pytorch_tensorflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMQmESt2n7vCtajmXecZQsV",
      "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_tensorflow.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": 1,
      "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": 48,
      "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": 5,
      "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": 14,
      "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": 15,
      "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": 26,
      "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": 30,
      "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": 28,
      "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": 29,
      "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": 31,
      "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": 33,
      "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": 34,
      "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": 35,
      "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": 38,
      "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": 40,
      "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": 44,
      "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": 41,
      "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": 49,
      "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": 50,
      "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": 60,
      "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": "code",
      "source": [],
      "metadata": {
        "id": "VV5oeZAKwtot"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}