344NA_erro: 0.06077530310135479 \n",
            "EU_erro: 0.03549970166627751JP_erro: 0.027683622153230416loss: 0.041319547697555185\n",
            "Epoch: 4345NA_erro: 0.06279542002567025 \n",
            "EU_erro: 0.030670960744222004JP_erro: 0.024609930755556092loss: 0.03935877204865448\n",
            "Epoch: 4346NA_erro: 0.08224789486374966 \n",
            "EU_erro: 0.02377457396928654JP_erro: 0.03546922936920048loss: 0.04716390325117481\n",
            "Epoch: 4347NA_erro: 0.07112848204235699 \n",
            "EU_erro: 0.22470073182453482JP_erro: 0.05621870820836503loss: 0.11734931967979254\n",
            "Epoch: 4348NA_erro: 0.07061015265856603 \n",
            "EU_erro: 0.022498949091563854JP_erro: 0.008038206733474437loss: 0.03371576930201331\n",
            "Epoch: 4349NA_erro: 0.3435316381528396 \n",
            "EU_erro: 0.19949205161989197JP_erro: 0.1403383413950602loss: 0.22778734680294066\n",
            "Epoch: 4350NA_erro: 0.25121333987213845 \n",
            "EU_erro: 0.1749008940171826JP_erro: 0.13766384864038275loss: 0.18792603367058805\n",
            "Epoch: 4351NA_erro: 0.06612006083939427 \n",
            "EU_erro: 0.019578178261601648JP_erro: 0.01863977590272593loss: 0.034779337949531024\n",
            "Epoch: 4352NA_erro: 0.6840779060541198 \n",
            "EU_erro: 0.05289156769597253JP_erro: 0.01147915573083153loss: 0.2494828460752502\n",
            "Epoch: 4353NA_erro: 0.10348829188088114 \n",
            "EU_erro: 0.06627744482469189JP_erro: 0.017285757055578305loss: 0.062350501385770106\n",
            "Epoch: 4354NA_erro: 0.09425055148989656 \n",
            "EU_erro: 0.018670275922893553JP_erro: 0.03599405288696289loss: 0.049638295358465624\n",
            "Epoch: 4355NA_erro: 0.1905409864677015 \n",
            "EU_erro: 0.16764535460361216JP_erro: 0.07734796335530836loss: 0.14517809993536898\n",
            "Epoch: 4356NA_erro: 0.09022016857945642 \n",
            "EU_erro: 0.027554624302442685JP_erro: 0.009627422505570936loss: 0.042467403781506444\n",
            "Epoch: 4357NA_erro: 0.08531340332918388 \n",
            "EU_erro: 0.033561887667160624JP_erro: 0.018505852582842805loss: 0.04579371722169625\n",
            "Epoch: 4358NA_erro: 0.07144101368364437 \n",
            "EU_erro: 0.033893904020619946JP_erro: 0.011152619308279467loss: 0.03882917784905249\n",
            "Epoch: 4359NA_erro: 0.12981456379557765 \n",
            "EU_erro: 0.0459150796712831JP_erro: 0.009006508445554926loss: 0.06157871826674587\n",
            "Epoch: 4360NA_erro: 0.08001617682996646 \n",
            "EU_erro: 0.013152110253193582JP_erro: 0.012100846037384152loss: 0.03508971181026725\n",
            "Epoch: 4361NA_erro: 0.45990263768868855 \n",
            "EU_erro: 0.22740425065506337JP_erro: 0.143967118374137loss: 0.277091384858124\n",
            "Epoch: 4362NA_erro: 0.2086736804755159 \n",
            "EU_erro: 0.06067881750506024JP_erro: 0.024401155091071314loss: 0.09791788204695827\n",
            "Epoch: 4363NA_erro: 0.19325291463571 \n",
            "EU_erro: 0.16226393307826315JP_erro: 0.040681916613911476loss: 0.13206625169561814\n",
            "Epoch: 4364NA_erro: 0.6884172720502514 \n",
            "EU_erro: 0.45062967049059016JP_erro: 0.049436753110368124loss: 0.39616122726322145\n",
            "Epoch: 4365NA_erro: 0.09946437769157943 \n",
            "EU_erro: 0.057757926541705466JP_erro: 0.014811441186786622loss: 0.057344589122506076\n",
            "Epoch: 4366NA_erro: 0.3676506900048071 \n",
            "EU_erro: 0.1910285986671152JP_erro: 0.02086590888888337loss: 0.19318174022112705\n",
            "Epoch: 4367NA_erro: 0.8481234912724458 \n",
            "EU_erro: 0.3378761276718258JP_erro: 0.19942363103230795loss: 0.46180780543837435\n",
            "Epoch: 4368NA_erro: 0.687282399613728 \n",
            "EU_erro: 0.31041880910710773JP_erro: 0.03677143144977185loss: 0.34482419952865717\n",
            "Epoch: 4369NA_erro: 0.11206959569176962 \n",
            "EU_erro: 0.11821021405301353JP_erro: 0.014320320861284123loss: 0.0815333784088608\n",
            "Epoch: 4370NA_erro: 0.05125479642734971 \n",
            "EU_erro: 0.11357625325520833JP_erro: 0.1170834275179131loss: 0.09397149085998535\n",
            "Epoch: 4371NA_erro: 0.17139349796975306 \n",
            "EU_erro: 0.15600912330686584JP_erro: 0.05745414153550022loss: 0.12828559838524162\n",
            "Epoch: 4372NA_erro: 0.062487647514934685 \n",
            "EU_erro: 0.06732789120932882JP_erro: 0.0255033997602241loss: 0.0517729871956877\n",
            "Epoch: 4373NA_erro: 0.1393939912781235 \n",
            "EU_erro: 0.10908350463985472JP_erro: 0.10436182798341263loss: 0.11761311412781708\n",
            "Epoch: 4374NA_erro: 0.5069118691969288 \n",
            "EU_erro: 0.2095595071482104JP_erro: 0.048423303190127824loss: 0.2549649024194525\n",
            "Epoch: 4375NA_erro: 0.11170269906982895 \n",
            "EU_erro: 0.2124724831692008JP_erro: 0.09510042131409165loss: 0.13975853143736375\n",
            "Epoch: 4376NA_erro: 0.1000026030133861 \n",
            "EU_erro: 0.026615019454512487JP_erro: 0.015440787686858066loss: 0.047352799149446706\n",
            "Epoch: 4377NA_erro: 0.06041933399762294 \n",
            "EU_erro: 0.02426839845125065JP_erro: 0.028031775655672532loss: 0.037573169368182044\n",
            "Epoch: 4378NA_erro: 0.6606028061504512 \n",
            "EU_erro: 0.07779856060826501JP_erro: 0.015167183885278627loss: 0.25118953497834906\n",
            "Epoch: 4379NA_erro: 0.12727352999901587 \n",
            "EU_erro: 0.05015722078870433JP_erro: 0.013370568207068037loss: 0.06360043850980064\n",
            "Epoch: 4380NA_erro: 0.07578041202338166 \n",
            "EU_erro: 0.08031933806663336JP_erro: 0.009709927000740701loss: 0.05526989005332769\n",
            "Epoch: 4381NA_erro: 0.10852649230365605 \n",
            "EU_erro: 0.027749545352403506JP_erro: 0.013518468585125235loss: 0.04993150862612466\n",
            "Epoch: 4382NA_erro: 0.21907593852789828 \n",
            "EU_erro: 0.07842340210611506JP_erro: 0.009597798650578936loss: 0.1023657192555509\n",
            "Epoch: 4383NA_erro: 0.05292365255281907 \n",
            "EU_erro: 0.03547369740730108JP_erro: 0.050241240235262136loss: 0.046212862628374915\n",
            "Epoch: 4384NA_erro: 0.1438042729399925 \n",
            "EU_erro: 0.026834378408831218JP_erro: 0.014675301174784815loss: 0.06177131519761196\n",
            "Epoch: 4385NA_erro: 0.05361077397368675 \n",
            "EU_erro: 0.09110915568447853JP_erro: 0.08954778198123903loss: 0.07808923721313477\n",
            "Epoch: 4386NA_erro: 0.06147980227950932 \n",
            "EU_erro: 0.2152064789173215JP_erro: 0.05818906218506569loss: 0.11162512062131896\n",
            "Epoch: 4387NA_erro: 0.31465833501298296 \n",
            "EU_erro: 0.2627137095429177JP_erro: 0.14908701874489008loss: 0.24215304574301078\n",
            "Epoch: 4388NA_erro: 0.1923945523047632 \n",
            "EU_erro: 0.06233272164366966JP_erro: 0.016331450883732286loss: 0.09035291597824688\n",
            "Epoch: 4389NA_erro: 0.11090679686198864 \n",
            "EU_erro: 0.07536316102789353JP_erro: 0.09347935055577478loss: 0.09324976640154226\n",
            "Epoch: 4390NA_erro: 0.7512528027674948 \n",
            "EU_erro: 0.44970922691877496JP_erro: 0.048956589181293815loss: 0.4166395719661269\n",
            "Epoch: 4391NA_erro: 0.12114505435145179 \n",
            "EU_erro: 0.028114954168482344JP_erro: 0.011161327824112057loss: 0.05347378032152043\n",
            "Epoch: 4392NA_erro: 0.11281365572020065 \n",
            "EU_erro: 0.07682967555615329JP_erro: 0.07765541705050209loss: 0.08909958277561868\n",
            "Epoch: 4393NA_erro: 0.6539008414098458 \n",
            "EU_erro: 0.12471296990564627JP_erro: 0.07758264393769493loss: 0.28539884922116304\n",
            "Epoch: 4394NA_erro: 0.6669361646785292 \n",
            "EU_erro: 0.45703913814337677JP_erro: 0.06700147492016932loss: 0.39699225462684334\n",
            "Epoch: 4395NA_erro: 0.24099000664644463 \n",
            "EU_erro: 0.12464518694914589JP_erro: 0.07029041301372439loss: 0.14530853707661\n",
            "Epoch: 4396NA_erro: 5.739694639693859 \n",
            "EU_erro: 3.007255376771439JP_erro: 0.09780358898547269loss: 2.948251177174176\n",
            "Epoch: 4397NA_erro: 0.08635762125946754 \n",
            "EU_erro: 0.06452942079351853JP_erro: 0.015576517628144848loss: 0.05548785361208657\n",
            "Epoch: 4398NA_erro: 0.21294553150502285 \n",
            "EU_erro: 0.12743765069532764JP_erro: 0.053762571756229845loss: 0.13138190720432488\n",
            "Epoch: 4399NA_erro: 5.67478150360344 \n",
            "EU_erro: 2.807412258414335JP_erro: 0.0515902042388916loss: 2.844594911087391\n",
            "Epoch: 4400NA_erro: 0.1630182229271231 \n",
            "EU_erro: 0.03367472757664762JP_erro: 0.011679525523222694loss: 0.06945749123891194\n",
            "Epoch: 4401NA_erro: 0.06005400373030079 \n",
            "EU_erro: 0.032060250293376835JP_erro: 0.06291361280189928loss: 0.05167595175809638\n",
            "Epoch: 4402NA_erro: 0.20311788071033565 \n",
            "EU_erro: 0.03659484922423843JP_erro: 0.008943787263345349loss: 0.08288550746533298\n",
            "Epoch: 4403NA_erro: 0.09099004804625992 \n",
            "EU_erro: 0.03209694411403449JP_erro: 0.016868538403695867loss: 0.04665184390637302\n",
            "Epoch: 4404NA_erro: 0.0605785985325658 \n",
            "EU_erro: 0.11070967644684074JP_erro: 0.010162872630496358loss: 0.06048371163449546\n",
            "Epoch: 4405NA_erro: 0.04137045191239941 \n",
            "EU_erro: 0.029992100804351097JP_erro: 0.014335933581803196loss: 0.02856616594994715\n",
            "Epoch: 4406NA_erro: 0.14618468839068746 \n",
            "EU_erro: 0.38430679676144625JP_erro: 0.05806152210679165loss: 0.1961843542350355\n",
            "Epoch: 4407NA_erro: 0.3138652698014134 \n",
            "EU_erro: 0.21689664485842683JP_erro: 0.14032807461051053loss: 0.22369667541149052\n",
            "Epoch: 4408NA_erro: 0.2650594156841899 \n",
            "EU_erro: 0.15289521032525588JP_erro: 0.06993089073388152loss: 0.1626285102016242\n",
            "Epoch: 4409NA_erro: 0.12998628061871195 \n",
            "EU_erro: 0.05078799040742623JP_erro: 0.0159088204535403loss: 0.06556103857912758\n",
            "Epoch: 4410NA_erro: 0.19608201906662578 \n",
            "EU_erro: 0.04940961682519247JP_erro: 0.043399089066557185loss: 0.09629692218100377\n",
            "Epoch: 4411NA_erro: 0.09960322417030039 \n",
            "EU_erro: 0.03975921823072803JP_erro: 0.04142044126525406loss: 0.06026096122209416\n",
            "Epoch: 4412NA_erro: 0.7097039481466131 \n",
            "EU_erro: 0.4520828040071236JP_erro: 0.046905912170114444loss: 0.40289753167204156\n",
            "Epoch: 4413NA_erro: 0.5595706599627355 \n",
            "EU_erro: 0.11644235877103584JP_erro: 0.07642640161883923loss: 0.25081318478251613\n",
            "Epoch: 4414NA_erro: 0.14676566271818886 \n",
            "EU_erro: 0.043423226637433664JP_erro: 0.011778459992519644loss: 0.06732245286305745\n",
            "Epoch: 4415NA_erro: 0.08293156475983848 \n",
            "EU_erro: 0.053279709446337795JP_erro: 0.06744520146717396loss: 0.06788549959197525\n",
            "Epoch: 4416NA_erro: 0.08756541466528131 \n",
            "EU_erro: 0.040122270584106445JP_erro: 0.027689919915310172loss: 0.05179253197455591\n",
            "Epoch: 4417NA_erro: 0.7050431421560834 \n",
            "EU_erro: 0.4764222359472467JP_erro: 0.060526839522428294loss: 0.4139974135761113\n",
            "Epoch: 4418NA_erro: 0.040230635524720185 \n",
            "EU_erro: 0.06234822809234146JP_erro: 0.10543911956077398loss: 0.06933932618577351\n",
            "Epoch: 4419NA_erro: 5.71579984916273 \n",
            "EU_erro: 2.7803533391435016JP_erro: 0.04929713962614074loss: 2.848483536594598\n",
            "Epoch: 4420NA_erro: 0.0734743360401124 \n",
            "EU_erro: 0.062045150024946344JP_erro: 0.09339579316072685loss: 0.07630509923594866\n",
            "Epoch: 4421NA_erro: 5.815407804740492 \n",
            "EU_erro: 2.8215461553529253JP_erro: 0.08642378703568333loss: 2.907792616260144\n",
            "Epoch: 4422NA_erro: 0.04730446394099746 \n",
            "EU_erro: 0.027559991030730018JP_erro: 0.014174577108649321loss: 0.029679677745168522\n",
            "Epoch: 4423NA_erro: 0.19018699956494708 \n",
            "EU_erro: 0.06803450658339863JP_erro: 0.027644618075023327loss: 0.09528871654540069\n",
            "Epoch: 4424NA_erro: 0.36749159642892293 \n",
            "EU_erro: 0.2632928079412889JP_erro: 0.12008129164229991loss: 0.2502885714981907\n",
            "Epoch: 4425NA_erro: 0.21199381628701852 \n",
            "EU_erro: 0.22105969199838565JP_erro: 0.015009756005087565loss: 0.14935443567675213\n",
            "Epoch: 4426NA_erro: 0.0865446874337603 \n",
            "EU_erro: 0.013153325448664583JP_erro: 0.008568035424217697loss: 0.036088683346445244\n",
            "Epoch: 4427NA_erro: 0.5600354098534399 \n",
            "EU_erro: 0.16572728637577028JP_erro: 0.08051202278728634loss: 0.2687582489131957\n",
            "Epoch: 4428NA_erro: 0.21994940070218819 \n",
            "EU_erro: 0.16256086586057678JP_erro: 0.04305315572162007loss: 0.14185447101445162\n",
            "Epoch: 4429NA_erro: 0.0736603857010834 \n",
            "EU_erro: 0.06647417711657147JP_erro: 0.02264505670976269loss: 0.054259873175805856\n",
            "Epoch: 4430NA_erro: 0.13745140659716704 \n",
            "EU_erro: 0.06590840428374535JP_erro: 0.010154969239419744loss: 0.07117160068925961\n",
            "Epoch: 4431NA_erro: 0.0846487422322118 \n",
            "EU_erro: 0.028857766657836677JP_erro: 0.011581252942713656loss: 0.04169592561647874\n",
            "Epoch: 4432NA_erro: 0.6801393789838451 \n",
            "EU_erro: 0.3165409916131071JP_erro: 0.046215828999068384loss: 0.3476321050362994\n",
            "Epoch: 4433NA_erro: 0.1318844067033871 \n",
            "EU_erro: 0.22759359936381496JP_erro: 0.05741429421328759loss: 0.13896409855332487\n",
            "Epoch: 4434NA_erro: 0.10791861733724904 \n",
            "EU_erro: 0.08342835330223852JP_erro: 0.020228492659191754loss: 0.07052516290383745\n",
            "Epoch: 4435NA_erro: 0.04891513391982678 \n",
            "EU_erro: 0.027706345846486645JP_erro: 0.013545510149741358loss: 0.030055662920308666\n",
            "Epoch: 4436NA_erro: 0.14726795891458674 \n",
            "EU_erro: 0.11710242707599965JP_erro: 0.09837656058082285loss: 0.12091565501782321\n",
            "Epoch: 4437NA_erro: 0.16690121140590933 \n",
            "EU_erro: 0.2116243044535319JP_erro: 0.027602185574612876loss: 0.13537590817887654\n",
            "Epoch: 4438NA_erro: 0.19035980682964473 \n",
            "EU_erro: 0.17784514168436213JP_erro: 0.04803032376045405loss: 0.13874509537866872\n",
            "Epoch: 4439NA_erro: 0.3937355677286784 \n",
            "EU_erro: 0.13395488724228025JP_erro: 0.05954632925432782loss: 0.19574559012124704\n",
            "Epoch: 4440NA_erro: 0.06892565146897191 \n",
            "EU_erro: 0.04394601019777993JP_erro: 0.03566285205441852loss: 0.0495115084241527\n",
            "Epoch: 4441NA_erro: 0.6756768485372381 \n",
            "EU_erro: 0.32475197962088176JP_erro: 0.03741242164789244loss: 0.34594713255416515\n",
            "Epoch: 4442NA_erro: 0.09606649709302326 \n",
            "EU_erro: 0.20909004433210507JP_erro: 0.06322865338288536loss: 0.12279506801634796\n",
            "Epoch: 4443NA_erro: 0.5713517166847406 \n",
            "EU_erro: 0.23252670155015104JP_erro: 0.03280253835426745loss: 0.2788936629775883\n",
            "Epoch: 4444NA_erro: 0.2499610324238622 \n",
            "EU_erro: 0.07655226445013238JP_erro: 0.01168706735899282loss: 0.1127334620601447\n",
            "Epoch: 4445NA_erro: 0.09912899298261302 \n",
            "EU_erro: 0.029957234397415045JP_erro: 0.011299311421638312loss: 0.046795181525770085\n",
            "Epoch: 4446NA_erro: 0.2679265377133392 \n",
            "EU_erro: 0.06270532922227254JP_erro: 0.02284045598303625loss: 0.11782411457032196\n",
            "Epoch: 4447NA_erro: 0.056098560954249185 \n",
            "EU_erro: 0.023120015628578128JP_erro: 0.009112783180650815loss: 0.02944378658782604\n",
            "Epoch: 4448NA_erro: 0.049562477326208304 \n",
            "EU_erro: 0.10259083999219791JP_erro: 0.03454077844471894loss: 0.0622313660244609\n",
            "Epoch: 4449NA_erro: 0.08281126502872438 \n",
            "EU_erro: 0.03301775039628495JP_erro: 0.09427068769469742loss: 0.07003323514332142\n",
            "Epoch: 4450NA_erro: 0.11661229207534199 \n",
            "EU_erro: 0.017833606910335927JP_erro: 0.05387433277544125loss: 0.06277341251225435\n",
            "Epoch: 4451NA_erro: 0.20498894905859186 \n",
            "EU_erro: 0.17156076061633208JP_erro: 0.023501508457716123loss: 0.1333504222160162\n",
            "Epoch: 4452NA_erro: 0.1230176567107208 \n",
            "EU_erro: 0.2346781982007877JP_erro: 0.010424914748169655loss: 0.12270691782929176\n",
            "Epoch: 4453NA_erro: 0.1647618574689525 \n",
            "EU_erro: 0.0524730784024379JP_erro: 0.02788065939910652loss: 0.08170519688332728\n",
            "Epoch: 4454NA_erro: 1.1762525129687877 \n",
            "EU_erro: 0.6429170268450597JP_erro: 0.08090617120728012loss: 0.633358548777972\n",
            "Epoch: 4455NA_erro: 0.10408542877019838 \n",
            "EU_erro: 0.20138254461362382JP_erro: 0.02047622619673263loss: 0.10864806729693745\n",
            "Epoch: 4456NA_erro: 0.0878841100737106 \n",
            "EU_erro: 0.03555028013480726JP_erro: 0.009799455256425133loss: 0.04441127758617549\n",
            "Epoch: 4457NA_erro: 0.14852792717689692 \n",
            "EU_erro: 0.023040260917456576JP_erro: 0.010051149730534517loss: 0.06053978158522022\n",
            "Epoch: 4458NA_erro: 0.07085273894228676 \n",
            "EU_erro: 0.05522754303244657JP_erro: 0.01618754263072051loss: 0.0474226105120755\n",
            "Epoch: 4459NA_erro: 0.03784084042837454 \n",
            "EU_erro: 0.035695128662641656JP_erro: 0.03786884768064632loss: 0.037134941234145055\n",
            "Epoch: 4460NA_erro: 0.1024574457212936 \n",
            "EU_erro: 0.03438942192136779JP_erro: 0.09278985881066137loss: 0.07654558318529943\n",
            "Epoch: 4461NA_erro: 0.07850614629050558 \n",
            "EU_erro: 0.055732962697051296JP_erro: 0.10279596313949703loss: 0.07901169532953306\n",
            "Epoch: 4462NA_erro: 0.20834767541220023 \n",
            "EU_erro: 0.030772628248199936JP_erro: 0.02149894024974616loss: 0.086873076682867\n",
            "Epoch: 4463NA_erro: 0.05808781283770421 \n",
            "EU_erro: 0.029964391575303187JP_erro: 0.0293782676837241loss: 0.03914349300916805\n",
            "Epoch: 4464NA_erro: 0.11752990789191667 \n",
            "EU_erro: 0.033978484859762265JP_erro: 0.016726497993912806loss: 0.05607829999554065\n",
            "Epoch: 4465NA_erro: 0.18271862074386241 \n",
            "EU_erro: 0.16375608222429142JP_erro: 0.04539618658465008loss: 0.13062364371247995\n",
            "Epoch: 4466NA_erro: 5.732951201209726 \n",
            "EU_erro: 2.7950124223102897JP_erro: 0.05788513394289239loss: 2.8619497136552203\n",
            "Epoch: 4467NA_erro: 0.06539214951123377 \n",
            "EU_erro: 0.01945813496907552JP_erro: 0.012447122455567353loss: 0.03243247205896895\n",
            "Epoch: 4468NA_erro: 0.6920053792554278 \n",
            "EU_erro: 0.15681242757989455JP_erro: 0.03400368745936904loss: 0.2942738237307053\n",
            "Epoch: 4469NA_erro: 0.7424565248711165 \n",
            "EU_erro: 0.3613941798838534JP_erro: 0.12497743894887525loss: 0.40960940279701885\n",
            "Epoch: 4470NA_erro: 0.0503140041070391 \n",
            "EU_erro: 0.030092019443364107JP_erro: 0.024064146271047665loss: 0.034823389940483625\n",
            "Epoch: 4471NA_erro: 0.25017065595286764 \n",
            "EU_erro: 0.20621503046316694JP_erro: 0.02239785453145818loss: 0.15959453213122465\n",
            "Epoch: 4472NA_erro: 0.08696991343830907 \n",
            "EU_erro: 0.02814749876658122JP_erro: 0.011755018964294315loss: 0.042290812314942826\n",
            "Epoch: 4473NA_erro: 0.0911688527395559 \n",
            "EU_erro: 0.2200076376744943JP_erro: 0.06409335506054782loss: 0.12508993925050246\n",
            "Epoch: 4474NA_erro: 0.3247706465018812 \n",
            "EU_erro: 0.22041415059289268JP_erro: 0.1429279737694319loss: 0.22937092670174533\n",
            "Epoch: 4475NA_erro: 0.7478277812632479 \n",
            "EU_erro: 0.4529228506162185JP_erro: 0.05937949169513791loss: 0.42004337606503983\n",
            "Epoch: 4476NA_erro: 0.8984968643779903 \n",
            "EU_erro: 0.6190594222194464JP_erro: 0.05977737349133159loss: 0.525777912879175\n",
            "Epoch: 4477NA_erro: 0.6206251484479091 \n",
            "EU_erro: 0.09644050006718599JP_erro: 0.010864533657251402loss: 0.24264343025148377\n",
            "Epoch: 4478NA_erro: 0.1179618706074796 \n",
            "EU_erro: 0.08682521738747294JP_erro: 0.06797732770905014loss: 0.09092147960219273\n",
            "Epoch: 4479NA_erro: 0.1154915122098701 \n",
            "EU_erro: 0.04772500936375108JP_erro: 0.011176156212193098loss: 0.058130894520486046\n",
            "Epoch: 4480NA_erro: 0.2880644058996393 \n",
            "EU_erro: 0.09926853253859882JP_erro: 0.012580944124118303loss: 0.13330462367035623\n",
            "Epoch: 4481NA_erro: 0.24042277373084725 \n",
            "EU_erro: 0.053821973098340886JP_erro: 0.01952374166296434loss: 0.10458949924439423\n",
            "Epoch: 4482NA_erro: 0.04997975604478703 \n",
            "EU_erro: 0.03444492354873539JP_erro: 0.03379757090132366loss: 0.039407421452130456\n",
            "Epoch: 4483NA_erro: 0.15069177908490794 \n",
            "EU_erro: 0.01763726736223975JP_erro: 0.01273327673128409loss: 0.06035410618597223\n",
            "Epoch: 4484NA_erro: 0.7800767588060956 \n",
            "EU_erro: 0.46791512836781585JP_erro: 0.17878086992012437loss: 0.47559091287065847\n",
            "Epoch: 4485NA_erro: 0.5233803830405538 \n",
            "EU_erro: 0.21789502727892973JP_erro: 0.03224345833756203loss: 0.2578396390574847\n",
            "Epoch: 4486NA_erro: 0.2353153672329215 \n",
            "EU_erro: 0.20248945369276888JP_erro: 0.03946982150854066loss: 0.15909156133962232\n",
            "Epoch: 4487NA_erro: 0.09055027666017991 \n",
            "EU_erro: 0.02536803252937258JP_erro: 0.06573850794355998loss: 0.060552280078562656\n",
            "Epoch: 4488NA_erro: 0.31590077304100805 \n",
            "EU_erro: 0.20291228627049646JP_erro: 0.156411514725796loss: 0.2250748641731203\n",
            "Epoch: 4489NA_erro: 0.2323219203209692 \n",
            "EU_erro: 0.0894895634909933JP_erro: 0.02542898405429929loss: 0.11574683263320332\n",
            "Epoch: 4490NA_erro: 0.3190979477046996 \n",
            "EU_erro: 0.23561778918717258JP_erro: 0.14056575390719628loss: 0.23176049077233604\n",
            "Epoch: 4491NA_erro: 5.780741965123849 \n",
            "EU_erro: 2.796545694040698JP_erro: 0.060554326966751455loss: 2.8792808591857435\n",
            "Epoch: 4492NA_erro: 0.05875271882197654 \n",
            "EU_erro: 0.03885129625483077JP_erro: 0.014435572679652724loss: 0.037346528482067494\n",
            "Epoch: 4493NA_erro: 0.9114644693773847 \n",
            "EU_erro: 0.49877285033233404JP_erro: 0.1746217964231506loss: 0.5282864090084105\n",
            "Epoch: 4494NA_erro: 0.4532387829566187 \n",
            "EU_erro: 0.10131125302277794JP_erro: 0.016542068978612736loss: 0.19036402073941489\n",
            "Epoch: 4495NA_erro: 0.6779418620028237 \n",
            "EU_erro: 0.4537012780359549JP_erro: 0.0752886552219243loss: 0.4023106523262438\n",
            "Epoch: 4496NA_erro: 0.08421061574950699 \n",
            "EU_erro: 0.0653969317443611JP_erro: 0.011276200760242551loss: 0.05362791608470355\n",
            "Epoch: 4497NA_erro: 0.09117424950119137 \n",
            "EU_erro: 0.05346407723981281JP_erro: 0.015720212182333302loss: 0.053452854008637654\n",
            "Epoch: 4498NA_erro: 0.18480424733124962 \n",
            "EU_erro: 0.07621882959853771JP_erro: 0.033735131570535114loss: 0.0982527400172034\n",
            "Epoch: 4499NA_erro: 0.6177623142567716 \n",
            "EU_erro: 0.053156350934228234JP_erro: 0.009445377098497494loss: 0.2267880217973576\n",
            "Epoch: 4500NA_erro: 0.22985757783401845 \n",
            "EU_erro: 0.027105297691138217JP_erro: 0.009676180491151735loss: 0.08887968322103337\n",
            "Epoch: 4501NA_erro: 0.49205373423968174 \n",
            "EU_erro: 0.21334143572075423JP_erro: 0.03946759442026301loss: 0.24828759274741477\n",
            "Epoch: 4502NA_erro: 0.0963849522346674 \n",
            "EU_erro: 0.036600151265314386JP_erro: 0.06934887679048286loss: 0.0674446654874225\n",
            "Epoch: 4503NA_erro: 0.6520046559415122 \n",
            "EU_erro: 0.3591988068218379JP_erro: 0.030526807603909988loss: 0.34724342730618263\n",
            "Epoch: 4504NA_erro: 0.06926860458167024 \n",
            "EU_erro: 0.03330652565919152JP_erro: 0.023858431697815888loss: 0.04214452218639758\n",
            "Epoch: 4505NA_erro: 0.32018805659094524 \n",
            "EU_erro: 0.210427628007046JP_erro: 0.14168473177177962loss: 0.224100157271984\n",
            "Epoch: 4506NA_erro: 5.731064552484557 \n",
            "EU_erro: 2.7856646397317104JP_erro: 0.045135668081830636loss: 2.853955113610556\n",
            "Epoch: 4507NA_erro: 0.23472532745479613 \n",
            "EU_erro: 0.28587394906568897JP_erro: 0.06535321705101073loss: 0.19531750863836717\n",
            "Epoch: 4508NA_erro: 0.040542284647623696 \n",
            "EU_erro: 0.0372255379839461JP_erro: 0.013782274584437525loss: 0.030516702537388765\n",
            "Epoch: 4509NA_erro: 0.20618976548660634 \n",
            "EU_erro: 0.030481819958649865JP_erro: 0.008172909765280494loss: 0.08161483809005382\n",
            "Epoch: 4510NA_erro: 0.10942700297333473 \n",
            "EU_erro: 0.2241838994876359JP_erro: 0.010338016720705254loss: 0.11464964511782624\n",
            "Epoch: 4511NA_erro: 0.2972951231076736 \n",
            "EU_erro: 0.18003153246502543JP_erro: 0.036771211975304655loss: 0.17136595969976381\n",
            "Epoch: 4512NA_erro: 0.2785340944925944 \n",
            "EU_erro: 0.19830361817234246JP_erro: 0.01014450146246326loss: 0.1623274189557216\n",
            "Epoch: 4513NA_erro: 0.10967205661211828 \n",
            "EU_erro: 0.027696451013402422JP_erro: 0.01210617233616437loss: 0.04982489486073339\n",
            "Epoch: 4514NA_erro: 0.12282397395880647 \n",
            "EU_erro: 0.04025806752286216JP_erro: 0.009257966118265492loss: 0.05744667940361555\n",
            "Epoch: 4515NA_erro: 0.09842758030854454 \n",
            "EU_erro: 0.030594387257746023JP_erro: 0.005768293211626452loss: 0.04493008750353673\n",
            "Epoch: 4516NA_erro: 0.18595190935356673 \n",
            "EU_erro: 0.17399312913879866JP_erro: 0.02465836530507997loss: 0.1282011353692343\n",
            "Epoch: 4517NA_erro: 0.8462355118389278 \n",
            "EU_erro: 0.5523312738699506JP_erro: 0.10876275772272154loss: 0.5024432396703913\n",
            "Epoch: 4518NA_erro: 0.10586053826088129 \n",
            "EU_erro: 0.04717922487924265JP_erro: 0.031399576700935065loss: 0.06147978379744892\n",
            "Epoch: 4519NA_erro: 0.13293764387914378 \n",
            "EU_erro: 0.26589894479559373JP_erro: 0.026373486648234287loss: 0.14173669408458148\n",
            "Epoch: 4520NA_erro: 0.10206315868584685 \n",
            "EU_erro: 0.058754021807234415JP_erro: 0.03381354402202044loss: 0.06487691125204396\n",
            "Epoch: 4521NA_erro: 0.06046971147374589 \n",
            "EU_erro: 0.02098137094068897JP_erro: 0.011711631172387174loss: 0.031054237092188162\n",
            "Epoch: 4522NA_erro: 0.08802635725154433 \n",
            "EU_erro: 0.032533846160238106JP_erro: 0.011912543875302455loss: 0.04415758820467217\n",
            "Epoch: 4523NA_erro: 0.652910498685615 \n",
            "EU_erro: 0.458936284678851JP_erro: 0.04734819711640824loss: 0.38639837457228077\n",
            "Epoch: 4524NA_erro: 0.5533721835114235 \n",
            "EU_erro: 0.12131366618843965JP_erro: 0.07650807846424192loss: 0.2503979668136715\n",
            "Epoch: 4525NA_erro: 0.07286028806553331 \n",
            "EU_erro: 0.02649966598481171JP_erro: 0.018609505753184475loss: 0.039323152497757315\n",
            "Epoch: 4526NA_erro: 0.04363756309183993 \n",
            "EU_erro: 0.02783518428950347JP_erro: 0.04828787589258002loss: 0.039920206217802776\n",
            "Epoch: 4527NA_erro: 0.21312787551288456 \n",
            "EU_erro: 0.04978482575379601JP_erro: 0.01928631302922271loss: 0.0940663407939349\n",
            "Epoch: 4528NA_erro: 0.13925042263297147 \n",
            "EU_erro: 0.033415997213171435JP_erro: 0.017979246701381003loss: 0.06354855936627056\n",
            "Epoch: 4529NA_erro: 0.31705482985622196 \n",
            "EU_erro: 0.24871550789175106JP_erro: 0.1392744400704554loss: 0.23501494134119313\n",
            "Epoch: 4530NA_erro: 0.2781335697617642 \n",
            "EU_erro: 0.19355132598285527JP_erro: 0.05018653333649155loss: 0.1739571630492691\n",
            "Epoch: 4531NA_erro: 0.09345463079999583 \n",
            "EU_erro: 0.04020879434984784JP_erro: 0.012851768685865772loss: 0.04883839640506478\n",
            "Epoch: 4532NA_erro: 0.08337286091590113 \n",
            "EU_erro: 0.03743302683497584JP_erro: 0.007822190144265344loss: 0.042876028275304984\n",
            "Epoch: 4533NA_erro: 0.07534655489662821 \n",
            "EU_erro: 0.03245095642962197JP_erro: 0.027378573436145633loss: 0.045058704161828805\n",
            "Epoch: 4534NA_erro: 0.09747451590013134 \n",
            "EU_erro: 0.23773339367652124JP_erro: 0.06394846032756243loss: 0.1330521310022635\n",
            "Epoch: 4535NA_erro: 1.0911673908085786 \n",
            "EU_erro: 0.3237584949463837JP_erro: 0.11108231174853421loss: 0.5086694022481756\n",
            "Epoch: 4536NA_erro: 0.19792068836300872 \n",
            "EU_erro: 0.17540517703507297JP_erro: 0.047030864759933115loss: 0.140118913133015\n",
            "Epoch: 4537NA_erro: 0.5038236277972081 \n",
            "EU_erro: 0.2118788763534191JP_erro: 0.11334977408712224loss: 0.2763507717339567\n",
            "Epoch: 4538NA_erro: 0.1589786174685456 \n",
            "EU_erro: 0.02367479163546895JP_erro: 0.006519673868667248loss: 0.06305769894474236\n",
            "Epoch: 4539NA_erro: 0.0766407288322153 \n",
            "EU_erro: 0.028689616872358693JP_erro: 0.007546975515609564loss: 0.03762577393258265\n",
            "Epoch: 4540NA_erro: 0.1350378343301226 \n",
            "EU_erro: 0.04313044769819393JP_erro: 0.010143928518591002loss: 0.062770732613497\n",
            "Epoch: 4541NA_erro: 0.16941029896107754 \n",
            "EU_erro: 0.04643343677816465JP_erro: 0.02346900082373804loss: 0.07977091064748838\n",
            "Epoch: 4542NA_erro: 0.08128501648126646 \n",
            "EU_erro: 0.05933289842088093JP_erro: 0.012393703525380571loss: 0.051003874734390615\n",
            "Epoch: 4543NA_erro: 0.19870929939802304 \n",
            "EU_erro: 0.05203385223713956JP_erro: 0.009363058001496071loss: 0.08670207141905792\n",
            "Epoch: 4544NA_erro: 0.3031631403191145 \n",
            "EU_erro: 0.1013457128243853JP_erro: 0.026854336723800776loss: 0.14378772225490835\n",
            "Epoch: 4545NA_erro: 0.643628290457319 \n",
            "EU_erro: 0.30874945396600767JP_erro: 0.08799596350322399loss: 0.34679128218066785\n",
            "Epoch: 4546NA_erro: 0.21973120149715927 \n",
            "EU_erro: 0.05138708639514539JP_erro: 0.010052290997763938loss: 0.09372353553771973\n",
            "Epoch: 4547NA_erro: 0.06216392498607783 \n",
            "EU_erro: 0.03200299287027167JP_erro: 0.011515740969384363loss: 0.035227553326954215\n",
            "Epoch: 4548NA_erro: 0.22058257760927658 \n",
            "EU_erro: 0.16985580902691036JP_erro: 0.04483199858850287loss: 0.14509013456891673\n",
            "Epoch: 4549NA_erro: 0.06910915522612343 \n",
            "EU_erro: 0.08738728456718978JP_erro: 0.021657761215239532loss: 0.05938473597977512\n",
            "Epoch: 4550NA_erro: 0.06766871426456658 \n",
            "EU_erro: 0.026083699492521064JP_erro: 0.01855552312015563loss: 0.03743597703386647\n",
            "Epoch: 4551NA_erro: 0.23231563642043476 \n",
            "EU_erro: 0.05172366781752239JP_erro: 0.0058661747810452485loss: 0.09663516236830128\n",
            "Epoch: 4552NA_erro: 0.14682675516882607 \n",
            "EU_erro: 0.06378107292707576JP_erro: 0.009107484604961188loss: 0.07323844488276991\n",
            "Epoch: 4553NA_erro: 0.6875259192414986 \n",
            "EU_erro: 0.2082000222317008JP_erro: 0.03067698589591093loss: 0.30880096346832986\n",
            "Epoch: 4554NA_erro: 0.7109074259913245 \n",
            "EU_erro: 0.3257059126861336JP_erro: 0.06290981473848802loss: 0.36650772242583046\n",
            "Epoch: 4555NA_erro: 0.054668303607970245 \n",
            "EU_erro: 0.20265052484911542JP_erro: 0.19727625588113948loss: 0.15153169631958008\n",
            "Epoch: 4556NA_erro: 0.6209102896756904 \n",
            "EU_erro: 0.045002135195473365JP_erro: 0.03986003324967022loss: 0.2352574814197629\n",
            "Epoch: 4557NA_erro: 0.5884422627530357 \n",
            "EU_erro: 0.09617822114811388JP_erro: 0.08478595304858777loss: 0.2564688061558923\n",
            "Epoch: 4558NA_erro: 0.2842807030492975 \n",
            "EU_erro: 0.12879963068999062JP_erro: 0.05678370479465455loss: 0.1566213415574658\n",
            "Epoch: 4559NA_erro: 0.21905279898828314 \n",
            "EU_erro: 0.32360690508702006JP_erro: 0.04158765308616697loss: 0.19474911135296488\n",
            "Epoch: 4560NA_erro: 0.5899869563967682 \n",
            "EU_erro: 0.2782174413518388JP_erro: 0.08072070373121158loss: 0.31630837640096976\n",
            "Epoch: 4561NA_erro: 0.07219147774600243 \n",
            "EU_erro: 0.15026867851730466JP_erro: 0.01475314645804176loss: 0.0790711059126743\n",
            "Epoch: 4562NA_erro: 0.08418164511983708 \n",
            "EU_erro: 0.057443375735319865JP_erro: 0.029942589674809183loss: 0.05718920582024626\n",
            "Epoch: 4563NA_erro: 0.11072549709053926 \n",
            "EU_erro: 0.033908756666405256JP_erro: 0.022139916586321452loss: 0.05559139011442199\n",
            "Epoch: 4564NA_erro: 0.08475043976953787 \n",
            "EU_erro: 0.04364820413811262JP_erro: 0.09648317514463912loss: 0.07496060789093491\n",
            "Epoch: 4565NA_erro: 0.10942180027333341 \n",
            "EU_erro: 0.03177432126777117JP_erro: 0.023788273796554682loss: 0.05499480306640152\n",
            "Epoch: 4566NA_erro: 0.23062170013900876 \n",
            "EU_erro: 0.02646942702374717JP_erro: 0.009453433544136757loss: 0.08884818979011949\n",
            "Epoch: 4567NA_erro: 0.21985205569008523 \n",
            "EU_erro: 0.18004389696343JP_erro: 0.06253853324771852loss: 0.1541448378747748\n",
            "Epoch: 4568NA_erro: 0.09509499682936558 \n",
            "EU_erro: 0.04597020241641259JP_erro: 0.08628408114115398loss: 0.07578309654265411\n",
            "Epoch: 4569NA_erro: 0.2571832302004792 \n",
            "EU_erro: 0.05086060180220493JP_erro: 0.024844294832658396loss: 0.11096270509468492\n",
            "Epoch: 4570NA_erro: 0.04098146922828615 \n",
            "EU_erro: 0.0722062818763792JP_erro: 0.011997751949369445loss: 0.04172850024792575\n",
            "Epoch: 4571NA_erro: 6.456676157870034 \n",
            "EU_erro: 3.3762188105620154JP_erro: 0.11667155480200006loss: 3.3165220511976137\n",
            "Epoch: 4572NA_erro: 0.5751314458920974 \n",
            "EU_erro: 0.12631736984548642JP_erro: 0.07150242957033852loss: 0.2576504197231559\n",
            "Epoch: 4573NA_erro: 0.718451876972997 \n",
            "EU_erro: 0.21883777869764223JP_erro: 0.026591838330261467loss: 0.32129383826440616\n",
            "Epoch: 4574NA_erro: 0.17204258793084196 \n",
            "EU_erro: 0.07568130197451096JP_erro: 0.013768147359522738loss: 0.0871640027955521\n",
            "Epoch: 4575NA_erro: 0.5627010404601578 \n",
            "EU_erro: 0.2309769068577493JP_erro: 0.04302868085314137loss: 0.27890222017155136\n",
            "Epoch: 4576NA_erro: 0.30916859013165615 \n",
            "EU_erro: 0.3046062195947928JP_erro: 0.05439618299173754loss: 0.2227236688599106\n",
            "Epoch: 4577NA_erro: 0.036838239477586375 \n",
            "EU_erro: 0.039630615433981255JP_erro: 0.011288182911022689loss: 0.029252347095992216\n",
            "Epoch: 4578NA_erro: 0.5010187903115916 \n",
            "EU_erro: 0.21452650543331175JP_erro: 0.14170472012009733loss: 0.2857500083686769\n",
            "Epoch: 4579NA_erro: 0.6650430102681004 \n",
            "EU_erro: 0.4524070532747017JP_erro: 0.05639911159988521loss: 0.3912830352783203\n",
            "Epoch: 4580NA_erro: 0.09501441504604133 \n",
            "EU_erro: 0.06904511950736822JP_erro: 0.02394585646400156loss: 0.06266846213229867\n",
            "Epoch: 4581NA_erro: 0.6748954270237176 \n",
            "EU_erro: 0.46450027199678645JP_erro: 0.05224031533381736loss: 0.3972119693608247\n",
            "Epoch: 4582NA_erro: 0.0757684772328813 \n",
            "EU_erro: 0.03837285346763079JP_erro: 0.016565855390341708loss: 0.04356906395549922\n",
            "Epoch: 4583NA_erro: 0.04326701626297115 \n",
            "EU_erro: 0.05570170029189236JP_erro: 0.02366720012916151loss: 0.04087863966476086\n",
            "Epoch: 4584NA_erro: 0.2917004186053609 \n",
            "EU_erro: 0.048047636830529504JP_erro: 0.005066899597182754loss: 0.11493833490120348\n",
            "Epoch: 4585NA_erro: 0.09527286817861158 \n",
            "EU_erro: 0.03903079402539157JP_erro: 0.009921500387117844loss: 0.04807505034661108\n",
            "Epoch: 4586NA_erro: 0.6524752091991809 \n",
            "EU_erro: 0.047825339228607884JP_erro: 0.01152160786843115loss: 0.23727405902951262\n",
            "Epoch: 4587NA_erro: 1.146480501160141 \n",
            "EU_erro: 0.5335714281067367JP_erro: 0.2233249642128168loss: 0.6344589706539183\n",
            "Epoch: 4588NA_erro: 0.26752673378286435 \n",
            "EU_erro: 0.3126432729321857JP_erro: 0.0955481584682021loss: 0.22523940995682118\n",
            "Epoch: 4589NA_erro: 0.5987517038981119 \n",
            "EU_erro: 0.05044096662092579JP_erro: 0.06232676579970722loss: 0.23717314697975336\n",
            "Epoch: 4590NA_erro: 0.1479568037875863 \n",
            "EU_erro: 0.06020072818726532JP_erro: 0.06261671698370645loss: 0.09025808452635772\n",
            "Epoch: 4591NA_erro: 0.15625914861989576 \n",
            "EU_erro: 0.22410814152207487JP_erro: 0.07015799829202105loss: 0.1501750946044922\n",
            "Epoch: 4592NA_erro: 0.09400684704152189 \n",
            "EU_erro: 0.03768726605777593JP_erro: 0.02717986587406129loss: 0.05295799684154895\n",
            "Epoch: 4593NA_erro: 0.13758920883947565 \n",
            "EU_erro: 0.07069702296293984JP_erro: 0.008258007524549499loss: 0.0721814142641171\n",
            "Epoch: 4594NA_erro: 0.09674944618875667 \n",
            "EU_erro: 0.02722825190817663JP_erro: 0.0107406149076861loss: 0.04490610703017361\n",
            "Epoch: 4595NA_erro: 0.2014567870502324 \n",
            "EU_erro: 0.04490785820539608JP_erro: 0.013964780310327692loss: 0.08677648019421008\n",
            "Epoch: 4596NA_erro: 0.2941731334656708 \n",
            "EU_erro: 0.1821340516556141JP_erro: 0.03976651864458424loss: 0.1720245679219564\n",
            "Epoch: 4597NA_erro: 0.04108437734056813 \n",
            "EU_erro: 0.11780178824136424JP_erro: 0.12326681336691213loss: 0.09405100068380666\n",
            "Epoch: 4598NA_erro: 0.07527400356854579 \n",
            "EU_erro: 0.02171941505846127JP_erro: 0.05120482093604036loss: 0.049399414727854175\n",
            "Epoch: 4599NA_erro: 0.08933996045312216 \n",
            "EU_erro: 0.03042349981707196JP_erro: 0.028223808883696563loss: 0.04932909510856451\n",
            "Epoch: 4600NA_erro: 0.07818736771280452 \n",
            "EU_erro: 0.04232570182445437JP_erro: 0.025985289451687835loss: 0.04883279172024985\n",
            "Epoch: 4601NA_erro: 0.029044555601223494 \n",
            "EU_erro: 0.08041148037873497JP_erro: 0.023687731388003327loss: 0.04438125347906305\n",
            "Epoch: 4602NA_erro: 0.08272746736689131 \n",
            "EU_erro: 0.05936533443687498JP_erro: 0.025454662566961245loss: 0.055849154790242515\n",
            "Epoch: 4603NA_erro: 0.23856819138046384 \n",
            "EU_erro: 0.3139307702234549JP_erro: 0.1258695772451948loss: 0.22612285244372465\n",
            "Epoch: 4604NA_erro: 0.20369699759076731 \n",
            "EU_erro: 0.05996410698853722JP_erro: 0.06255829519079636loss: 0.10873980300371038\n",
            "Epoch: 4605NA_erro: 0.09116850158040837 \n",
            "EU_erro: 0.08462481720502986JP_erro: 0.022155885086503138loss: 0.06598306718722795\n",
            "Epoch: 4606NA_erro: 0.3729332694711611 \n",
            "EU_erro: 0.0980291255684786JP_erro: 0.04174306411151738loss: 0.17090183819911275\n",
            "Epoch: 4607NA_erro: 0.0903960900713307 \n",
            "EU_erro: 0.04449397094489992JP_erro: 0.015862268763919208loss: 0.0502507769784262\n",
            "Epoch: 4608NA_erro: 0.3342723846435547 \n",
            "EU_erro: 0.3126566909080328JP_erro: 0.1385024906128876loss: 0.26181050973345144\n",
            "Epoch: 4609NA_erro: 0.06326937398245168 \n",
            "EU_erro: 0.02639684104180151JP_erro: 0.017868172983790554loss: 0.03584479407746662\n",
            "Epoch: 4610NA_erro: 0.3587681748146235 \n",
            "EU_erro: 0.2540019501087277JP_erro: 0.2623629939648532loss: 0.29171106427214866\n",
            "Epoch: 4611NA_erro: 0.17449290253395258 \n",
            "EU_erro: 0.088034239850303JP_erro: 0.011512355286945669loss: 0.09134650230407715\n",
            "Epoch: 4612NA_erro: 0.3398943686670111 \n",
            "EU_erro: 0.1436859877534615JP_erro: 0.058701121529867485loss: 0.18076050189114357\n",
            "Epoch: 4613NA_erro: 0.056916461434475214 \n",
            "EU_erro: 0.030042168705962425JP_erro: 0.04963991715926533loss: 0.04553285218024439\n",
            "Epoch: 4614NA_erro: 0.6404618699421254 \n",
            "EU_erro: 0.042190537896267206JP_erro: 0.03188220105429952loss: 0.2381782383881798\n",
            "Epoch: 4615NA_erro: 0.5804157257080078 \n",
            "EU_erro: 0.3140160464501196JP_erro: 0.1186680886172509loss: 0.33769995667213615\n",
            "Epoch: 4616NA_erro: 0.08188961088195328 \n",
            "EU_erro: 0.02479585797287697JP_erro: 0.08002039998076683loss: 0.06223528884177984\n",
            "Epoch: 4617NA_erro: 0.5774412598720816 \n",
            "EU_erro: 0.05824071492335593JP_erro: 0.007092859162840732loss: 0.2142583122549131\n",
            "Epoch: 4618NA_erro: 0.049894946490147314 \n",
            "EU_erro: 0.026568379975104518JP_erro: 0.02133321854495263loss: 0.032598852187164074\n",
            "Epoch: 4619NA_erro: 0.07106837376143581 \n",
            "EU_erro: 0.04664044971613921JP_erro: 0.018892416427301807loss: 0.045533748560173566\n",
            "Epoch: 4620NA_erro: 0.1926410475442576 \n",
            "EU_erro: 0.1701179031253785JP_erro: 0.09805445523225059loss: 0.15360446863396224\n",
            "Epoch: 4621NA_erro: 0.09485182836074238 \n",
            "EU_erro: 0.02468577420064645JP_erro: 0.011746497579323228loss: 0.04376136979391409\n",
            "Epoch: 4622NA_erro: 0.22925195767897968 \n",
            "EU_erro: 0.0770882996477822JP_erro: 0.021149511485136756loss: 0.10916325473046118\n",
            "Epoch: 4623NA_erro: 0.09438443553540134 \n",
            "EU_erro: 0.19859260366868603JP_erro: 0.04570616770160291loss: 0.11289441308309865\n",
            "Epoch: 4624NA_erro: 0.09518122488214065 \n",
            "EU_erro: 0.07842513941979223JP_erro: 0.018307044524555057loss: 0.06397113781566768\n",
            "Epoch: 4625NA_erro: 0.2075461269349091 \n",
            "EU_erro: 0.050972815631896026JP_erro: 0.014501832021299258loss: 0.09100692216740099\n",
            "Epoch: 4626NA_erro: 0.1090489431869152 \n",
            "EU_erro: 0.11959717255230098JP_erro: 0.011196471461954042loss: 0.07994752521662749\n",
            "Epoch: 4627NA_erro: 0.1428397404131039 \n",
            "EU_erro: 0.06356092386467513JP_erro: 0.01684119419534077loss: 0.07441395937010299\n",
            "Epoch: 4628NA_erro: 0.2531869097273479 \n",
            "EU_erro: 0.0535420160885005JP_erro: 0.009454116225242615loss: 0.10539434676946596\n",
            "Epoch: 4629NA_erro: 0.22982928179955298 \n",
            "EU_erro: 0.07190040377683418JP_erro: 0.009025569225466528loss: 0.10358508243117222\n",
            "Epoch: 4630NA_erro: 0.14790140381155087 \n",
            "EU_erro: 0.0389910945596621JP_erro: 0.023238492566485736loss: 0.07004367288692977\n",
            "Epoch: 4631NA_erro: 0.18419019935667053 \n",
            "EU_erro: 0.1819316730942837JP_erro: 0.05228546700736349loss: 0.13946911161260087\n",
            "Epoch: 4632NA_erro: 0.08272935253705165 \n",
            "EU_erro: 0.033468225205591486JP_erro: 0.008432150118110715loss: 0.04154324993606685\n",
            "Epoch: 4633NA_erro: 0.19473532373590988 \n",
            "EU_erro: 0.14347045920615972JP_erro: 0.048274106757585394loss: 0.1288266329802284\n",
            "Epoch: 4634NA_erro: 0.10179336680922398 \n",
            "EU_erro: 0.20615531492602918JP_erro: 0.057661607283954475loss: 0.12187010558076607\n",
            "Epoch: 4635NA_erro: 0.7463348004245018 \n",
            "EU_erro: 0.3590768252232278JP_erro: 0.03610736639924752loss: 0.3805063676464465\n",
            "Epoch: 4636NA_erro: 0.6118920422339624 \n",
            "EU_erro: 0.12106800264166306JP_erro: 0.010195008313008982loss: 0.24771836376929468\n",
            "Epoch: 4637NA_erro: 0.09772991025170615 \n",
            "EU_erro: 0.03081577461819316JP_erro: 0.010812905176665432loss: 0.04645286142364029\n",
            "Epoch: 4638NA_erro: 0.22481129150982052 \n",
            "EU_erro: 0.12961013372554336JP_erro: 0.055082856222640635loss: 0.13650142869283988\n",
            "Epoch: 4639NA_erro: 0.7743244762568511 \n",
            "EU_erro: 0.5914709919182829JP_erro: 0.0766994879227276loss: 0.4808316859163979\n",
            "Epoch: 4640NA_erro: 0.09957121324169543 \n",
            "EU_erro: 0.06040071794228961JP_erro: 0.013971177413482075loss: 0.057981037354284476\n",
            "Epoch: 4641NA_erro: 0.7514566968577777 \n",
            "EU_erro: 0.10414239972136742JP_erro: 0.03813666428706443loss: 0.297911943391312\n",
            "Epoch: 4642NA_erro: 0.20573922830034597 \n",
            "EU_erro: 0.1910126301669335JP_erro: 0.024110351422036342loss: 0.1402874048366103\n",
            "Epoch: 4643NA_erro: 1.0986285985902298 \n",
            "EU_erro: 0.500284239303234JP_erro: 0.05306844101395718loss: 0.5506604216819586\n",
            "Epoch: 4644NA_erro: 0.07795292277668797 \n",
            "EU_erro: 0.033732273781946466JP_erro: 0.013306075057318045loss: 0.04166376221087552\n",
            "Epoch: 4645NA_erro: 0.02536965895068738 \n",
            "EU_erro: 0.03921598889106928JP_erro: 0.012935760409332985loss: 0.02584047095720158\n",
            "Epoch: 4646NA_erro: 0.20273652187613553 \n",
            "EU_erro: 0.04689388958982719JP_erro: 0.025109376094137974loss: 0.09157992148584174\n",
            "Epoch: 4647NA_erro: 0.6775324474009432 \n",
            "EU_erro: 0.3138707774554112JP_erro: 0.03981094489725985loss: 0.343738082767457\n",
            "Epoch: 4648NA_erro: 0.08639600849890894 \n",
            "EU_erro: 0.19013256989708244JP_erro: 0.04846571489822033loss: 0.10833143263824227\n",
            "Epoch: 4649NA_erro: 0.060925003170043 \n",
            "EU_erro: 0.018953999807668286JP_erro: 0.03014019062352735loss: 0.03667306530383206\n",
            "Epoch: 4650NA_erro: 0.03209526917731115 \n",
            "EU_erro: 0.025465365990187772JP_erro: 0.013012402048406676loss: 0.023524345353592272\n",
            "Epoch: 4651NA_erro: 0.07471629353456719 \n",
            "EU_erro: 0.0324353298475576JP_erro: 0.011570965365846028loss: 0.03957419432410898\n",
            "Epoch: 4652NA_erro: 0.6828450047692587 \n",
            "EU_erro: 0.45649303022281146JP_erro: 0.14443566632825275loss: 0.4279245701871177\n",
            "Epoch: 4653NA_erro: 0.22321627121563106 \n",
            "EU_erro: 0.19213218097538912JP_erro: 0.08016955944918847loss: 0.165172676707423\n",
            "Epoch: 4654NA_erro: 0.05899152090383131 \n",
            "EU_erro: 0.038659561050030614JP_erro: 0.05144274512002634loss: 0.049697946208392005\n",
            "Epoch: 4655NA_erro: 0.08183168810467388 \n",
            "EU_erro: 0.03371756899264432JP_erro: 0.02389208290928094loss: 0.046480445898780524\n",
            "Epoch: 4656NA_erro: 0.2285203638002854 \n",
            "EU_erro: 0.17001120619071547JP_erro: 0.024867875631465468loss: 0.141133143920307\n",
            "Epoch: 4657NA_erro: 0.05400993565256281 \n",
            "EU_erro: 0.045710215272829514JP_erro: 0.02401332753573277loss: 0.04124449205028918\n",
            "Epoch: 4658NA_erro: 0.04356430020443229 \n",
            "EU_erro: 0.026071217170981473JP_erro: 0.06090599437092626loss: 0.043513834938522455\n",
            "Epoch: 4659NA_erro: 0.1689242392547371 \n",
            "EU_erro: 0.046084028805873194JP_erro: 0.013408093720443489loss: 0.07613878841548002\n",
            "Epoch: 4660NA_erro: 0.043786870416744735 \n",
            "EU_erro: 0.029164014398589615JP_erro: 0.030238810897797577loss: 0.03439656523771064\n",
            "Epoch: 4661NA_erro: 0.061358393624771475 \n",
            "EU_erro: 0.09387762047523676JP_erro: 0.012060057285220124loss: 0.05576535712840945\n",
            "Epoch: 4662NA_erro: 0.3546888883723769 \n",
            "EU_erro: 0.18953890763511955JP_erro: 0.06008702193119729loss: 0.20143828650777654\n",
            "Epoch: 4663NA_erro: 0.582274873127309 \n",
            "EU_erro: 0.07832481879596562JP_erro: 0.01176895212757495loss: 0.22412287172421005\n",
            "Epoch: 4664NA_erro: 0.09701798128527264 \n",
            "EU_erro: 0.03770547781803811JP_erro: 0.01016379904377368loss: 0.048295753870823584\n",
            "Epoch: 4665NA_erro: 0.33647186072297797 \n",
            "EU_erro: 0.09555481200994448JP_erro: 0.032637518967768946loss: 0.15488807545151823\n",
            "Epoch: 4666NA_erro: 0.10373740233192148 \n",
            "EU_erro: 0.20293095315149587JP_erro: 0.013363000958464866loss: 0.10667712189430414\n",
            "Epoch: 4667NA_erro: 0.06627063880595126 \n",
            "EU_erro: 0.03751015247300614JP_erro: 0.03043539764345154loss: 0.04473872887071713\n",
            "Epoch: 4668NA_erro: 0.10424842206082603 \n",
            "EU_erro: 0.045803106108377144JP_erro: 0.060879514198894646loss: 0.0703103551568911\n",
            "Epoch: 4669NA_erro: 0.32064822293067163 \n",
            "EU_erro: 0.20839561787686606JP_erro: 0.13713962347932565loss: 0.22206114244091418\n",
            "Epoch: 4670NA_erro: 0.10113790977832883 \n",
            "EU_erro: 0.034113371557043505JP_erro: 0.019492081893506902loss: 0.05158112030620723\n",
            "Epoch: 4671NA_erro: 0.1914606537929801 \n",
            "EU_erro: 0.06996629312056904JP_erro: 0.010474849809971891loss: 0.09063393570656\n",
            "Epoch: 4672NA_erro: 0.12509007786595544 \n",
            "EU_erro: 0.03081394720447156JP_erro: 0.018384948719379512loss: 0.05809632382651632\n",
            "Epoch: 4673NA_erro: 0.13648720674736556 \n",
            "EU_erro: 0.11008717292963072JP_erro: 0.014859061370524325loss: 0.08714448573977449\n",
            "Epoch: 4674NA_erro: 0.08193808932637059 \n",
            "EU_erro: 0.04187497981759005JP_erro: 0.02323456281839415loss: 0.049015875010527385\n",
            "Epoch: 4675NA_erro: 0.2869046935739443 \n",
            "EU_erro: 0.14858231063961058JP_erro: 0.1128097560054572loss: 0.1827656021413877\n",
            "Epoch: 4676NA_erro: 0.5281287570332371 \n",
            "EU_erro: 0.23687861686529116JP_erro: 0.15292420867801637loss: 0.3059772188349288\n",
            "Epoch: 4677NA_erro: 0.18146483473075453 \n",
            "EU_erro: 0.2024200905201047JP_erro: 0.008840740535610406loss: 0.1309085723965667\n",
            "Epoch: 4678NA_erro: 0.24110570434452028 \n",
            "EU_erro: 0.23305935453074847JP_erro: 0.04026902738467667loss: 0.17147803491400193\n",
            "Epoch: 4679NA_erro: 0.6639461369477502 \n",
            "EU_erro: 0.4440809959589049JP_erro: 0.0483177197996036loss: 0.38544828577559126\n",
            "Epoch: 4680NA_erro: 0.08771402891292128 \n",
            "EU_erro: 0.024507191292075223JP_erro: 0.014321326978446902loss: 0.042180853296619976\n",
            "Epoch: 4681NA_erro: 0.12184851853422417 \n",
            "EU_erro: 0.044216180956640906JP_erro: 0.007037643429844878loss: 0.05770078001096267\n",
            "Epoch: 4682NA_erro: 5.942607554354409 \n",
            "EU_erro: 2.959375307541485JP_erro: 0.1850993319075237loss: 3.0290272439173025\n",
            "Epoch: 4683NA_erro: 1.1275791937066604 \n",
            "EU_erro: 0.6556523493094037JP_erro: 0.06376314070797706loss: 0.6156649700430936\n",
            "Epoch: 4684NA_erro: 0.11020775913268097 \n",
            "EU_erro: 0.032416995643645294JP_erro: 0.02517255239708479loss: 0.0559324349543845\n",
            "Epoch: 4685NA_erro: 0.1754169685896053 \n",
            "EU_erro: 0.08859800737957622JP_erro: 0.006466966497805692loss: 0.09016063786292261\n",
            "Epoch: 4686NA_erro: 0.06754429303398428 \n",
            "EU_erro: 0.030261654724446377JP_erro: 0.00947502405606499loss: 0.03576032413068668\n",
            "Epoch: 4687NA_erro: 0.22508586099905561 \n",
            "EU_erro: 0.07779387540595475JP_erro: 0.018409577219985252loss: 0.10709645212158676\n",
            "Epoch: 4688NA_erro: 0.07326059563215388 \n",
            "EU_erro: 0.029076176096302593JP_erro: 0.012979482033456018loss: 0.038438753564228385\n",
            "Epoch: 4689NA_erro: 0.06332404853761658 \n",
            "EU_erro: 0.033852739851604134JP_erro: 0.0326452513997869loss: 0.04327401634334594\n",
            "Epoch: 4690NA_erro: 0.09663884029831997 \n",
            "EU_erro: 0.03176851790080699JP_erro: 0.009715420793193255loss: 0.046040933261546055\n",
            "Epoch: 4691NA_erro: 1.7180750351543574 \n",
            "EU_erro: 0.9019110923589662JP_erro: 0.21506233732829722loss: 0.9450162103933881\n",
            "Epoch: 4692NA_erro: 0.6555087806642518 \n",
            "EU_erro: 0.31504527542942257JP_erro: 0.04248370033826015loss: 0.33767925676449323\n",
            "Epoch: 4693NA_erro: 0.09601553281148274 \n",
            "EU_erro: 0.06846277288688246JP_erro: 0.14389269111692443loss: 0.10279032611107641\n",
            "Epoch: 4694NA_erro: 0.2081898386164229 \n",
            "EU_erro: 0.15443715938301975JP_erro: 0.04220347995905913loss: 0.13494350189386412\n",
            "Epoch: 4695NA_erro: 0.14372004094974014 \n",
            "EU_erro: 0.2940395820972531JP_erro: 0.05582053069920503loss: 0.16452672869660134\n",
            "Epoch: 4696NA_erro: 0.5914348040440286 \n",
            "EU_erro: 0.24064021517139997JP_erro: 0.03231991862141809loss: 0.288131643635358\n",
            "Epoch: 4697NA_erro: 0.05862425464068272 \n",
            "EU_erro: 0.02414748188137084JP_erro: 0.01726583570472954loss: 0.03334586010422817\n",
            "Epoch: 4698NA_erro: 0.12059514836747517 \n",
            "EU_erro: 0.09955542032108751JP_erro: 0.09717295336168866loss: 0.10577452275180077\n",
            "Epoch: 4699NA_erro: 0.24622771167015844 \n",
            "EU_erro: 0.18226599508477737JP_erro: 0.06351232066635014loss: 0.16400202300197395\n",
            "Epoch: 4700NA_erro: 0.07609889950863151 \n",
            "EU_erro: 0.028870775718097538JP_erro: 0.013170365908349206loss: 0.03938001717707908\n",
            "Epoch: 4701NA_erro: 0.05784810975540516 \n",
            "EU_erro: 0.03207678018614303JP_erro: 0.010487399128980415loss: 0.03347076417863831\n",
            "Epoch: 4702NA_erro: 0.11805867039880087 \n",
            "EU_erro: 0.03802637490191201JP_erro: 0.009178747386895409loss: 0.05508793878924939\n",
            "Epoch: 4703NA_erro: 0.050637079763782115 \n",
            "EU_erro: 0.018409465172494106JP_erro: 0.012136055517566297loss: 0.027060866817947504\n",
            "Epoch: 4704NA_erro: 0.23219779480335323 \n",
            "EU_erro: 0.053612871687541634JP_erro: 0.011897050133047178loss: 0.09923591170200081\n",
            "Epoch: 4705NA_erro: 5.7780243629633 \n",
            "EU_erro: 2.7814084430073582JP_erro: 0.06837345833002134loss: 2.8759354583976804\n",
            "Epoch: 4706NA_erro: 0.0706167738567027 \n",
            "EU_erro: 0.022825635680856632JP_erro: 0.009490449645722559loss: 0.03431095633395882\n",
            "Epoch: 4707NA_erro: 0.06092553452927937 \n",
            "EU_erro: 0.03743643446486126JP_erro: 0.027869761914245842loss: 0.042077242866042974\n",
            "Epoch: 4708NA_erro: 0.3577450818793718 \n",
            "EU_erro: 0.08388688397961994JP_erro: 0.01157746065494626loss: 0.1510698111482369\n",
            "Epoch: 4709NA_erro: 0.06638657215029695 \n",
            "EU_erro: 0.0502152729404065JP_erro: 0.023241179396015728loss: 0.04661433918531551\n",
            "Epoch: 4710NA_erro: 0.30317696490029034 \n",
            "EU_erro: 0.30162293781605803JP_erro: 0.058665418809698534loss: 0.22115515184032825\n",
            "Epoch: 4711NA_erro: 0.07947578910709352 \n",
            "EU_erro: 0.023283501004063806JP_erro: 0.04827670348707096loss: 0.05034533119940943\n",
            "Epoch: 4712NA_erro: 0.18521318139955978 \n",
            "EU_erro: 0.11566415313602418JP_erro: 0.006063802297725234loss: 0.10231371073759804\n",
            "Epoch: 4713NA_erro: 0.746039678884107 \n",
            "EU_erro: 0.19828478495279947JP_erro: 0.023044532583665477loss: 0.3224563228991605\n",
            "Epoch: 4714NA_erro: 0.17680092375407847 \n",
            "EU_erro: 0.23572276729021885JP_erro: 0.05778871303380922loss: 0.15677080597988394\n",
            "Epoch: 4715NA_erro: 6.298238473345143 \n",
            "EU_erro: 3.067813843719719JP_erro: 0.15528032021929128loss: 3.173777558082758\n",
            "Epoch: 4716NA_erro: 0.09210732556128687 \n",
            "EU_erro: 0.027778333471727JP_erro: 0.014178294313046359loss: 0.04468798637390137\n",
            "Epoch: 4717NA_erro: 0.08856641229733016 \n",
            "EU_erro: 0.08332122203915618JP_erro: 0.14953338822653128loss: 0.1071403377739958\n",
            "Epoch: 4718NA_erro: 0.09128597355628199 \n",
            "EU_erro: 0.05104600459106209JP_erro: 0.010357791370199633loss: 0.05089659099431001\n",
            "Epoch: 4719NA_erro: 0.22366954374682996 \n",
            "EU_erro: 0.18465262050776518JP_erro: 0.0373283583064412loss: 0.14855017033658285\n",
            "Epoch: 4720NA_erro: 0.11629027913707171 \n",
            "EU_erro: 0.05351968975954278JP_erro: 0.022067707176356352loss: 0.06395923074825789\n",
            "Epoch: 4721NA_erro: 0.04321545593498289 \n",
            "EU_erro: 0.039473693500193516JP_erro: 0.08130544839903366loss: 0.05466487056525179\n",
            "Epoch: 4722NA_erro: 0.716482354689014 \n",
            "EU_erro: 0.12259599774382836JP_erro: 0.07288673589395922loss: 0.30398838279783263\n",
            "Epoch: 4723NA_erro: 0.3733163656190384 \n",
            "EU_erro: 0.13587716937989228JP_erro: 0.06078232628430507loss: 0.1899919583815937\n",
            "Epoch: 4724NA_erro: 0.03714221392491067 \n",
            "EU_erro: 0.0684401185013527JP_erro: 0.10239966155946717loss: 0.06932733594909195\n",
            "Epoch: 4725NA_erro: 0.08697887723760088 \n",
            "EU_erro: 0.03156858590222145JP_erro: 0.014817498682081237loss: 0.04445499227952587\n",
            "Epoch: 4726NA_erro: 0.08276121560917345 \n",
            "EU_erro: 0.08244255716486494JP_erro: 0.013684601515762566loss: 0.05962946156198664\n",
            "Epoch: 4727NA_erro: 0.06648858388264973 \n",
            "EU_erro: 0.04196867000224978JP_erro: 0.010460136934768322loss: 0.03963913104330846\n",
            "Epoch: 4728NA_erro: 0.04439739755881849 \n",
            "EU_erro: 0.040987009225889694JP_erro: 0.00978916421417118loss: 0.031724521356035575\n",
            "Epoch: 4729NA_erro: 0.7975857017576232 \n",
            "EU_erro: 0.6537247443383978JP_erro: 0.05844478459321251loss: 0.5032517558844515\n",
            "Epoch: 4730NA_erro: 0.3116635389106218 \n",
            "EU_erro: 0.2356443294259005JP_erro: 0.1458201205083566loss: 0.23104266602863638\n",
            "Epoch: 4731NA_erro: 0.5163717269897461 \n",
            "EU_erro: 0.39931323177130645JP_erro: 0.07745128731394924loss: 0.33104541689850564\n",
            "Epoch: 4732NA_erro: 0.5233631577602652 \n",
            "EU_erro: 0.2061000350833863JP_erro: 0.07145147452982821loss: 0.26697155117064486\n",
            "Epoch: 4733NA_erro: 0.10972021162047867 \n",
            "EU_erro: 0.019206827463105668JP_erro: 0.02314379510953445loss: 0.05069028037463048\n",
            "Epoch: 4734NA_erro: 0.15764667082202527 \n",
            "EU_erro: 0.03694697405940803JP_erro: 0.02616849518561548loss: 0.07358738156252129\n",
            "Epoch: 4735NA_erro: 0.06988755492276924 \n",
            "EU_erro: 0.032223310581473416JP_erro: 0.02790391907211422loss: 0.04333825998528059\n",
            "Epoch: 4736NA_erro: 0.22780568100685297 \n",
            "EU_erro: 0.039181612258733706JP_erro: 0.00843733780143797loss: 0.09180821189584658\n",
            "Epoch: 4737NA_erro: 0.6532810270324234 \n",
            "EU_erro: 0.517694635908733JP_erro: 0.045962638633195746loss: 0.4056461097658143\n",
            "Epoch: 4738NA_erro: 0.10296071222586226 \n",
            "EU_erro: 0.042397536972696465JP_erro: 0.024787076683931573loss: 0.05671510862749676\n",
            "Epoch: 4739NA_erro: 0.10637304579564767 \n",
            "EU_erro: 0.08794700452523638JP_erro: 0.01455813299777896loss: 0.06962605687074883\n",
            "Epoch: 4740NA_erro: 0.1064651419025983 \n",
            "EU_erro: 0.047592192657234134JP_erro: 0.03137201994888542loss: 0.06180979022684023\n",
            "Epoch: 4741NA_erro: 0.15675110410350238 \n",
            "EU_erro: 0.06821690141692642JP_erro: 0.010818491379419962loss: 0.07859549781148747\n",
            "Epoch: 4742NA_erro: 0.13153265612994053 \n",
            "EU_erro: 0.06152189517206JP_erro: 0.0941573741824128loss: 0.09573731311531954\n",
            "Epoch: 4743NA_erro: 0.1837583105693492 \n",
            "EU_erro: 0.1797628402709961JP_erro: 0.037878111813419546loss: 0.13379976730938106\n",
            "Epoch: 4744NA_erro: 5.897793437159339 \n",
            "EU_erro: 2.884666975154433JP_erro: 0.10465382605560067loss: 2.9623714713163154\n",
            "Epoch: 4745NA_erro: 0.6863938560781553 \n",
            "EU_erro: 0.06374427052431328JP_erro: 0.012402412041213161loss: 0.2541801726171213\n",
            "Epoch: 4746NA_erro: 0.43273386105086453 \n",
            "EU_erro: 0.16599265179892844JP_erro: 0.042509247166241784loss: 0.21374525025833485\n",
            "Epoch: 4747NA_erro: 0.7663435529368793 \n",
            "EU_erro: 0.4991288887437924JP_erro: 0.059642509896625846loss: 0.44170497923858404\n",
            "Epoch: 4748NA_erro: 0.09794537411179653 \n",
            "EU_erro: 0.04324467607246813JP_erro: 0.04551243874453759loss: 0.062234166056610814\n",
            "Epoch: 4749NA_erro: 0.10089507398679275 \n",
            "EU_erro: 0.1878312391828197JP_erro: 0.009439861936162608loss: 0.09938873061838076\n",
            "Epoch: 4750NA_erro: 0.07067733032758845 \n",
            "EU_erro: 0.04420593265415162JP_erro: 0.058905426845994105loss: 0.05792956481608309\n",
            "Epoch: 4751NA_erro: 0.6176137924194336 \n",
            "EU_erro: 0.34447710643443025JP_erro: 0.09697136028792507loss: 0.3530207715293234\n",
            "Epoch: 4752NA_erro: 0.4470322852910951 \n",
            "EU_erro: 0.42628510053767715JP_erro: 0.23329686748889067loss: 0.3688714300939279\n",
            "Epoch: 4753NA_erro: 0.07793886716975722 \n",
            "EU_erro: 0.025235818800076033JP_erro: 0.005470316770464875loss: 0.03621500129847564\n",
            "Epoch: 4754NA_erro: 0.22843033768409907 \n",
            "EU_erro: 0.04459027172059052JP_erro: 0.01389834769936495loss: 0.09563965390818988\n",
            "Epoch: 4755NA_erro: 0.06705401017684345 \n",
            "EU_erro: 0.016029351210409357JP_erro: 0.013738965341287065loss: 0.032274110834727913\n",
            "Epoch: 4756NA_erro: 0.1447374783745108 \n",
            "EU_erro: 0.11218421218931213JP_erro: 0.09010021076645962loss: 0.11567397635112438\n",
            "Epoch: 4757NA_erro: 0.05825969137886698 \n",
            "EU_erro: 0.07327181886332904JP_erro: 0.0054805125145949134loss: 0.04567067290461341\n",
            "Epoch: 4758NA_erro: 0.04657183506692103 \n",
            "EU_erro: 0.027356297932853994JP_erro: 0.011101705852405046loss: 0.02834328154260798\n",
            "Epoch: 4759NA_erro: 0.2024188152579374 \n",
            "EU_erro: 0.13339384581691535JP_erro: 0.08611094119936921loss: 0.14064120691876078\n",
            "Epoch: 4760NA_erro: 0.22140100020770878 \n",
            "EU_erro: 0.028081004933793414JP_erro: 0.008334919463756472loss: 0.08593897486841956\n",
            "Epoch: 4761NA_erro: 0.06031664304955061 \n",
            "EU_erro: 0.020628703194995258JP_erro: 0.06319338037062061loss: 0.04804624143496964\n",
            "Epoch: 4762NA_erro: 0.22305301917615786 \n",
            "EU_erro: 0.03579935131146926JP_erro: 0.01167201603105826loss: 0.09017479512118554\n",
            "Epoch: 4763NA_erro: 0.17115038494731105 \n",
            "EU_erro: 0.03138396629067355JP_erro: 0.025638358075489368loss: 0.07605756900107213\n",
            "Epoch: 4764NA_erro: 0.24343499841616134 \n",
            "EU_erro: 0.0369016075319098JP_erro: 0.014274188483408255loss: 0.09820359621861184\n",
            "Epoch: 4765NA_erro: 0.16194999680038571 \n",
            "EU_erro: 0.03716648087020992JP_erro: 0.021025189595629077loss: 0.07338055806566579\n",
            "Epoch: 4766NA_erro: 5.892280756041061 \n",
            "EU_erro: 2.9438077941421392JP_erro: 0.06241229153418726loss: 2.966166947239129\n",
            "Epoch: 4767NA_erro: 5.726994359216024 \n",
            "EU_erro: 2.8234940166621243JP_erro: 0.047104552734729854loss: 2.8658640661904977\n",
            "Epoch: 4768NA_erro: 0.10958651239557783 \n",
            "EU_erro: 0.21406216214793597JP_erro: 0.056611683017523715loss: 0.12675346330154774\n",
            "Epoch: 4769NA_erro: 0.12560492338136184 \n",
            "EU_erro: 0.08336663246154785JP_erro: 0.008873327873473945loss: 0.07261497105738914\n",
            "Epoch: 4770NA_erro: 0.12759883274403652 \n",
            "EU_erro: 0.033111225726992585JP_erro: 0.0213841204495393loss: 0.06069806657096212\n",
            "Epoch: 4771NA_erro: 0.7869404046110404 \n",
            "EU_erro: 0.5979254079419513JP_erro: 0.06271922773168992loss: 0.48252841298894367\n",
            "Epoch: 4772NA_erro: 0.07087485272754994 \n",
            "EU_erro: 0.04157677177310914JP_erro: 0.04120753717052844loss: 0.05121971747671911\n",
            "Epoch: 4773NA_erro: 0.06996084091275237 \n",
            "EU_erro: 0.0260931322740954JP_erro: 0.01753825203392857loss: 0.03786407699880674\n",
            "Epoch: 4774NA_erro: 0.17238226971885032 \n",
            "EU_erro: 0.04888621873633806JP_erro: 0.08502390957617945loss: 0.10209747063097104\n",
            "Epoch: 4775NA_erro: 0.565528389095336 \n",
            "EU_erro: 0.17062909843385682JP_erro: 0.06914705731147944loss: 0.26843488678451655\n",
            "Epoch: 4776NA_erro: 0.19032467243283294 \n",
            "EU_erro: 0.08027608080427776JP_erro: 0.09338167286658472loss: 0.12132747228755507\n",
            "Epoch: 4777NA_erro: 0.25819207346716594 \n",
            "EU_erro: 0.12856488079987755JP_erro: 0.10157197944877684loss: 0.16277632972066716\n",
            "Epoch: 4778NA_erro: 0.13994727023812228 \n",
            "EU_erro: 0.08868320043696913JP_erro: 0.0244926135669383loss: 0.08437436680461086\n",
            "Epoch: 4779NA_erro: 0.05702760330466337 \n",
            "EU_erro: 0.033829889094182684JP_erro: 0.026716582996900693loss: 0.03919135692507722\n",
            "Epoch: 4780NA_erro: 0.10466648626697156 \n",
            "EU_erro: 0.1864382647728735JP_erro: 0.03259776867637339loss: 0.10790083759514861\n",
            "Epoch: 4781NA_erro: 0.06442603676818139 \n",
            "EU_erro: 0.03832901402037273JP_erro: 0.019894293112348218loss: 0.040883116943891656\n",
            "Epoch: 4782NA_erro: 0.04878869352414626 \n",
            "EU_erro: 0.02177587544271188JP_erro: 0.06676880426185075loss: 0.04577779954718065\n",
            "Epoch: 4783NA_erro: 0.054782005243523176 \n",
            "EU_erro: 0.047512451807657875JP_erro: 0.007581421455671621loss: 0.03662529379822487\n",
            "Epoch: 4784NA_erro: 0.7560573252596596 \n",
            "EU_erro: 0.07997218952622524JP_erro: 0.007197504011235496loss: 0.28107567350993784\n",
            "Epoch: 4785NA_erro: 0.10358813197113746 \n",
            "EU_erro: 0.06580599980760915JP_erro: 0.004832224501657855loss: 0.05807545758032984\n",
            "Epoch: 4786NA_erro: 0.12290190356646398 \n",
            "EU_erro: 0.01323819969051568JP_erro: 0.014183126216711loss: 0.05010773969251056\n",
            "Epoch: 4787NA_erro: 0.16008696814840154 \n",
            "EU_erro: 0.037599279899005744JP_erro: 0.029518555763155915loss: 0.07573493691377861\n",
            "Epoch: 4788NA_erro: 0.08773015451061633 \n",
            "EU_erro: 0.06925197072731432JP_erro: 0.01715232582055321loss: 0.058044818944709246\n",
            "Epoch: 4789NA_erro: 0.4939007943914842 \n",
            "EU_erro: 0.21799934002780175JP_erro: 0.04817482112914093loss: 0.2533583308375159\n",
            "Epoch: 4790NA_erro: 0.4849875989810441 \n",
            "EU_erro: 0.21437598753345105JP_erro: 0.028655503609383754loss: 0.2426730569942977\n",
            "Epoch: 4791NA_erro: 0.26069541310155114 \n",
            "EU_erro: 0.0470807062563046JP_erro: 0.008778257887492808loss: 0.10551812112793442\n",
            "Epoch: 4792NA_erro: 0.06794033586516861 \n",
            "EU_erro: 0.05143878533858662JP_erro: 0.01904231633326804loss: 0.046140482259351155\n",
            "Epoch: 4793NA_erro: 0.0929280953813893 \n",
            "EU_erro: 0.02241707356401192JP_erro: 0.0930357256601023loss: 0.06946030051209205\n",
            "Epoch: 4794NA_erro: 0.15103682067043098 \n",
            "EU_erro: 0.0429939022359922JP_erro: 0.02842161544533663loss: 0.0741507794505866\n",
            "Epoch: 4795NA_erro: 0.07402100766351981 \n",
            "EU_erro: 0.03633147986360299JP_erro: 0.09039330852124118loss: 0.06691526996996976\n",
            "Epoch: 4796NA_erro: 0.03527937702430311 \n",
            "EU_erro: 0.03829022479611774JP_erro: 0.036691609741181366loss: 0.036753737187200736\n",
            "Epoch: 4797NA_erro: 0.302181133004122 \n",
            "EU_erro: 0.13966467029364535JP_erro: 0.11947795402172loss: 0.18710790678512218\n",
            "Epoch: 4798NA_erro: 0.05293855371401292 \n",
            "EU_erro: 0.02713629210642142JP_erro: 0.02737678529680237loss: 0.035817209602326386\n",
            "Epoch: 4799NA_erro: 0.08632292119107506 \n",
            "EU_erro: 0.05935986374699792JP_erro: 0.012955458820328232loss: 0.05287941574126251\n",
            "Epoch: 4800NA_erro: 0.06809077983678774 \n",
            "EU_erro: 0.09594528249991957JP_erro: 0.013036146875499754loss: 0.059024067812187726\n",
            "Epoch: 4801NA_erro: 0.04005733848542206 \n",
            "EU_erro: 0.02503110918887826JP_erro: 0.00864285311495611loss: 0.024577101995778637\n",
            "Epoch: 4802NA_erro: 0.6357014086819435 \n",
            "EU_erro: 0.11161620302717815JP_erro: 0.019942732282387195loss: 0.25575343952622526\n",
            "Epoch: 4803NA_erro: 0.11293155278346335 \n",
            "EU_erro: 0.08440061133037242JP_erro: 0.008864441467810047loss: 0.06873220436332762\n",
            "Epoch: 4804NA_erro: 0.05919527637866116 \n",
            "EU_erro: 0.06308823130851568JP_erro: 0.025439936985341152loss: 0.049241148224172665\n",
            "Epoch: 4805NA_erro: 0.08768722992534786 \n",
            "EU_erro: 0.025429171185160793JP_erro: 0.00955237840959268loss: 0.040889590285545174\n",
            "Epoch: 4806NA_erro: 0.09808192881502846 \n",
            "EU_erro: 0.02573244793470516JP_erro: 0.022451993106871612loss: 0.04875546277955521\n",
            "Epoch: 4807NA_erro: 0.07121949694877447 \n",
            "EU_erro: 0.06632469421209292JP_erro: 0.02069092536157416loss: 0.052745037300642145\n",
            "Epoch: 4808NA_erro: 0.07291554942611576 \n",
            "EU_erro: 0.03554906031882116JP_erro: 0.016521879637888235loss: 0.04166216351265131\n",
            "Epoch: 4809NA_erro: 0.41992501695026724 \n",
            "EU_erro: 0.3476447098014891JP_erro: 0.1675361625907957loss: 0.3117019815962444\n",
            "Epoch: 4810NA_erro: 0.5497502171716024 \n",
            "EU_erro: 0.11052294294963512JP_erro: 0.07420165132182513loss: 0.24482496025026307\n",
            "Epoch: 4811NA_erro: 0.09133338004119637 \n",
            "EU_erro: 0.07349242073620936JP_erro: 0.031961643880651903loss: 0.06559581719627676\n",
            "Epoch: 4812NA_erro: 0.33189725506213286 \n",
            "EU_erro: 0.20744107490362124JP_erro: 0.14217538427012835loss: 0.22717124731965768\n",
            "Epoch: 4813NA_erro: 0.07379769816879155 \n",
            "EU_erro: 0.0338863217553427JP_erro: 0.011904002853142198loss: 0.03986267618430677\n",
            "Epoch: 4814NA_erro: 0.7145577068476714 \n",
            "EU_erro: 0.4665921824847081JP_erro: 0.046294082042782805loss: 0.40914801664130634\n",
            "Epoch: 4815NA_erro: 0.04009664058685303 \n",
            "EU_erro: 0.032760177471841026JP_erro: 0.025326630403829174loss: 0.03272781769434611\n",
            "Epoch: 4816NA_erro: 5.683600255685259 \n",
            "EU_erro: 2.8119189240211666JP_erro: 0.08056015007255614loss: 2.8586930267570554\n",
            "Epoch: 4817NA_erro: 0.08364511090655659 \n",
            "EU_erro: 0.04426035308098608JP_erro: 0.011850708676863086loss: 0.046585391658221106\n",
            "Epoch: 4818NA_erro: 6.270975512127544 \n",
            "EU_erro: 2.810809704684472JP_erro: 0.08404724357664123loss: 3.05527738822523\n",
            "Epoch: 4819NA_erro: 0.1621379963187284 \n",
            "EU_erro: 0.14631817507189374JP_erro: 0.050879966380984284loss: 0.11977871259053548\n",
            "Epoch: 4820NA_erro: 0.5222527555717055 \n",
            "EU_erro: 0.21774585856947787JP_erro: 0.03098549537880476loss: 0.2569947131844454\n",
            "Epoch: 4821NA_erro: 0.09632509808207668 \n",
            "EU_erro: 0.043291163074877834JP_erro: 0.010219738464946894loss: 0.04994533320730047\n",
            "Epoch: 4822NA_erro: 0.16992079195126084 \n",
            "EU_erro: 0.14970497567524282JP_erro: 0.1000540108643761loss: 0.1398932564166165\n",
            "Epoch: 4823NA_erro: 0.09018217870431353 \n",
            "EU_erro: 0.024381614932718204JP_erro: 0.018328186846518702loss: 0.04429732644280722\n",
            "Epoch: 4824NA_erro: 0.037769444229066834 \n",
            "EU_erro: 0.025888189326885136JP_erro: 0.01049139702966971loss: 0.02471634352854056\n",
            "Epoch: 4825NA_erro: 0.053649475408154866 \n",
            "EU_erro: 0.06594074788943742JP_erro: 0.00997938735540523loss: 0.04318987214288046\n",
            "Epoch: 4826NA_erro: 0.11668732924054759 \n",
            "EU_erro: 0.19374357637508896JP_erro: 0.0164502154487048loss: 0.1089603863945303\n",
            "Epoch: 4827NA_erro: 0.08698691693387291 \n",
            "EU_erro: 0.04369784233181975JP_erro: 0.012393099393031393loss: 0.04769261955290802\n",
            "Epoch: 4828NA_erro: 0.6817551546318587 \n",
            "EU_erro: 0.15040375465570494JP_erro: 0.07470777792523997loss: 0.3022888834162276\n",
            "Epoch: 4829NA_erro: 0.3844211267870526 \n",
            "EU_erro: 0.2316467336906019JP_erro: 0.1576155655143797loss: 0.2578944938127385\n",
            "Epoch: 4830NA_erro: 0.08880691010822621 \n",
            "EU_erro: 0.0768495853557143JP_erro: 0.016718297734741094loss: 0.0607915996581085\n",
            "Epoch: 4831NA_erro: 0.10578808858413105 \n",
            "EU_erro: 0.02551791510840719JP_erro: 0.026709585226783458loss: 0.052671862203021384\n",
            "Epoch: 4832NA_erro: 0.2648741884749065 \n",
            "EU_erro: 0.18452840257984723JP_erro: 0.04124923731929572loss: 0.16355061715887498\n",
            "Epoch: 4833NA_erro: 0.8039642304413078 \n",
            "EU_erro: 0.4502039547114409JP_erro: 0.0718619703322418loss: 0.44201008109159245\n",
            "Epoch: 4834NA_erro: 5.984695197999939 \n",
            "EU_erro: 2.7904214785080548JP_erro: 0.045193322869234304loss: 2.9401033978129543\n",
            "Epoch: 4835NA_erro: 0.08002572281416072 \n",
            "EU_erro: 0.021499291408893673JP_erro: 0.0847731911858847loss: 0.06209940411323725\n",
            "Epoch: 4836NA_erro: 0.5339599210162496 \n",
            "EU_erro: 0.11304970859557159JP_erro: 0.07576453131298687loss: 0.24092472800912784\n",
            "Epoch: 4837NA_erro: 0.06603891535322795 \n",
            "EU_erro: 0.11757764705391817JP_erro: 0.011138866345087687loss: 0.06491847740587338\n",
            "Epoch: 4838NA_erro: 0.05326260429944179 \n",
            "EU_erro: 0.032077080519624455JP_erro: 0.038608910963516825loss: 0.04131619782410851\n",
            "Epoch: 4839NA_erro: 0.05997768206189769 \n",
            "EU_erro: 0.08433546206747838JP_erro: 0.009158961763677672loss: 0.05115736824597499\n",
            "Epoch: 4840NA_erro: 0.10598335155220918 \n",
            "EU_erro: 0.03553814897241518JP_erro: 0.01361563570739687loss: 0.051712384519650954\n",
            "Epoch: 4841NA_erro: 0.6311166748520016 \n",
            "EU_erro: 0.054928313854128816JP_erro: 0.012588124404581942loss: 0.232877675877061\n",
            "Epoch: 4842NA_erro: 0.06979695586271065 \n",
            "EU_erro: 0.05806693735048753JP_erro: 0.029633719791737637loss: 0.0524992027948069\n",
            "Epoch: 4843NA_erro: 6.174599107845809 \n",
            "EU_erro: 2.982806124428446JP_erro: 0.0668265699416168loss: 3.074744468511537\n",
            "Epoch: 4844NA_erro: 0.07706649081651555 \n",
            "EU_erro: 0.04971032918885697JP_erro: 0.015191043070120406loss: 0.04732262256533601\n",
            "Epoch: 4845NA_erro: 0.3742090491361396 \n",
            "EU_erro: 0.18306299697521122JP_erro: 0.053322056467218916loss: 0.20353136136550312\n",
            "Epoch: 4846NA_erro: 0.21200841711473095 \n",
            "EU_erro: 0.060258969780086546JP_erro: 0.017825931079627932loss: 0.09669777034789093\n",
            "Epoch: 4847NA_erro: 0.08183888686719791 \n",
            "EU_erro: 0.02206273781236752JP_erro: 0.012162340472834978loss: 0.038687988769176396\n",
            "Epoch: 4848NA_erro: 0.18423703289771265 \n",
            "EU_erro: 0.18354188564211824JP_erro: 0.024567927963049838loss: 0.13078229371891464\n",
            "Epoch: 4849NA_erro: 0.1626088637714238 \n",
            "EU_erro: 0.20471047985461333JP_erro: 0.062318254810895105loss: 0.14321253281231075\n",
            "Epoch: 4850NA_erro: 0.601633352826732 \n",
            "EU_erro: 0.08254711942155232JP_erro: 0.011083923800047054loss: 0.23175481677979462\n",
            "Epoch: 4851NA_erro: 0.13718702996424 \n",
            "EU_erro: 0.030142621014469354JP_erro: 0.008096560373786808loss: 0.0584754093672878\n",
            "Epoch: 4852NA_erro: 0.6750830569008525 \n",
            "EU_erro: 0.44964324596316313JP_erro: 0.06143347237461297loss: 0.3953866441120473\n",
            "Epoch: 4853NA_erro: 0.17871451932330465 \n",
            "EU_erro: 0.056332913480063744JP_erro: 0.020902646604434465loss: 0.08531669313593428\n",
            "Epoch: 4854NA_erro: 0.2374336701030879 \n",
            "EU_erro: 0.03542411465977514JP_erro: 0.0184359961701918loss: 0.09709792543751325\n",
            "Epoch: 4855NA_erro: 0.17845595529837202 \n",
            "EU_erro: 0.06310250870017119JP_erro: 0.013034337943838549loss: 0.08486427077951357\n",
            "Epoch: 4856NA_erro: 0.5291222786718561 \n",
            "EU_erro: 0.1032930196717728JP_erro: 0.08091920105985893loss: 0.23777815722679907\n",
            "Epoch: 4857NA_erro: 0.3409218603326369 \n",
            "EU_erro: 0.053472985592923426JP_erro: 0.009907634221306142loss: 0.13476749723271805\n",
            "Epoch: 4858NA_erro: 0.8079468187435653 \n",
            "EU_erro: 0.3489061473876007JP_erro: 0.034462911214015284loss: 0.3971053278723428\n",
            "Epoch: 4859NA_erro: 0.7388604334158491 \n",
            "EU_erro: 0.4962028089419816JP_erro: 0.05508562853169996loss: 0.43004963749138886\n",
            "Epoch: 4860NA_erro: 0.65409778624542 \n",
            "EU_erro: 0.35970329314239263JP_erro: 0.038814826529155404loss: 0.3508719732595045\n",
            "Epoch: 4861NA_erro: 0.23681293162264566 \n",
            "EU_erro: 0.3083278412042662JP_erro: 0.1196534134620844loss: 0.2215980928997661\n",
            "Epoch: 4862NA_erro: 0.1859222271645716 \n",
            "EU_erro: 0.023656697698341785JP_erro: 0.012385669604752415loss: 0.07398819738580276\n",
            "Epoch: 4863NA_erro: 0.08714989174244016 \n",
            "EU_erro: 0.04528123741002046JP_erro: 0.10830289633699165loss: 0.08024468902469606\n",
            "Epoch: 4864NA_erro: 0.06900225498879603 \n",
            "EU_erro: 0.028749207655588787JP_erro: 0.011849181596622911loss: 0.03653354885042176\n",
            "Epoch: 4865NA_erro: 0.45781656753185185 \n",
            "EU_erro: 0.1786980148433715JP_erro: 0.05568961764490882loss: 0.23073471793832706\n",
            "Epoch: 4866NA_erro: 0.664653038793756 \n",
            "EU_erro: 0.3270823456520258JP_erro: 0.04833045393921608loss: 0.3466886328172314\n",
            "Epoch: 4867NA_erro: 0.8883020859356074 \n",
            "EU_erro: 0.5641181339589201JP_erro: 0.09470009988592576loss: 0.5157068163849586\n",
            "Epoch: 4868NA_erro: 0.23034097612366194 \n",
            "EU_erro: 0.31706924586333046JP_erro: 0.11546631192052087loss: 0.22095885387686795\n",
            "Epoch: 4869NA_erro: 0.08170418037000553 \n",
            "EU_erro: 0.03376430088235426JP_erro: 0.037932659304419226loss: 0.05113371120866879\n",
            "Epoch: 4870NA_erro: 0.04798924738122511 \n",
            "EU_erro: 0.10089295779087747JP_erro: 0.08923669194066247loss: 0.07937297340511351\n",
            "Epoch: 4871NA_erro: 0.2637783131858175 \n",
            "EU_erro: 0.16221820846084475JP_erro: 0.0316048823585806loss: 0.15253380287525264\n",
            "Epoch: 4872NA_erro: 0.05900521149006925 \n",
            "EU_erro: 0.210827579794004JP_erro: 0.16333132751228274loss: 0.14438804729964383\n",
            "Epoch: 4873NA_erro: 0.07705737916074058 \n",
            "EU_erro: 0.0336847795072452JP_erro: 0.0094078717998756loss: 0.04005001034847526\n",
            "Epoch: 4874NA_erro: 0.15373468399047852 \n",
            "EU_erro: 0.0626246457876161JP_erro: 0.1771582189456437loss: 0.1311725239421046\n",
            "Epoch: 4875NA_erro: 0.7228572608888612 \n",
            "EU_erro: 0.2029962133067523JP_erro: 0.07204234600067139loss: 0.33263191696285277\n",
            "Epoch: 4876NA_erro: 0.20591549171033754 \n",
            "EU_erro: 0.04546661709630212JP_erro: 0.03183736357578011loss: 0.09440649387448333\n",
            "Epoch: 4877NA_erro: 0.2122493677361067 \n",
            "EU_erro: 0.1885994090590366JP_erro: 0.0500781914984533loss: 0.15030899713205736\n",
            "Epoch: 4878NA_erro: 0.3527207707249841 \n",
            "EU_erro: 0.25640535724255464JP_erro: 0.1574605195097221loss: 0.25552890097448067\n",
            "Epoch: 4879NA_erro: 0.09351104728935301 \n",
            "EU_erro: 0.028065071087474972JP_erro: 0.022040420724439992loss: 0.047872182010680206\n",
            "Epoch: 4880NA_erro: 0.6265383727790773 \n",
            "EU_erro: 0.09088419204534487JP_erro: 0.02785638783329217loss: 0.24842631909274315\n",
            "Epoch: 4881NA_erro: 0.04354280556819236 \n",
            "EU_erro: 0.02900867037070814JP_erro: 0.05547569241634635loss: 0.042675728945769084\n",
            "Epoch: 4882NA_erro: 0.2387747468874436 \n",
            "EU_erro: 0.20437704500301865JP_erro: 0.04111123177432274loss: 0.16142100326774655\n",
            "Epoch: 4883NA_erro: 0.2851786909177322 \n",
            "EU_erro: 0.0823729814485062JP_erro: 0.03505804742029471loss: 0.13420324916987456\n",
            "Epoch: 4884NA_erro: 0.05991248659385267 \n",
            "EU_erro: 0.016391973855883576JP_erro: 0.013713484355645586loss: 0.030005983141965644\n",
            "Epoch: 4885NA_erro: 0.03882080547569334 \n",
            "EU_erro: 0.03396369228067324JP_erro: 0.011269444412039233loss: 0.028017978797587315\n",
            "Epoch: 4886NA_erro: 0.11610418327094972 \n",
            "EU_erro: 0.02864702727443488JP_erro: 0.057832875917124195loss: 0.06752802882083626\n",
            "Epoch: 4887NA_erro: 1.2289619445800781 \n",
            "EU_erro: 0.4967546832653903JP_erro: 0.037345486555912695loss: 0.5876873445141223\n",
            "Epoch: 4888NA_erro: 5.762712936992793 \n",
            "EU_erro: 2.7817978969840116JP_erro: 0.05147441875102908loss: 2.8653285300084788\n",
            "Epoch: 4889NA_erro: 0.043607571328333185 \n",
            "EU_erro: 0.07245083187901696JP_erro: 0.013162799814874811loss: 0.04307373549587043\n",
            "Epoch: 4890NA_erro: 0.08989931076996086 \n",
            "EU_erro: 0.04114517869875413JP_erro: 0.011522380649581436loss: 0.04752228888430337\n",
            "Epoch: 4891NA_erro: 1.1386231858600941 \n",
            "EU_erro: 0.5743653275245844JP_erro: 0.19545309303342834loss: 0.6361471959786822\n",
            "Epoch: 4892NA_erro: 0.6711852642916893 \n",
            "EU_erro: 0.38475328637648JP_erro: 0.05002054595207983loss: 0.368653046068295\n",
            "Epoch: 4893NA_erro: 0.5056623710218326 \n",
            "EU_erro: 0.2229121489118236JP_erro: 0.027404963508132818loss: 0.25199317192846493\n",
            "Epoch: 4894NA_erro: 0.4339415897694669 \n",
            "EU_erro: 0.37127188009809153JP_erro: 0.1442086326983548loss: 0.3164740126262339\n",
            "Epoch: 4895NA_erro: 0.6809765423915183 \n",
            "EU_erro: 0.3225244108096574JP_erro: 0.05525565424630808loss: 0.35291886144830276\n",
            "Epoch: 4896NA_erro: 0.15944870867470437 \n",
            "EU_erro: 0.06946739762328392JP_erro: 0.010390654783840327loss: 0.07976893306702607\n",
            "Epoch: 4897NA_erro: 0.5231976324273634 \n",
            "EU_erro: 0.23042956063913744JP_erro: 0.03106126258539599loss: 0.2615628316420917\n",
            "Epoch: 4898NA_erro: 0.6327501193497532 \n",
            "EU_erro: 0.31585911447687665JP_erro: 0.05385218664657238loss: 0.33415380374405734\n",
            "Epoch: 4899NA_erro: 0.053270893503529156 \n",
            "EU_erro: 0.059923196947851844JP_erro: 0.11841612268787946loss: 0.07720340591992518\n",
            "Epoch: 4900NA_erro: 0.0644965643106505 \n",
            "EU_erro: 0.03792383874109549JP_erro: 0.016320376664169075loss: 0.039580261060433795\n",
            "Epoch: 4901NA_erro: 0.06566534208696942 \n",
            "EU_erro: 0.05237179671147073JP_erro: 0.011331908000532047loss: 0.04312301790991495\n",
            "Epoch: 4902NA_erro: 0.1876945643461952 \n",
            "EU_erro: 0.14623611472373785JP_erro: 0.0653059491815493loss: 0.13307888378468594\n",
            "Epoch: 4903NA_erro: 0.7210815045260643 \n",
            "EU_erro: 0.4706956064978311JP_erro: 0.04095132036726604loss: 0.41090950485347777\n",
            "Epoch: 4904NA_erro: 0.6272926626279373 \n",
            "EU_erro: 0.2759991505349329JP_erro: 0.06998989933220916loss: 0.32442724981973337\n",
            "Epoch: 4905NA_erro: 0.24418980576271235 \n",
            "EU_erro: 0.2596964947012968JP_erro: 0.035774416701738226loss: 0.17988691034243087\n",
            "Epoch: 4906NA_erro: 0.051186371219250584 \n",
            "EU_erro: 0.025465056415676146JP_erro: 0.009963344926981962loss: 0.02887159123901249\n",
            "Epoch: 4907NA_erro: 0.11272476625072864 \n",
            "EU_erro: 0.03792345061782719JP_erro: 0.013013099746186604loss: 0.054553770279699515\n",
            "Epoch: 4908NA_erro: 0.8271815425665804 \n",
            "EU_erro: 0.345440243565759JP_erro: 0.04842086817867072loss: 0.4070142258045285\n",
            "Epoch: 4909NA_erro: 0.2330445688824321 \n",
            "EU_erro: 0.127587872882222JP_erro: 0.05630629469257917loss: 0.13897956803787587\n",
            "Epoch: 4910NA_erro: 0.6365653341130693 \n",
            "EU_erro: 0.3560450280359549JP_erro: 0.11775357778682265loss: 0.37012129791023196\n",
            "Epoch: 4911NA_erro: 0.09244957635569018 \n",
            "EU_erro: 0.03221189559892167JP_erro: 0.04991006943606591loss: 0.05819051764732183\n",
            "Epoch: 4912NA_erro: 0.0535126573355623 \n",
            "EU_erro: 0.019858264645864796JP_erro: 0.008626335351042045loss: 0.027332421421080597\n",
            "Epoch: 4913NA_erro: 0.6639463587324749 \n",
            "EU_erro: 0.05141332167987676JP_erro: 0.012416867322699969loss: 0.24259219798006754\n",
            "Epoch: 4914NA_erro: 0.06071790706279666 \n",
            "EU_erro: 0.08336666018463844JP_erro: 0.015749149313268734loss: 0.05327790744544924\n",
            "Epoch: 4915NA_erro: 0.4971822842146999 \n",
            "EU_erro: 0.2117148665494697JP_erro: 0.04271184289178183loss: 0.250536323517792\n",
            "Epoch: 4916NA_erro: 0.08826215137806974 \n",
            "EU_erro: 0.03590838160625724JP_erro: 0.013232790222463682loss: 0.04580111004585444\n",
            "Epoch: 4917NA_erro: 0.25589870852093366 \n",
            "EU_erro: 0.04424579383790955JP_erro: 0.008612952606622563loss: 0.10291915531306303\n",
            "Epoch: 4918NA_erro: 0.5467613353285679 \n",
            "EU_erro: 0.11607812356579211JP_erro: 0.09474424428718034loss: 0.25252789489982663\n",
            "Epoch: 4919NA_erro: 0.08960250736207001 \n",
            "EU_erro: 0.06437108036159545JP_erro: 0.06395326566326526loss: 0.07264229678368384\n",
            "Epoch: 4920NA_erro: 0.1551003419151602 \n",
            "EU_erro: 0.033243896887283915JP_erro: 0.01706671345141507loss: 0.06847031356752381\n",
            "Epoch: 4921NA_erro: 0.08484484613403794 \n",
            "EU_erro: 0.024441857208577236JP_erro: 0.06967575513115225loss: 0.05965414897415989\n",
            "Epoch: 4922NA_erro: 0.10295182235481203 \n",
            "EU_erro: 0.03867782594621644JP_erro: 0.055272306582724404loss: 0.0656339826509934\n",
            "Epoch: 4923NA_erro: 0.02393902965294298 \n",
            "EU_erro: 0.05402407442876535JP_erro: 0.034803519415300944loss: 0.03758887449900309\n",
            "Epoch: 4924NA_erro: 0.07915507915408113 \n",
            "EU_erro: 0.07808902466944022JP_erro: 0.1090672866318577loss: 0.08877047272615654\n",
            "Epoch: 4925NA_erro: 1.1965110690094705 \n",
            "EU_erro: 0.3855422855347626JP_erro: 0.03454834684845089loss: 0.5388672776924547\n",
            "Epoch: 4926NA_erro: 0.6827745511550312 \n",
            "EU_erro: 0.33405215241188224JP_erro: 0.03523971221243688loss: 0.3506887790768646\n",
            "Epoch: 4927NA_erro: 0.6774200764737388 \n",
            "EU_erro: 0.35885182461997334JP_erro: 0.12397835421007733loss: 0.3867501251457274\n",
            "Epoch: 4928NA_erro: 0.46352634134218673 \n",
            "EU_erro: 0.2669191360473633JP_erro: 0.14218954152839128loss: 0.29087835504103077\n",
            "Epoch: 4929NA_erro: 0.5977720068406689 \n",
            "EU_erro: 0.2724994984708091JP_erro: 0.12752367544543836loss: 0.3325983905052954\n",
            "Epoch: 4930NA_erro: 6.445815803468689 \n",
            "EU_erro: 3.041862960933715JP_erro: 0.24349428886591loss: 3.243724320286004\n",
            "Epoch: 4931NA_erro: 0.08148540822110435 \n",
            "EU_erro: 0.03187587787938673JP_erro: 0.013727193423943925loss: 0.042362827663273775\n",
            "Epoch: 4932NA_erro: 1.098538184350775 \n",
            "EU_erro: 0.6385211796723596JP_erro: 0.07534820442051851loss: 0.6041359346966411\n",
            "Epoch: 4933NA_erro: 0.0642933679181476 \n",
            "EU_erro: 0.04866626835608667JP_erro: 0.020276841729186302loss: 0.044412160104559374\n",
            "Epoch: 4934NA_erro: 0.7337235295495321 \n",
            "EU_erro: 0.4845157889432685JP_erro: 0.06019774333451145loss: 0.4261456718740537\n",
            "Epoch: 4935NA_erro: 0.22372153378272241 \n",
            "EU_erro: 0.13178115667298781JP_erro: 0.10869859725005868loss: 0.15473377797030663\n",
            "Epoch: 4936NA_erro: 0.06426448969877968 \n",
            "EU_erro: 0.03983079463012459JP_erro: 0.007904921622239342loss: 0.03733340390892916\n",
            "Epoch: 4937NA_erro: 0.07067749204561692 \n",
            "EU_erro: 0.02475379973418953JP_erro: 0.03242000359897466loss: 0.04261709922967955\n",
            "Epoch: 4938NA_erro: 0.2062021115029505 \n",
            "EU_erro: 0.22574940393137377JP_erro: 0.05357477554055148loss: 0.16184211701385734\n",
            "Epoch: 4939NA_erro: 0.11040369669596355 \n",
            "EU_erro: 0.05984231945156127JP_erro: 0.04385048566862594loss: 0.07136550522589868\n",
            "Epoch: 4940NA_erro: 0.2599585148715234 \n",
            "EU_erro: 0.037011358627053195JP_erro: 0.027567926765412323loss: 0.10817927907603656\n",
            "Epoch: 4941NA_erro: 0.11987675067990325 \n",
            "EU_erro: 0.24362577024356338JP_erro: 0.018314295267873957loss: 0.12727227321890897\n",
            "Epoch: 4942NA_erro: 0.05804892196211704 \n",
            "EU_erro: 0.031035741170247395JP_erro: 0.009822749583296073loss: 0.03296913795692976\n",
            "Epoch: 4943NA_erro: 0.15757684559785118 \n",
            "EU_erro: 0.052258307619612344JP_erro: 0.022523599539616312loss: 0.07745292297629423\n",
            "Epoch: 4944NA_erro: 0.08598894111869872 \n",
            "EU_erro: 0.20725566287373387JP_erro: 0.05998388279316037loss: 0.11774283970973289\n",
            "Epoch: 4945NA_erro: 0.14006968616515167 \n",
            "EU_erro: 0.036494152028431266JP_erro: 0.02473187770030295loss: 0.06709857504497203\n",
            "Epoch: 4946NA_erro: 0.31872978506162186 \n",
            "EU_erro: 0.1967420504074688JP_erro: 0.16451563946036404loss: 0.22666251012521196\n",
            "Epoch: 4947NA_erro: 0.13505018958749698 \n",
            "EU_erro: 0.03370319919068684JP_erro: 0.011294309714043787loss: 0.06001590758331062\n",
            "Epoch: 4948NA_erro: 0.6977735563766124 \n",
            "EU_erro: 0.36589086517807123JP_erro: 0.05494679129400919loss: 0.3728704304658165\n",
            "Epoch: 4949NA_erro: 0.7171964275744535 \n",
            "EU_erro: 0.2778532153876253JP_erro: 0.029922418816145078loss: 0.341657372408135\n",
            "Epoch: 4950NA_erro: 0.20427820294402366 \n",
            "EU_erro: 0.036988718103068745JP_erro: 0.010486852753069974loss: 0.08391792460005412\n",
            "Epoch: 4951NA_erro: 1.1053026923837588 \n",
            "EU_erro: 0.2391775079475817JP_erro: 0.03494236127350681loss: 0.45980752900589345\n",
            "Epoch: 4952NA_erro: 0.16709207564361336 \n",
            "EU_erro: 0.04912503005922303JP_erro: 0.035658289295758386loss: 0.08395846499953159\n",
            "Epoch: 4953NA_erro: 0.11480226997257204 \n",
            "EU_erro: 0.03434390060661375JP_erro: 0.011965421050093895loss: 0.05370386349138363\n",
            "Epoch: 4954NA_erro: 0.06363927855972172 \n",
            "EU_erro: 0.03680785959081132JP_erro: 0.014183433480965074loss: 0.038210192854090254\n",
            "Epoch: 4955NA_erro: 0.030710590902224992 \n",
            "EU_erro: 0.028743769309317418JP_erro: 0.05133022633633872loss: 0.036928196286046225\n",
            "Epoch: 4956NA_erro: 0.13989802478819854 \n",
            "EU_erro: 0.016370677901792897JP_erro: 0.023863267066866853loss: 0.06004398645356644\n",
            "Epoch: 4957NA_erro: 0.30636656191921974 \n",
            "EU_erro: 0.04091036874194478JP_erro: 0.0232178388639938loss: 0.1234982549682144\n",
            "Epoch: 4958NA_erro: 0.4507801514263301 \n",
            "EU_erro: 0.36711718684943145JP_erro: 0.17344602318697197loss: 0.3304477809935577\n",
            "Epoch: 4959NA_erro: 0.12811971265216207 \n",
            "EU_erro: 0.18389407978501432JP_erro: 0.01228859258252521loss: 0.1081008042475974\n",
            "Epoch: 4960NA_erro: 0.08471133172974106 \n",
            "EU_erro: 0.08249242176381193JP_erro: 0.01406030021896658loss: 0.06042135778323624\n",
            "Epoch: 4961NA_erro: 0.27667544608892397 \n",
            "EU_erro: 0.046780335810757426JP_erro: 0.0099349345347678loss: 0.11113023573113967\n",
            "Epoch: 4962NA_erro: 0.15647178472474563 \n",
            "EU_erro: 0.07135709126790364JP_erro: 0.03792045652404312loss: 0.08858312007992766\n",
            "Epoch: 4963NA_erro: 0.07407277129417242 \n",
            "EU_erro: 0.104747123496477JP_erro: 0.10072654531907665loss: 0.09318215902461562\n",
            "Epoch: 4964NA_erro: 5.843143315278282 \n",
            "EU_erro: 2.951426247293635JP_erro: 0.04711459773455479loss: 2.9472283060236495\n",
            "Epoch: 4965NA_erro: 0.03980071045631586 \n",
            "EU_erro: 0.04848694154458453JP_erro: 0.01941415459610695loss: 0.035900600658830745\n",
            "Epoch: 4966NA_erro: 0.08409988972567772 \n",
            "EU_erro: 0.06265097810316456JP_erro: 0.09604315425074378loss: 0.08093134377353875\n",
            "Epoch: 4967NA_erro: 0.3515733674515125 \n",
            "EU_erro: 0.18490815347479295JP_erro: 0.06655249484749727loss: 0.20101133243058078\n",
            "Epoch: 4968NA_erro: 0.3882416274196418 \n",
            "EU_erro: 0.20979312039160913JP_erro: 0.14242455940838009loss: 0.24681980295698772\n",
            "Epoch: 4969NA_erro: 0.08462835651959559 \n",
            "EU_erro: 0.2322882090428079JP_erro: 0.09225726127624512loss: 0.13639126637185267\n",
            "Epoch: 4970NA_erro: 0.10868976282518963 \n",
            "EU_erro: 0.029953782872636187JP_erro: 0.008799410028051036loss: 0.04914765672166218\n",
            "Epoch: 4971NA_erro: 0.17659141111743543 \n",
            "EU_erro: 0.18224481464356415JP_erro: 0.021248573018598928loss: 0.1266949306162753\n",
            "Epoch: 4972NA_erro: 0.24784980818282726 \n",
            "EU_erro: 0.042478054068809334JP_erro: 0.09389308995978776loss: 0.12807364611662636\n",
            "Epoch: 4973NA_erro: 0.5291982399400814 \n",
            "EU_erro: 0.4191320995951808JP_erro: 0.1052305679912715loss: 0.35118694453276406\n",
            "Epoch: 4974NA_erro: 0.1199247116266295 \n",
            "EU_erro: 0.035599627236063164JP_erro: 0.023387615070786585loss: 0.059637321058169816\n",
            "Epoch: 4975NA_erro: 0.08163824561954469 \n",
            "EU_erro: 0.1226137497628382JP_erro: 0.04428034604981888loss: 0.08284411689107732\n",
            "Epoch: 4976NA_erro: 0.08462928986364557 \n",
            "EU_erro: 0.037126649719800134JP_erro: 0.03904157368711723loss: 0.05359917186027349\n",
            "Epoch: 4977NA_erro: 0.07161115491112997 \n",
            "EU_erro: 0.03166041401929633JP_erro: 0.009205328632694807loss: 0.037492301112921664\n",
            "Epoch: 4978NA_erro: 0.09244938229405603 \n",
            "EU_erro: 0.03087208945621816JP_erro: 0.013788451057995936loss: 0.045703307602756704\n",
            "Epoch: 4979NA_erro: 0.2329869787822398 \n",
            "EU_erro: 0.0251743520877158JP_erro: 0.015176168476888376loss: 0.0911125009374101\n",
            "Epoch: 4980NA_erro: 0.5263535920963731 \n",
            "EU_erro: 0.11466282282688821JP_erro: 0.0746057061261909loss: 0.23854074552077656\n",
            "Epoch: 4981NA_erro: 0.5055601652278456 \n",
            "EU_erro: 0.21680258965307428JP_erro: 0.0424504418705785loss: 0.2549377522727316\n",
            "Epoch: 4982NA_erro: 0.19169958986977273 \n",
            "EU_erro: 0.04879910354466401JP_erro: 0.0781555878099545loss: 0.10621809220129205\n",
            "Epoch: 4983NA_erro: 0.10502186856528585 \n",
            "EU_erro: 0.034228085085403086JP_erro: 0.028018942174985427loss: 0.05575630091881567\n",
            "Epoch: 4984NA_erro: 0.09775116462116093 \n",
            "EU_erro: 0.031207742155060288JP_erro: 0.019063221853832867loss: 0.04934071570403816\n",
            "Epoch: 4985NA_erro: 0.13277852258016898 \n",
            "EU_erro: 0.06565212279327157JP_erro: 0.027391656424648077loss: 0.07527410521987797\n",
            "Epoch: 4986NA_erro: 0.15161620554073837 \n",
            "EU_erro: 0.028841130493223205JP_erro: 0.013069759967715242loss: 0.06450903970141743\n",
            "Epoch: 4987NA_erro: 0.10911644891250966 \n",
            "EU_erro: 0.024141341216804446JP_erro: 0.06117887275163517loss: 0.06481222404065982\n",
            "Epoch: 4988NA_erro: 0.18129746119181314 \n",
            "EU_erro: 0.24076038552809131JP_erro: 0.08127353912176088loss: 0.16777713169423183\n",
            "Epoch: 4989NA_erro: 0.17442392748455668 \n",
            "EU_erro: 0.05386460659115813JP_erro: 0.016175998273745988loss: 0.08148818053016367\n",
            "Epoch: 4990NA_erro: 1.0280002179995988 \n",
            "EU_erro: 0.22381514541862546JP_erro: 0.06351043087567469loss: 0.43844193451164304\n",
            "Epoch: 4991NA_erro: 0.7133453576139701 \n",
            "EU_erro: 0.43968633163807JP_erro: 0.07515907287597656loss: 0.4093969330307125\n",
            "Epoch: 4992NA_erro: 0.8824831201124561 \n",
            "EU_erro: 0.25618610456008317JP_erro: 0.15032465143721233loss: 0.4296646192092304\n",
            "Epoch: 4993NA_erro: 0.19336874170820842 \n",
            "EU_erro: 0.06310276744901672JP_erro: 0.02115346202554629loss: 0.09254166322161061\n",
            "Epoch: 4994NA_erro: 5.719605083613432 \n",
            "EU_erro: 2.9978990185168364JP_erro: 0.19134626832119253loss: 2.969617030417272\n",
            "Epoch: 4995NA_erro: 0.0768381634423899 \n",
            "EU_erro: 0.045744735141133155JP_erro: 0.022162517835927563loss: 0.048248472139816874\n",
            "Epoch: 4996NA_erro: 0.0757105174914811 \n",
            "EU_erro: 0.0418588680814403JP_erro: 0.02527679352797279loss: 0.047615392263545546\n",
            "Epoch: 4997NA_erro: 0.09752329929854518 \n",
            "EU_erro: 0.04681037377941516JP_erro: 0.00777584291243738loss: 0.05070317623227141\n",
            "Epoch: 4998NA_erro: 0.20580038543819457 \n",
            "EU_erro: 0.0717493406561918JP_erro: 0.010928990998009378loss: 0.09615957274917485\n",
            "Epoch: 4999NA_erro: 0.05804953649062519 \n",
            "EU_erro: 0.023448977821557096JP_erro: 0.021117773166922635loss: 0.03420543069987334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "js-0KS7BLhUu",
        "outputId": "3241434d-3df5-4abc-f2a6-0d66c855a5e0"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "regressor_torch(\n",
              "  (dense0): Linear(in_features=61, out_features=32, bias=True)\n",
              "  (dense1): Linear(in_features=32, out_features=32, bias=True)\n",
              "  (output): Linear(in_features=32, out_features=3, bias=True)\n",
              "  (activation): Sigmoid()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsores = previsores.to(device)\n",
        "y_preds = regressor(previsores)"
      ],
      "metadata": {
        "id": "IzqnQ_vPLjnd"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = y_preds.cpu().detach().numpy()"
      ],
      "metadata": {
        "id": "D4nNBJ8rLrWE"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds.mean(axis = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGZSxk8RLxYL",
        "outputId": "d52b8bde-ae92-4b2c-e1c8-774498b62eac"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.2711465, 2.336537 , 0.5838735], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vendas.mean(axis = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9f8XlqYL2vd",
        "outputId": "75a9dd18-4b62-4a53-ff4e-ae0ab1ca74ae"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([3.2838, 2.3505, 0.5862])"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 3, figsize=(12, 6))\n",
        "\n",
        "for i in range(3):\n",
        "    ax[i].plot(vendas[:, i])\n",
        "    ax[i].plot(y_preds[:, i])\n",
        "    ax[i].legend(['Valores Reais', 'Predições'])\n",
        "    ax[i].set_title(base.columns[4 + i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "GNx-JMy-MA5M",
        "outputId": "a283a412-aaad-4475-8c7c-38077b4b4676"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}