A2_Q2_FashionMNISTHandwritten_Hparams_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "A2_Q2_FashionMNISTHandwritten_Hparams_final.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "uBW9LIqMtYBb"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mehrotrasan16/Keras-Deep-Learning/blob/02-Q2-HParamsFinal/A2_Q2_FashionMNISTHandwritten_Hparams_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFMPtP7dm2pd",
        "outputId": "151fd6dc-f690-491f-8dda-a5bb88c29828",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hjlzw-dmn0dv"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-j1fRFKkTsGZ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1zl2Z7dS330"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import models,layers"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8d-bgUMn3ny"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awLhr3eynTjo",
        "outputId": "33bb0fb2-82c0-4455-c7ea-c44ca3a1e863",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puk-2jSmn_87"
      },
      "source": [
        "# HandWritten Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQBg68byeGKQ"
      },
      "source": [
        "#INSERT YOUR MODEL CODE HERE\n",
        "#YOU CAN ALSO CHANGE THE CODE BELOW TO COMPILE AND RUN THE MODEL\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "model.add(layers.Conv2D(32, kernel_size=(3,3), padding='same', activation='relu', input_shape=(28,28)))\n",
        "model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))\n",
        "model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(512, activation='relu'))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Dropout(0.50))\n",
        "model.add(layers.Dense(10))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1Qi75gfehGe",
        "outputId": "c51bbd00-5f83-42a9-dc70-598dbaeb47ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        }
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(x_train, y_train, epochs=15, \n",
        "                    validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1861/1875 [============================>.] - ETA: 0s - loss: 0.4236 - accuracy: 0.8480WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4226 - accuracy: 0.8483 - val_loss: 0.3023 - val_accuracy: 0.8941\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2766 - accuracy: 0.8978 - val_loss: 0.2679 - val_accuracy: 0.9020\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2331 - accuracy: 0.9145 - val_loss: 0.2517 - val_accuracy: 0.9090\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2035 - accuracy: 0.9255 - val_loss: 0.2470 - val_accuracy: 0.9105\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1793 - accuracy: 0.9330 - val_loss: 0.2303 - val_accuracy: 0.9200\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1587 - accuracy: 0.9402 - val_loss: 0.2494 - val_accuracy: 0.9141\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1402 - accuracy: 0.9472 - val_loss: 0.2340 - val_accuracy: 0.9223\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1267 - accuracy: 0.9531 - val_loss: 0.2428 - val_accuracy: 0.9268\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1109 - accuracy: 0.9579 - val_loss: 0.2587 - val_accuracy: 0.9241\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0997 - accuracy: 0.9621 - val_loss: 0.2725 - val_accuracy: 0.9235\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0923 - accuracy: 0.9642 - val_loss: 0.2826 - val_accuracy: 0.9209\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0827 - accuracy: 0.9685 - val_loss: 0.2965 - val_accuracy: 0.9224\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0748 - accuracy: 0.9726 - val_loss: 0.3012 - val_accuracy: 0.9229\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0717 - accuracy: 0.9731 - val_loss: 0.3091 - val_accuracy: 0.9248\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0618 - accuracy: 0.9769 - val_loss: 0.3238 - val_accuracy: 0.9271\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hY4vjzIUeo5D",
        "outputId": "f66c6f00-c2ad-40c0-caef-e37ac9807cb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 1s - loss: 0.3238 - accuracy: 0.9271\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4i1JBYqkX5u",
        "outputId": "622b90e8-9eb7-4607-81fc-97416f01ca07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 953
        }
      },
      "source": [
        "keras.utils.plot_model(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgi1B6uxoFEK"
      },
      "source": [
        "# Loading AutoKeras Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYA2KnJ_fW3H",
        "outputId": "4c11b54f-257c-4639-e8f6-62468a268bbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "from keras.models import load_model\n",
        "# returns a compiled model\n",
        "# identical to the previous one\n",
        "model = load_model('/content/drive/My Drive/Colab Notebooks/AutoKerasFashionMNIST.h5')\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 28, 28, 1)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "normalization (Normalization)   (None, 28, 28, 1)    3           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "random_translation (RandomTrans (None, 28, 28, 1)    0           normalization[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "random_flip (RandomFlip)        (None, 28, 28, 1)    0           random_translation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "resizing (Resizing)             (None, 224, 224, 1)  0           random_flip[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 224, 224, 3)  0           resizing[0][0]                   \n",
            "                                                                 resizing[0][0]                   \n",
            "                                                                 resizing[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "resnet50 (Functional)           (None, None, None, 2 23587712    concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d (Globa (None, 2048)         0           resnet50[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 10)           20490       global_average_pooling2d[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "classification_head_1 (Softmax) (None, 10)           0           dense[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 23,608,205\n",
            "Trainable params: 23,555,082\n",
            "Non-trainable params: 53,123\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q884fpKJoOdY",
        "outputId": "307bcdc8-4ef5-46d2-facc-2ee54fb663ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 953
        }
      },
      "source": [
        "keras.utils.plot_model(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1owSJxzqoTr9"
      },
      "source": [
        "# HParams Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXOirNa8WY7-"
      },
      "source": [
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRV3mzFnDHwE"
      },
      "source": [
        "from tensorboard.plugins.hparams import api as hp"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUMrTNj-VAXO"
      },
      "source": [
        "# Clear any logs from previous runs\n",
        "!rm -rf ./logs/ "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NN07Woq0oWqr"
      },
      "source": [
        "# Optimizer Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Koh1cwkvkmKa"
      },
      "source": [
        "HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['rmsprop','sgd','adam', 'nadam','adadelta','adagrad','adamax','ftrl']))\n",
        "\n",
        "METRIC_ACCURACY = 'accuracy'\n",
        "\n",
        "with tf.summary.create_file_writer('logs/hparam_tuning').as_default():\n",
        "  hp.hparams_config(\n",
        "    hparams=[HP_OPTIMIZER],\n",
        "    metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],\n",
        "  )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6PD7hvSkn1l"
      },
      "source": [
        "def train_test_model(hparams):\n",
        "  #model = load_model('/content/drive/My Drive/Colab Notebooks/CS581-TestResults/AutoKerasFashionMNIST.h5')\n",
        "    model = models.Sequential()\n",
        "    model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "    model.add(layers.Conv2D(32, kernel_size=(3,3), padding='same', activation='relu', input_shape=(28,28)))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model.add(layers.Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(512, activation='relu'))\n",
        "    model.add(layers.Dropout(0.50))\n",
        "    model.add(layers.Dense(10))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=hparams[HP_OPTIMIZER],\n",
        "        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "        metrics=['accuracy'],\n",
        "    )\n",
        "    model.fit(x_train, y_train, epochs=15) # Run with 1 epoch to speed things up for demo purposes\n",
        "    _, accuracy = model.evaluate(x_test, y_test)\n",
        "    return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZrIlzrlErzB"
      },
      "source": [
        "def run(run_dir, hparams):\n",
        "  with tf.summary.create_file_writer(run_dir).as_default():\n",
        "    hp.hparams(hparams)  # record the values used in this trial\n",
        "    accuracy = train_test_model(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)\n",
        "  return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_FPVYUqFQgu",
        "outputId": "18ad7290-e205-444f-ca84-5dd385dc08b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "session_num = 0\n",
        "algodict = {}\n",
        "for optimizer in HP_OPTIMIZER.domain.values:\n",
        "    hparams = {\n",
        "        HP_OPTIMIZER: optimizer,\n",
        "    }\n",
        "    run_name = \"run-%d\" % session_num\n",
        "    print('--- Starting trial: %s' % run_name)\n",
        "    print({h.name: hparams[h] for h in hparams})\n",
        "    algodict[optimizer] = run('logs/hparam_opt_tuning/' + run_name, hparams)\n",
        "    session_num += 1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Starting trial: run-0\n",
            "{'optimizer': 'adadelta'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.1896 - accuracy: 0.2725\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.9125 - accuracy: 0.4915\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.5769 - accuracy: 0.5528\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.3030 - accuracy: 0.5834\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.1367 - accuracy: 0.6085\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0330 - accuracy: 0.6344\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9633 - accuracy: 0.6567\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9148 - accuracy: 0.6760\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.8724 - accuracy: 0.6916\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.8373 - accuracy: 0.7054\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.8085 - accuracy: 0.7167\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7836 - accuracy: 0.7237\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7590 - accuracy: 0.7340\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7426 - accuracy: 0.7397\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7239 - accuracy: 0.7458\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.6642 - accuracy: 0.7753\n",
            "--- Starting trial: run-1\n",
            "{'optimizer': 'adagrad'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_2_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_2_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.2325 - accuracy: 0.5637\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7462 - accuracy: 0.7321\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6525 - accuracy: 0.7693\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5994 - accuracy: 0.7868\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5669 - accuracy: 0.7976\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5419 - accuracy: 0.8066\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5199 - accuracy: 0.8135\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5025 - accuracy: 0.8206\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4893 - accuracy: 0.8265\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4760 - accuracy: 0.8313\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4665 - accuracy: 0.8325\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4571 - accuracy: 0.8379\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4477 - accuracy: 0.8400\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4420 - accuracy: 0.8436\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4341 - accuracy: 0.8462\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_2_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4264 - accuracy: 0.8469\n",
            "--- Starting trial: run-2\n",
            "{'optimizer': 'adam'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_3_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_3_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4266 - accuracy: 0.8454\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2805 - accuracy: 0.8975\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2364 - accuracy: 0.9126\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2040 - accuracy: 0.9251\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1808 - accuracy: 0.9333\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1623 - accuracy: 0.9390\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1452 - accuracy: 0.9449\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1288 - accuracy: 0.9520\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1133 - accuracy: 0.9575\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1022 - accuracy: 0.9619\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0937 - accuracy: 0.9638\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0826 - accuracy: 0.9683\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0792 - accuracy: 0.9703\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0710 - accuracy: 0.9740\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0643 - accuracy: 0.9761\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_3_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2995 - accuracy: 0.9247\n",
            "--- Starting trial: run-3\n",
            "{'optimizer': 'adamax'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_4_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_4_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4967 - accuracy: 0.8217\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3388 - accuracy: 0.8789\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2940 - accuracy: 0.8928\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2656 - accuracy: 0.9040\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2442 - accuracy: 0.9119\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2260 - accuracy: 0.9174\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2135 - accuracy: 0.9233\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2004 - accuracy: 0.9271\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1885 - accuracy: 0.9317\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1782 - accuracy: 0.9355\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1684 - accuracy: 0.9392\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1594 - accuracy: 0.9421\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1500 - accuracy: 0.9450\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1434 - accuracy: 0.9485\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1355 - accuracy: 0.9496\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_4_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2340 - accuracy: 0.9221\n",
            "--- Starting trial: run-4\n",
            "{'optimizer': 'ftrl'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_5_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_5_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_5_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-5\n",
            "{'optimizer': 'nadam'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4198 - accuracy: 0.8487\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2739 - accuracy: 0.9004\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2306 - accuracy: 0.9150\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1991 - accuracy: 0.9258\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1801 - accuracy: 0.9337\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1587 - accuracy: 0.9414\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1416 - accuracy: 0.9475\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1270 - accuracy: 0.9531\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1135 - accuracy: 0.9572\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1042 - accuracy: 0.9612\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0919 - accuracy: 0.9647\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0827 - accuracy: 0.9690\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0775 - accuracy: 0.9706\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0690 - accuracy: 0.9742\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0665 - accuracy: 0.9748\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3251 - accuracy: 0.9201\n",
            "--- Starting trial: run-6\n",
            "{'optimizer': 'rmsprop'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4128 - accuracy: 0.8522\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2925 - accuracy: 0.8966\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2748 - accuracy: 0.9033\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2668 - accuracy: 0.9073\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2696 - accuracy: 0.9069\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2698 - accuracy: 0.9074\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2777 - accuracy: 0.9063\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2812 - accuracy: 0.9059\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2848 - accuracy: 0.9050\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2935 - accuracy: 0.9022\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3028 - accuracy: 0.9010\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3082 - accuracy: 0.8995\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3161 - accuracy: 0.8966\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3208 - accuracy: 0.8945\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3262 - accuracy: 0.8917\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3402 - accuracy: 0.8896\n",
            "--- Starting trial: run-7\n",
            "{'optimizer': 'sgd'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.8465 - accuracy: 0.6899\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5401 - accuracy: 0.7999\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4660 - accuracy: 0.8301\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4226 - accuracy: 0.8473\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3971 - accuracy: 0.8558\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3730 - accuracy: 0.8643\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3570 - accuracy: 0.8712\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3432 - accuracy: 0.8760\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3285 - accuracy: 0.8810\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3184 - accuracy: 0.8832\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3093 - accuracy: 0.8882\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3010 - accuracy: 0.8912\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2924 - accuracy: 0.8927\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2852 - accuracy: 0.8963\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2795 - accuracy: 0.8970\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2811 - accuracy: 0.8974\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03IcbG5qSv34",
        "outputId": "b0b9b671-68fc-4897-8219-98ada4a4c02e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "algodict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'adadelta': 0.7753000259399414,\n",
              " 'adagrad': 0.8468999862670898,\n",
              " 'adam': 0.9247000217437744,\n",
              " 'adamax': 0.9221000075340271,\n",
              " 'ftrl': 0.10000000149011612,\n",
              " 'nadam': 0.9200999736785889,\n",
              " 'rmsprop': 0.8895999789237976,\n",
              " 'sgd': 0.8974000215530396}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8E7eFt0kJAo",
        "outputId": "3d85a53a-3505-4dcb-f33e-471b5164e84f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(sorted(algodict.items(),key=lambda x: (x[1],x[0])))\n",
        "plotvar = sorted(algodict.items(),key=lambda x: (x[1],x[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('ftrl', 0.10000000149011612), ('adadelta', 0.7753000259399414), ('adagrad', 0.8468999862670898), ('rmsprop', 0.8895999789237976), ('sgd', 0.8974000215530396), ('nadam', 0.9200999736785889), ('adamax', 0.9221000075340271), ('adam', 0.9247000217437744)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYRhRWE_k1WH",
        "outputId": "8bd7fab1-4f1f-4444-9534-fd13a0e078be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        }
      },
      "source": [
        "plt.bar([x[0] for x in plotvar],[x[1] for x in plotvar])\n",
        "f = plt.gcf()\n",
        "f.set_size_inches(9,9)\n",
        "f.autofmt_xdate()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-Pm5dKUKaFb",
        "outputId": "eae26d14-a10b-4757-9c4f-61bc079d1f2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "plt.savefig('/content/optimizer-clean-compare.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJqyVJW9nyJL"
      },
      "source": [
        "# Activation function Tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqh-samOn0bb"
      },
      "source": [
        "### 1 - layer Minimal Network - 3 Combinations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYoIOTfWlTMI"
      },
      "source": [
        "HP_ACTIVATION = hp.HParam('activation1', hp.Discrete(['relu','selu','elu']))\n",
        "\n",
        "METRIC_ACCURACY = 'accuracy'\n",
        "\n",
        "with tf.summary.create_file_writer('logs/hparam_tuning').as_default():\n",
        "  hp.hparams_config(\n",
        "    hparams=[HP_ACTIVATION],\n",
        "    metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],\n",
        "  )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGeQQ6CllTMU"
      },
      "source": [
        "def train_test_model(hparams):\n",
        "  #model = load_model('/content/drive/My Drive/Colab Notebooks/CS581-TestResults/AutoKerasFashionMNIST.h5')\n",
        "    model = models.Sequential()\n",
        "    model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "    model.add(layers.Conv2D(32, kernel_size=(3,3), padding='same', activation='relu', input_shape=(28,28)))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    # model.add(layers.Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))\n",
        "    # model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(512, activation=hparams[HP_ACTIVATION]))\n",
        "    model.add(layers.Dropout(0.50))\n",
        "    model.add(layers.Dense(10))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "        metrics=['accuracy'],\n",
        "    )\n",
        "    model.fit(x_train, y_train, epochs=15) # Run with 1 epoch to speed things up for demo purposes\n",
        "    _, accuracy = model.evaluate(x_test, y_test)\n",
        "    return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sf9ORg5TlTMc"
      },
      "source": [
        "def run(run_dir, hparams):\n",
        "  with tf.summary.create_file_writer(run_dir).as_default():\n",
        "    hp.hparams(hparams)  # record the values used in this trial\n",
        "    accuracy = train_test_model(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)\n",
        "  return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQaUZQP8lTMj",
        "outputId": "c0dd7165-4821-467d-9b04-15fc04198388",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "session_num = 0\n",
        "acttemp = {}\n",
        "for act in HP_ACTIVATION.domain.values:\n",
        "    hparams = {\n",
        "        HP_ACTIVATION: act,\n",
        "    }\n",
        "    run_name = \"run-%d\" % session_num\n",
        "    print('--- Starting trial: %s' % run_name)\n",
        "    print({h.name: hparams[h] for h in hparams})\n",
        "    acttemp[act] = run('logs/hparam_opt_tuning/' + run_name, hparams)\n",
        "    session_num += 1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Starting trial: run-0\n",
            "{'activation1': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3861 - accuracy: 0.8631\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2892 - accuracy: 0.8962\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2561 - accuracy: 0.9077\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2359 - accuracy: 0.9134\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2154 - accuracy: 0.9210\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2013 - accuracy: 0.9263\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1896 - accuracy: 0.9302\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1811 - accuracy: 0.9330\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1711 - accuracy: 0.9366\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1653 - accuracy: 0.9380\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1548 - accuracy: 0.9425\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1508 - accuracy: 0.9435\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1451 - accuracy: 0.9474\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1380 - accuracy: 0.9495\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1346 - accuracy: 0.9503\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3663 - accuracy: 0.9030\n",
            "--- Starting trial: run-1\n",
            "{'activation1': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4231 - accuracy: 0.8491\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2926 - accuracy: 0.8932\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2538 - accuracy: 0.9072\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2231 - accuracy: 0.9171\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1998 - accuracy: 0.9258\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1802 - accuracy: 0.9335\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1639 - accuracy: 0.9392\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1499 - accuracy: 0.9447\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1373 - accuracy: 0.9486\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1241 - accuracy: 0.9528\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1128 - accuracy: 0.9576\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1056 - accuracy: 0.9597\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0952 - accuracy: 0.9649\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0880 - accuracy: 0.9668\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0837 - accuracy: 0.9698\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3199 - accuracy: 0.9212\n",
            "--- Starting trial: run-2\n",
            "{'activation1': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3913 - accuracy: 0.8632\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2905 - accuracy: 0.8957\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2581 - accuracy: 0.9067\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2357 - accuracy: 0.9135\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2229 - accuracy: 0.9198\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2081 - accuracy: 0.9239\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1974 - accuracy: 0.9281\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1879 - accuracy: 0.9310\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1819 - accuracy: 0.9329\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1737 - accuracy: 0.9364\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1675 - accuracy: 0.9376\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1642 - accuracy: 0.9395\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1589 - accuracy: 0.9405\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1544 - accuracy: 0.9448\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1507 - accuracy: 0.9452\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3643 - accuracy: 0.8989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEpaCPE_n-OL",
        "outputId": "dfb7f20d-b791-4678-8b62-0017d281379a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "acttemp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'elu': 0.902999997138977,\n",
              " 'relu': 0.9211999773979187,\n",
              " 'selu': 0.8988999724388123}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyhmsmzToEUM",
        "outputId": "8e4cc47c-5f7d-4f41-9e77-c434879bef05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "source": [
        "plotvartemp = sorted(acttemp.items(),key=lambda x: (x[1],x[0]))\n",
        "plt.bar([str(x[0]) for x in plotvartemp],[x[1] for x in plotvartemp])\n",
        "f = plt.gcf()\n",
        "f.set_size_inches(10,10)\n",
        "f.autofmt_xdate()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OXlGVVPkoVdN"
      },
      "source": [
        "## 2 - layer Handwritten Network - 9 Combos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URZ5a2rUobcN"
      },
      "source": [
        "HP_ACTIVATIONA = hp.HParam('activation1', hp.Discrete(['relu','selu','elu']))\n",
        "HP_ACTIVATIONB = hp.HParam('activation2', hp.Discrete(['relu','selu','elu']))\n",
        "\n",
        "METRIC_ACCURACY = 'accuracy'\n",
        "\n",
        "with tf.summary.create_file_writer('logs/hparam_tuning').as_default():\n",
        "  hp.hparams_config(\n",
        "    hparams=[HP_ACTIVATIONA,HP_ACTIVATIONB],\n",
        "    metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],\n",
        "  )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAw705O6ofou"
      },
      "source": [
        "def train_test_model(hparams):\n",
        "  #model = load_model('/content/drive/My Drive/Colab Notebooks/CS581-TestResults/AutoKerasFashionMNIST.h5')\n",
        "    model = models.Sequential()\n",
        "    model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "    model.add(layers.Conv2D(32, kernel_size=(3,3), padding='same', activation=hparams[HP_ACTIVATIONA], input_shape=(28,28)))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    # model.add(layers.Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))\n",
        "    # model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(512, activation=hparams[HP_ACTIVATIONB]))\n",
        "    model.add(layers.Dropout(0.50))\n",
        "    model.add(layers.Dense(10))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "        metrics=['accuracy'],\n",
        "    )\n",
        "    model.fit(x_train, y_train, epochs=15) # Run with 1 epoch to speed things up for demo purposes\n",
        "    _, accuracy = model.evaluate(x_test, y_test)\n",
        "    return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AiVXSfBp-qr"
      },
      "source": [
        "def run(run_dir, hparams):\n",
        "  with tf.summary.create_file_writer(run_dir).as_default():\n",
        "    hp.hparams(hparams)  # record the values used in this trial\n",
        "    accuracy = train_test_model(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)\n",
        "  return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBpfvoSssOVC",
        "outputId": "8a872ee9-f321-4c1a-dc80-1bb0259e91f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "session_num = 0\n",
        "act2 = {}\n",
        "for actA in HP_ACTIVATIONA.domain.values:\n",
        "    for actB in HP_ACTIVATIONB.domain.values:\n",
        "        hparams = {\n",
        "            HP_ACTIVATIONA: actA,\n",
        "            HP_ACTIVATIONB: actB\n",
        "        }\n",
        "        run_name = \"run-%d\" % session_num\n",
        "        print('--- Starting trial: %s' % run_name)\n",
        "        print({h.name: hparams[h] for h in hparams})\n",
        "        act2[(actA,actB)] = run('logs/hparam_opt_tuning/' + run_name, hparams)\n",
        "        session_num += 1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Starting trial: run-0\n",
            "{'activation1': 'elu', 'activation2': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_10_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_10_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4190 - accuracy: 0.8525\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3245 - accuracy: 0.8844\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2961 - accuracy: 0.8927\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2723 - accuracy: 0.9022\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2580 - accuracy: 0.9064\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.2454 - accuracy: 0.9114\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2278 - accuracy: 0.9171\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2166 - accuracy: 0.9214\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2064 - accuracy: 0.9253\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1949 - accuracy: 0.9283\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1907 - accuracy: 0.9315\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1794 - accuracy: 0.9348\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1693 - accuracy: 0.9388\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1628 - accuracy: 0.9413\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1575 - accuracy: 0.9432\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_10_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3169 - accuracy: 0.9110\n",
            "--- Starting trial: run-1\n",
            "{'activation1': 'elu', 'activation2': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_11_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_11_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4065 - accuracy: 0.8543\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2943 - accuracy: 0.8931\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2550 - accuracy: 0.9069\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2316 - accuracy: 0.9161\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2104 - accuracy: 0.9230\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1944 - accuracy: 0.9287\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1782 - accuracy: 0.9337\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1665 - accuracy: 0.9382\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1569 - accuracy: 0.9419\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1481 - accuracy: 0.9453\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1387 - accuracy: 0.9493\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1330 - accuracy: 0.9505\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1275 - accuracy: 0.9542\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1192 - accuracy: 0.9554\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1181 - accuracy: 0.9563\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_11_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3143 - accuracy: 0.9177\n",
            "--- Starting trial: run-2\n",
            "{'activation1': 'elu', 'activation2': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_12_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_12_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4303 - accuracy: 0.8507\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3380 - accuracy: 0.8810\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3115 - accuracy: 0.8895\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2960 - accuracy: 0.8952\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2819 - accuracy: 0.8997\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2720 - accuracy: 0.9023\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2657 - accuracy: 0.9052\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2596 - accuracy: 0.9071\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2520 - accuracy: 0.9096\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2432 - accuracy: 0.9124\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2402 - accuracy: 0.9137\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2338 - accuracy: 0.9162\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2306 - accuracy: 0.9176\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2224 - accuracy: 0.9211\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2213 - accuracy: 0.9223\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_12_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3659 - accuracy: 0.8867\n",
            "--- Starting trial: run-3\n",
            "{'activation1': 'relu', 'activation2': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_13_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_13_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3957 - accuracy: 0.8610\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2916 - accuracy: 0.8955\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2599 - accuracy: 0.9056\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2403 - accuracy: 0.9122\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2217 - accuracy: 0.9186\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2085 - accuracy: 0.9228\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1948 - accuracy: 0.9282\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1859 - accuracy: 0.9324\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1783 - accuracy: 0.9335\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1680 - accuracy: 0.9378\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1609 - accuracy: 0.9405\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1558 - accuracy: 0.9419\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1505 - accuracy: 0.9434\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1447 - accuracy: 0.9476\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1398 - accuracy: 0.9482\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_13_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3622 - accuracy: 0.8993\n",
            "--- Starting trial: run-4\n",
            "{'activation1': 'relu', 'activation2': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_14_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_14_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4151 - accuracy: 0.8518\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2934 - accuracy: 0.8932\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2505 - accuracy: 0.9074\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2241 - accuracy: 0.9166\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2000 - accuracy: 0.9258\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1781 - accuracy: 0.9327\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1604 - accuracy: 0.9399\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1434 - accuracy: 0.9456\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1316 - accuracy: 0.9511\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1191 - accuracy: 0.9549\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1101 - accuracy: 0.9589\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0970 - accuracy: 0.9639\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0940 - accuracy: 0.9639\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0856 - accuracy: 0.9688\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0792 - accuracy: 0.9707\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_14_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3013 - accuracy: 0.9204\n",
            "--- Starting trial: run-5\n",
            "{'activation1': 'relu', 'activation2': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_15_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_15_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3896 - accuracy: 0.8640\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2881 - accuracy: 0.8970\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2579 - accuracy: 0.9078\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2366 - accuracy: 0.9138\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2224 - accuracy: 0.9192\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2093 - accuracy: 0.9240\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1979 - accuracy: 0.9267\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1913 - accuracy: 0.9302\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1816 - accuracy: 0.9332\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1761 - accuracy: 0.9353\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1712 - accuracy: 0.9373\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1674 - accuracy: 0.9386\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1615 - accuracy: 0.9407\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1583 - accuracy: 0.9415\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1522 - accuracy: 0.9444\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_15_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3891 - accuracy: 0.8913\n",
            "--- Starting trial: run-6\n",
            "{'activation1': 'selu', 'activation2': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_16_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_16_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4308 - accuracy: 0.8490\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3316 - accuracy: 0.8820\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2964 - accuracy: 0.8952\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2800 - accuracy: 0.8988\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2575 - accuracy: 0.9074\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2435 - accuracy: 0.9121\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2295 - accuracy: 0.9172\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2171 - accuracy: 0.9222\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2079 - accuracy: 0.9264\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1968 - accuracy: 0.9294\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1882 - accuracy: 0.9326\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1770 - accuracy: 0.9366\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1707 - accuracy: 0.9399\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1637 - accuracy: 0.9430\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1558 - accuracy: 0.9441\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_16_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "  1/313 [..............................] - ETA: 0s - loss: 0.1533 - accuracy: 0.9375WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_test_batch_end` time: 0.0044s). Check your callbacks.\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3172 - accuracy: 0.9121\n",
            "--- Starting trial: run-7\n",
            "{'activation1': 'selu', 'activation2': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_17_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_17_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "   1/1875 [..............................] - ETA: 2s - loss: 2.2684 - accuracy: 0.0938WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0034s vs `on_train_batch_end` time: 0.0057s). Check your callbacks.\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4212 - accuracy: 0.8487\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2981 - accuracy: 0.8913\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2608 - accuracy: 0.9049\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2353 - accuracy: 0.9141\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2150 - accuracy: 0.9209\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2006 - accuracy: 0.9261\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1870 - accuracy: 0.9307\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1762 - accuracy: 0.9343\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1661 - accuracy: 0.9386\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1557 - accuracy: 0.9420\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1476 - accuracy: 0.9459\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1426 - accuracy: 0.9473\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1347 - accuracy: 0.9506\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1317 - accuracy: 0.9513\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1251 - accuracy: 0.9542\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_17_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3052 - accuracy: 0.9167\n",
            "--- Starting trial: run-8\n",
            "{'activation1': 'selu', 'activation2': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_18_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_18_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4411 - accuracy: 0.8482\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3430 - accuracy: 0.8798\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3211 - accuracy: 0.8870\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3016 - accuracy: 0.8928\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2875 - accuracy: 0.8979\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2778 - accuracy: 0.9020\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2690 - accuracy: 0.9043\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2599 - accuracy: 0.9069\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2537 - accuracy: 0.9100\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2479 - accuracy: 0.9128\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2398 - accuracy: 0.9149\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2339 - accuracy: 0.9171\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2293 - accuracy: 0.9190\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2228 - accuracy: 0.9219\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2173 - accuracy: 0.9244\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_18_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3410 - accuracy: 0.8997\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnT-_b_azWZ2",
        "outputId": "2401dcf2-436c-42ef-ba85-da8d307c6fdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "act2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('elu', 'elu'): 0.9110000133514404,\n",
              " ('elu', 'relu'): 0.9176999926567078,\n",
              " ('elu', 'selu'): 0.8866999745368958,\n",
              " ('relu', 'elu'): 0.8992999792098999,\n",
              " ('relu', 'relu'): 0.9204000234603882,\n",
              " ('relu', 'selu'): 0.8913000226020813,\n",
              " ('selu', 'elu'): 0.9121000170707703,\n",
              " ('selu', 'relu'): 0.916700005531311,\n",
              " ('selu', 'selu'): 0.8996999859809875}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_1gcTcszdt9",
        "outputId": "49d8a76a-e2fd-418b-ed2c-a4b336abcc6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        }
      },
      "source": [
        "plotvar2 = sorted(act2.items(),key=lambda x: (x[1],x[0]))\n",
        "plt.bar([str(x[0]) for x in plotvar2],[x[1] for x in plotvar2])\n",
        "f = plt.gcf()\n",
        "f.set_size_inches(10,10)\n",
        "f.autofmt_xdate()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OanuAAlbUZ0I"
      },
      "source": [
        "## 3 - layer Handwritten Network - 27 Combinations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zf-hvJwEzxKQ"
      },
      "source": [
        "HP_ACTIVATIONC = hp.HParam('activationC', hp.Discrete(['relu','selu','elu']))\n",
        "HP_ACTIVATIOND = hp.HParam('activationD', hp.Discrete(['relu','selu','elu']))\n",
        "HP_ACTIVATIONE = hp.HParam('activationE', hp.Discrete(['relu','selu','elu']))\n",
        "\n",
        "METRIC_ACCURACY = 'accuracy'\n",
        "\n",
        "with tf.summary.create_file_writer('logs/hparam_tuning').as_default():\n",
        "  hp.hparams_config(\n",
        "    hparams=[HP_ACTIVATIONC,HP_ACTIVATIOND,HP_ACTIVATIONE],\n",
        "    metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],\n",
        "  )"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIfRDFVlz2u1"
      },
      "source": [
        "def train_test_model(hparams):\n",
        "  #model = load_model('/content/drive/My Drive/Colab Notebooks/CS581-TestResults/AutoKerasFashionMNIST.h5')\n",
        "    model = models.Sequential()\n",
        "    model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "    model.add(layers.Conv2D(32, kernel_size=(3,3), padding='same', activation=hparams[HP_ACTIVATIONC], input_shape=(28,28)))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model.add(layers.Conv2D(64, kernel_size=(3,3), padding='same', activation=hparams[HP_ACTIVATIOND]))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(512, activation=hparams[HP_ACTIVATIONE]))\n",
        "    model.add(layers.Dropout(0.50))\n",
        "    model.add(layers.Dense(10))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "        metrics=['accuracy'],\n",
        "    )\n",
        "    model.fit(x_train, y_train, epochs=15) # Run with 1 epoch to speed things up for demo purposes\n",
        "    _, accuracy = model.evaluate(x_test, y_test)\n",
        "    return accuracy"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyPmNFlZ0IAF"
      },
      "source": [
        "def run(run_dir, hparams):\n",
        "  with tf.summary.create_file_writer(run_dir).as_default():\n",
        "    hp.hparams(hparams)  # record the values used in this trial\n",
        "    accuracy = train_test_model(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)\n",
        "  return accuracy"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AEARlN30Kzj",
        "outputId": "20bae8c7-5437-40c2-eb51-a2dfb9892aee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "session_num = 0\n",
        "act2p = {}\n",
        "for actC in HP_ACTIVATIONC.domain.values:\n",
        "    for actD in HP_ACTIVATIOND.domain.values:\n",
        "        for actE in HP_ACTIVATIONE.domain.values:\n",
        "            hparams = {\n",
        "                HP_ACTIVATIONC: actC,\n",
        "                HP_ACTIVATIOND: actD,\n",
        "                HP_ACTIVATIONE: actE\n",
        "            }\n",
        "            run_name = \"run-%d\" % session_num\n",
        "            print('--- Starting trial: %s' % run_name)\n",
        "            print({h.name: hparams[h] for h in hparams})\n",
        "            act2p[(actC,actD,actE)] = run('logs/hparam_opt_tuning/' + run_name, hparams)\n",
        "            session_num += 1\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Starting trial: run-0\n",
            "{'activationC': 'elu', 'activationD': 'elu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4028 - accuracy: 0.8566\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2936 - accuracy: 0.8957\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2593 - accuracy: 0.9067\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2365 - accuracy: 0.9140\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2158 - accuracy: 0.9220\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2005 - accuracy: 0.9277\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1856 - accuracy: 0.9321\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1751 - accuracy: 0.9355\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1614 - accuracy: 0.9412\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1510 - accuracy: 0.9459\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1388 - accuracy: 0.9500\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1336 - accuracy: 0.9517\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1241 - accuracy: 0.9552\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1153 - accuracy: 0.9594\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1091 - accuracy: 0.9617\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3793 - accuracy: 0.9091\n",
            "--- Starting trial: run-1\n",
            "{'activationC': 'elu', 'activationD': 'elu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4102 - accuracy: 0.8510\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2758 - accuracy: 0.8984\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2337 - accuracy: 0.9147\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2030 - accuracy: 0.9252\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1772 - accuracy: 0.9345\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1572 - accuracy: 0.9419\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1400 - accuracy: 0.9473\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1233 - accuracy: 0.9538\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1167 - accuracy: 0.9571\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1060 - accuracy: 0.9599\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0950 - accuracy: 0.9647\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0915 - accuracy: 0.9667\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0857 - accuracy: 0.9692\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0790 - accuracy: 0.9712\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0763 - accuracy: 0.9722\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_1_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3915 - accuracy: 0.9180\n",
            "--- Starting trial: run-2\n",
            "{'activationC': 'elu', 'activationD': 'elu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_2_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_2_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4015 - accuracy: 0.8582\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2978 - accuracy: 0.8943\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2661 - accuracy: 0.9042\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2435 - accuracy: 0.9132\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2259 - accuracy: 0.9197\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2146 - accuracy: 0.9227\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2008 - accuracy: 0.9280\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1889 - accuracy: 0.9327\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1817 - accuracy: 0.9355\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1730 - accuracy: 0.9383\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1668 - accuracy: 0.9407\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1597 - accuracy: 0.9432\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1553 - accuracy: 0.9453\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1472 - accuracy: 0.9476\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1477 - accuracy: 0.9486\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_2_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4236 - accuracy: 0.8964\n",
            "--- Starting trial: run-3\n",
            "{'activationC': 'elu', 'activationD': 'relu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_3_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_3_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4047 - accuracy: 0.8530\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2952 - accuracy: 0.8933\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2627 - accuracy: 0.9057\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2399 - accuracy: 0.9135\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2227 - accuracy: 0.9193\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2075 - accuracy: 0.9251\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1975 - accuracy: 0.9285\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1849 - accuracy: 0.9336\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1762 - accuracy: 0.9363\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1688 - accuracy: 0.9391\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1578 - accuracy: 0.9431\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1510 - accuracy: 0.9455\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1428 - accuracy: 0.9476\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1368 - accuracy: 0.9508\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1313 - accuracy: 0.9530\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_3_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3450 - accuracy: 0.9076\n",
            "--- Starting trial: run-4\n",
            "{'activationC': 'elu', 'activationD': 'relu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_4_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_4_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4232 - accuracy: 0.8468\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2927 - accuracy: 0.8948\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2524 - accuracy: 0.9068\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2222 - accuracy: 0.9172\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2006 - accuracy: 0.9259\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1815 - accuracy: 0.9322\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1622 - accuracy: 0.9393\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1486 - accuracy: 0.9452\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1364 - accuracy: 0.9492\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1227 - accuracy: 0.9543\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1148 - accuracy: 0.9563\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1049 - accuracy: 0.9607\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0951 - accuracy: 0.9646\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0893 - accuracy: 0.9674\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0869 - accuracy: 0.9683\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_4_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3386 - accuracy: 0.9201\n",
            "--- Starting trial: run-5\n",
            "{'activationC': 'elu', 'activationD': 'relu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_5_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_5_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4028 - accuracy: 0.8557\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2999 - accuracy: 0.8928\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2694 - accuracy: 0.9033\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2470 - accuracy: 0.9114\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2289 - accuracy: 0.9178\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2154 - accuracy: 0.9222\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2054 - accuracy: 0.9258\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1954 - accuracy: 0.9300\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1860 - accuracy: 0.9324\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1780 - accuracy: 0.9359\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1731 - accuracy: 0.9376\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1626 - accuracy: 0.9412\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1627 - accuracy: 0.9426\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1534 - accuracy: 0.9451\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1476 - accuracy: 0.9471\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_5_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3672 - accuracy: 0.8961\n",
            "--- Starting trial: run-6\n",
            "{'activationC': 'elu', 'activationD': 'selu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4022 - accuracy: 0.8573\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2947 - accuracy: 0.8952\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2611 - accuracy: 0.9056\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2376 - accuracy: 0.9140\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2189 - accuracy: 0.9203\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2008 - accuracy: 0.9264\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1860 - accuracy: 0.9324\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1733 - accuracy: 0.9375\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1626 - accuracy: 0.9409\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1545 - accuracy: 0.9451\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1434 - accuracy: 0.9483\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1296 - accuracy: 0.9542\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1263 - accuracy: 0.9555\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1169 - accuracy: 0.9596\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1102 - accuracy: 0.9619\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_6_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4275 - accuracy: 0.9078\n",
            "--- Starting trial: run-7\n",
            "{'activationC': 'elu', 'activationD': 'selu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4094 - accuracy: 0.8523\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2830 - accuracy: 0.8959\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2364 - accuracy: 0.9140\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2086 - accuracy: 0.9239\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1822 - accuracy: 0.9326\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1641 - accuracy: 0.9381\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1474 - accuracy: 0.9456\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1339 - accuracy: 0.9497\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1230 - accuracy: 0.9545\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1150 - accuracy: 0.9579\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1071 - accuracy: 0.9607\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1034 - accuracy: 0.9624\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0935 - accuracy: 0.9658\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0890 - accuracy: 0.9679\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0885 - accuracy: 0.9681\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_7_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3634 - accuracy: 0.9192\n",
            "--- Starting trial: run-8\n",
            "{'activationC': 'elu', 'activationD': 'selu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4097 - accuracy: 0.8566\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3003 - accuracy: 0.8931\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2721 - accuracy: 0.9031\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2459 - accuracy: 0.9123\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2259 - accuracy: 0.9187\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2160 - accuracy: 0.9230\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2051 - accuracy: 0.9266\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1948 - accuracy: 0.9299\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1879 - accuracy: 0.9322\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1796 - accuracy: 0.9355\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1734 - accuracy: 0.9388\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1639 - accuracy: 0.9413\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1616 - accuracy: 0.9434\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1562 - accuracy: 0.9453\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1551 - accuracy: 0.9463\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_8_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4135 - accuracy: 0.8975\n",
            "--- Starting trial: run-9\n",
            "{'activationC': 'relu', 'activationD': 'elu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_9_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_9_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3854 - accuracy: 0.8629\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2741 - accuracy: 0.9018\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2403 - accuracy: 0.9131\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2182 - accuracy: 0.9214\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2001 - accuracy: 0.9274\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1858 - accuracy: 0.9326\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1726 - accuracy: 0.9369\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1593 - accuracy: 0.9422\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1481 - accuracy: 0.9460\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1389 - accuracy: 0.9485\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1317 - accuracy: 0.9523\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1207 - accuracy: 0.9557\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1160 - accuracy: 0.9582\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1070 - accuracy: 0.9612\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1021 - accuracy: 0.9631\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_9_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3936 - accuracy: 0.9113\n",
            "--- Starting trial: run-10\n",
            "{'activationC': 'relu', 'activationD': 'elu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_10_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_10_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4016 - accuracy: 0.8540\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2672 - accuracy: 0.9015\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2297 - accuracy: 0.9146\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2011 - accuracy: 0.9254\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1776 - accuracy: 0.9338\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1565 - accuracy: 0.9425\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1395 - accuracy: 0.9475\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1254 - accuracy: 0.9540\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1111 - accuracy: 0.9578\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1025 - accuracy: 0.9618\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0892 - accuracy: 0.9668\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0857 - accuracy: 0.9681\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0763 - accuracy: 0.9715\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0703 - accuracy: 0.9741\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0667 - accuracy: 0.9753\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_10_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3381 - accuracy: 0.9242\n",
            "--- Starting trial: run-11\n",
            "{'activationC': 'relu', 'activationD': 'elu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_11_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_11_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3795 - accuracy: 0.8653\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2725 - accuracy: 0.9016\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2413 - accuracy: 0.9126\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2178 - accuracy: 0.9212\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1987 - accuracy: 0.9277\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1854 - accuracy: 0.9331\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1722 - accuracy: 0.9377\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1632 - accuracy: 0.9409\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1548 - accuracy: 0.9429\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1449 - accuracy: 0.9476\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1400 - accuracy: 0.9503\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1295 - accuracy: 0.9533\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1274 - accuracy: 0.9535\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1169 - accuracy: 0.9583\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1146 - accuracy: 0.9584\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_11_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4016 - accuracy: 0.9047\n",
            "--- Starting trial: run-12\n",
            "{'activationC': 'relu', 'activationD': 'relu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_12_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_12_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3919 - accuracy: 0.8597\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2726 - accuracy: 0.9006\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2358 - accuracy: 0.9140\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2163 - accuracy: 0.9217\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1988 - accuracy: 0.9274\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1871 - accuracy: 0.9314\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1744 - accuracy: 0.9362\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1651 - accuracy: 0.9390\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1537 - accuracy: 0.9436\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1452 - accuracy: 0.9464\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1387 - accuracy: 0.9493\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1316 - accuracy: 0.9517\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1247 - accuracy: 0.9542\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1173 - accuracy: 0.9570\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1130 - accuracy: 0.9594\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_12_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3175 - accuracy: 0.9092\n",
            "--- Starting trial: run-13\n",
            "{'activationC': 'relu', 'activationD': 'relu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_13_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_13_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4151 - accuracy: 0.8502\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2689 - accuracy: 0.9030\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2295 - accuracy: 0.9156\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1991 - accuracy: 0.9261\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1743 - accuracy: 0.9351\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1565 - accuracy: 0.9412\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1375 - accuracy: 0.9484\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1241 - accuracy: 0.9545\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1085 - accuracy: 0.9591\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0974 - accuracy: 0.9635\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0888 - accuracy: 0.9669\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0782 - accuracy: 0.9704\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0713 - accuracy: 0.9737\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0683 - accuracy: 0.9751\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0587 - accuracy: 0.9785\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_13_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3375 - accuracy: 0.9186\n",
            "--- Starting trial: run-14\n",
            "{'activationC': 'relu', 'activationD': 'relu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_14_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_14_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3881 - accuracy: 0.8619\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2735 - accuracy: 0.9013\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2394 - accuracy: 0.9143\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2192 - accuracy: 0.9205\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2017 - accuracy: 0.9261\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1887 - accuracy: 0.9304\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1770 - accuracy: 0.9356\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1670 - accuracy: 0.9394\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1585 - accuracy: 0.9425\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1497 - accuracy: 0.9449\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1426 - accuracy: 0.9482\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1352 - accuracy: 0.9499\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1290 - accuracy: 0.9526\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1239 - accuracy: 0.9540\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1206 - accuracy: 0.9554\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_14_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3376 - accuracy: 0.9053\n",
            "--- Starting trial: run-15\n",
            "{'activationC': 'relu', 'activationD': 'selu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_15_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_15_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3844 - accuracy: 0.8627\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2753 - accuracy: 0.8997\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2448 - accuracy: 0.9109\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2201 - accuracy: 0.9198\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2009 - accuracy: 0.9258\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1847 - accuracy: 0.9323\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1732 - accuracy: 0.9365\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1613 - accuracy: 0.9406\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1480 - accuracy: 0.9459\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1390 - accuracy: 0.9492\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1288 - accuracy: 0.9525\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1219 - accuracy: 0.9548\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1137 - accuracy: 0.9590\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1059 - accuracy: 0.9611\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1002 - accuracy: 0.9633\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_15_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4308 - accuracy: 0.9114\n",
            "--- Starting trial: run-16\n",
            "{'activationC': 'relu', 'activationD': 'selu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_16_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_16_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3946 - accuracy: 0.8567\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2679 - accuracy: 0.9022\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2281 - accuracy: 0.9163\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1989 - accuracy: 0.9269\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1734 - accuracy: 0.9358\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1543 - accuracy: 0.9433\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1367 - accuracy: 0.9495\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1228 - accuracy: 0.9545\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1120 - accuracy: 0.9592\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0987 - accuracy: 0.9632\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0924 - accuracy: 0.9659\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0860 - accuracy: 0.9684\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0794 - accuracy: 0.9702\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0750 - accuracy: 0.9731\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0708 - accuracy: 0.9743\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_16_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3499 - accuracy: 0.9224\n",
            "--- Starting trial: run-17\n",
            "{'activationC': 'relu', 'activationD': 'selu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_17_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_17_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3848 - accuracy: 0.8619\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2795 - accuracy: 0.8990\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2483 - accuracy: 0.9101\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2257 - accuracy: 0.9190\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2099 - accuracy: 0.9237\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1976 - accuracy: 0.9280\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1849 - accuracy: 0.9331\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1766 - accuracy: 0.9349\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1658 - accuracy: 0.9409\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1565 - accuracy: 0.9433\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1487 - accuracy: 0.9467\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1447 - accuracy: 0.9483\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1363 - accuracy: 0.9512\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1316 - accuracy: 0.9519\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1244 - accuracy: 0.9558\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_17_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3906 - accuracy: 0.9030\n",
            "--- Starting trial: run-18\n",
            "{'activationC': 'selu', 'activationD': 'elu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_18_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_18_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4059 - accuracy: 0.8552\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2916 - accuracy: 0.8954\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2585 - accuracy: 0.9069\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2329 - accuracy: 0.9162\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2135 - accuracy: 0.9228\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1980 - accuracy: 0.9291\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1850 - accuracy: 0.9333\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1716 - accuracy: 0.9380\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1584 - accuracy: 0.9427\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1505 - accuracy: 0.9464\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1360 - accuracy: 0.9508\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1296 - accuracy: 0.9539\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1186 - accuracy: 0.9581\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1138 - accuracy: 0.9604\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1119 - accuracy: 0.9622\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_18_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3964 - accuracy: 0.9151\n",
            "--- Starting trial: run-19\n",
            "{'activationC': 'selu', 'activationD': 'elu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_19_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_19_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4096 - accuracy: 0.8504\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2823 - accuracy: 0.8983\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2414 - accuracy: 0.9112\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2094 - accuracy: 0.9224\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1883 - accuracy: 0.9301\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1686 - accuracy: 0.9379\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1529 - accuracy: 0.9446\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1378 - accuracy: 0.9492\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1258 - accuracy: 0.9529\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1173 - accuracy: 0.9569\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1090 - accuracy: 0.9600\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1025 - accuracy: 0.9635\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0923 - accuracy: 0.9664\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0937 - accuracy: 0.9668\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0869 - accuracy: 0.9685\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_19_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3435 - accuracy: 0.9192\n",
            "--- Starting trial: run-20\n",
            "{'activationC': 'selu', 'activationD': 'elu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_20_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_20_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4026 - accuracy: 0.8590\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2992 - accuracy: 0.8950\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2651 - accuracy: 0.9063\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2443 - accuracy: 0.9123\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2277 - accuracy: 0.9191\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2166 - accuracy: 0.9230\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2027 - accuracy: 0.9279\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1934 - accuracy: 0.9307\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1844 - accuracy: 0.9337\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1792 - accuracy: 0.9366\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1710 - accuracy: 0.9400\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1634 - accuracy: 0.9423\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1568 - accuracy: 0.9445\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1524 - accuracy: 0.9473\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1492 - accuracy: 0.9483\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_20_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4111 - accuracy: 0.8980\n",
            "--- Starting trial: run-21\n",
            "{'activationC': 'selu', 'activationD': 'relu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_21_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_21_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3977 - accuracy: 0.8566\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2932 - accuracy: 0.8949\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2574 - accuracy: 0.9075\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2383 - accuracy: 0.9131\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2182 - accuracy: 0.9204\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2031 - accuracy: 0.9264\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1922 - accuracy: 0.9296\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1811 - accuracy: 0.9335\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1711 - accuracy: 0.9370\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1619 - accuracy: 0.9412\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1566 - accuracy: 0.9421\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1484 - accuracy: 0.9464\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1397 - accuracy: 0.9491\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1370 - accuracy: 0.9500\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1277 - accuracy: 0.9531\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_21_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3604 - accuracy: 0.9063\n",
            "--- Starting trial: run-22\n",
            "{'activationC': 'selu', 'activationD': 'relu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_22_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_22_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4208 - accuracy: 0.8486\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2901 - accuracy: 0.8952\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2512 - accuracy: 0.9080\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2248 - accuracy: 0.9169\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2007 - accuracy: 0.9252\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1807 - accuracy: 0.9322\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1627 - accuracy: 0.9388\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1498 - accuracy: 0.9448\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1373 - accuracy: 0.9489\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1273 - accuracy: 0.9531\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1143 - accuracy: 0.9570\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1078 - accuracy: 0.9596\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0991 - accuracy: 0.9628\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0897 - accuracy: 0.9660\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0868 - accuracy: 0.9677\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_22_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3460 - accuracy: 0.9202\n",
            "--- Starting trial: run-23\n",
            "{'activationC': 'selu', 'activationD': 'relu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_23_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_23_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4009 - accuracy: 0.8577\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2940 - accuracy: 0.8951\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2621 - accuracy: 0.9059\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2441 - accuracy: 0.9125\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2296 - accuracy: 0.9179\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2159 - accuracy: 0.9209\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2071 - accuracy: 0.9236\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1968 - accuracy: 0.9289\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1878 - accuracy: 0.9327\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1814 - accuracy: 0.9352\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1729 - accuracy: 0.9375\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1707 - accuracy: 0.9384\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1628 - accuracy: 0.9417\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1621 - accuracy: 0.9416\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1541 - accuracy: 0.9443\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_23_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3405 - accuracy: 0.9021\n",
            "--- Starting trial: run-24\n",
            "{'activationC': 'selu', 'activationD': 'selu', 'activationE': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_24_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_24_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4103 - accuracy: 0.8568\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2969 - accuracy: 0.8943\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2630 - accuracy: 0.9057\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2381 - accuracy: 0.9142\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2209 - accuracy: 0.9201\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2031 - accuracy: 0.9264\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1862 - accuracy: 0.9325\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1730 - accuracy: 0.9375\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1632 - accuracy: 0.9410\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1529 - accuracy: 0.9457\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1393 - accuracy: 0.9500\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1347 - accuracy: 0.9524\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1277 - accuracy: 0.9552\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1161 - accuracy: 0.9595\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1116 - accuracy: 0.9632\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_24_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4267 - accuracy: 0.9094\n",
            "--- Starting trial: run-25\n",
            "{'activationC': 'selu', 'activationD': 'selu', 'activationE': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_25_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_25_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4050 - accuracy: 0.8531\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2794 - accuracy: 0.8985\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2404 - accuracy: 0.9116\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2108 - accuracy: 0.9218\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1848 - accuracy: 0.9307\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1649 - accuracy: 0.9391\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1538 - accuracy: 0.9429\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1391 - accuracy: 0.9490\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1304 - accuracy: 0.9532\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1216 - accuracy: 0.9563\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1121 - accuracy: 0.9585\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1010 - accuracy: 0.9637\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1021 - accuracy: 0.9637\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0991 - accuracy: 0.9640\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0909 - accuracy: 0.9684\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_25_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3552 - accuracy: 0.9161\n",
            "--- Starting trial: run-26\n",
            "{'activationC': 'selu', 'activationD': 'selu', 'activationE': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_26_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_26_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4098 - accuracy: 0.8558\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3021 - accuracy: 0.8935\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2701 - accuracy: 0.9028\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2510 - accuracy: 0.9115\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2311 - accuracy: 0.9171\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2180 - accuracy: 0.9213\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2097 - accuracy: 0.9258\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2006 - accuracy: 0.9283\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1919 - accuracy: 0.9319\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1841 - accuracy: 0.9352\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1799 - accuracy: 0.9372\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1687 - accuracy: 0.9416\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1693 - accuracy: 0.9420\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1578 - accuracy: 0.9459\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1577 - accuracy: 0.9464\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_26_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3977 - accuracy: 0.9006\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PagYJpwKSWYb",
        "outputId": "cba1a7e8-b41a-4d9d-deb0-0d9b4d94587a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "act2p"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('elu', 'elu', 'elu'): 0.9090999960899353,\n",
              " ('elu', 'elu', 'relu'): 0.9179999828338623,\n",
              " ('elu', 'elu', 'selu'): 0.896399974822998,\n",
              " ('elu', 'relu', 'elu'): 0.9075999855995178,\n",
              " ('elu', 'relu', 'relu'): 0.9200999736785889,\n",
              " ('elu', 'relu', 'selu'): 0.8960999846458435,\n",
              " ('elu', 'selu', 'elu'): 0.907800018787384,\n",
              " ('elu', 'selu', 'relu'): 0.9192000031471252,\n",
              " ('elu', 'selu', 'selu'): 0.8974999785423279,\n",
              " ('relu', 'elu', 'elu'): 0.911300003528595,\n",
              " ('relu', 'elu', 'relu'): 0.9241999983787537,\n",
              " ('relu', 'elu', 'selu'): 0.904699981212616,\n",
              " ('relu', 'relu', 'elu'): 0.9092000126838684,\n",
              " ('relu', 'relu', 'relu'): 0.9186000227928162,\n",
              " ('relu', 'relu', 'selu'): 0.9053000211715698,\n",
              " ('relu', 'selu', 'elu'): 0.9114000201225281,\n",
              " ('relu', 'selu', 'relu'): 0.9223999977111816,\n",
              " ('relu', 'selu', 'selu'): 0.902999997138977,\n",
              " ('selu', 'elu', 'elu'): 0.9150999784469604,\n",
              " ('selu', 'elu', 'relu'): 0.9192000031471252,\n",
              " ('selu', 'elu', 'selu'): 0.8980000019073486,\n",
              " ('selu', 'relu', 'elu'): 0.9063000082969666,\n",
              " ('selu', 'relu', 'relu'): 0.920199990272522,\n",
              " ('selu', 'relu', 'selu'): 0.9021000266075134,\n",
              " ('selu', 'selu', 'elu'): 0.9093999862670898,\n",
              " ('selu', 'selu', 'relu'): 0.916100025177002,\n",
              " ('selu', 'selu', 'selu'): 0.900600016117096}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Uao1kx3n8Cn",
        "outputId": "7ee69d9e-2e40-4170-90a2-19b5097b7067",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        }
      },
      "source": [
        "plotvarA = sorted(act2p.items(),key=lambda x: (x[1],x[0]))\n",
        "plt.bar([str(x[0]) for x in plotvarA],[x[1] for x in plotvarA])\n",
        "f = plt.gcf()\n",
        "f.set_size_inches(10,10)\n",
        "f.autofmt_xdate()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBW9LIqMtYBb"
      },
      "source": [
        "### 4 - Layer - Modified Network - 81 Combinations "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBiBtvTookxc"
      },
      "source": [
        "# HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32]))\n",
        "# HP_DROPOUT = hp.HParam('dropout', hp.RealInterval(0.1, 0.2))\n",
        "HP_ACTIVATIONS1 = hp.HParam('activation1',hp.Discrete(['relu','selu','elu']))\n",
        "HP_ACTIVATIONS2 = hp.HParam('activation2',hp.Discrete(['relu','selu','elu']))\n",
        "HP_ACTIVATIONS3 = hp.HParam('activation3',hp.Discrete(['relu','selu','elu']))\n",
        "HP_ACTIVATIONS4 = hp.HParam('activation4',hp.Discrete(['relu','selu','elu']))\n",
        "\n",
        "METRIC_ACCURACY = 'accuracy'\n",
        "\n",
        "with tf.summary.create_file_writer('logs/hparam_tuning_acts').as_default():\n",
        "  hp.hparams_config(\n",
        "    hparams=[HP_ACTIVATIONS1,HP_ACTIVATIONS2,HP_ACTIVATIONS3,HP_ACTIVATIONS4],\n",
        "    metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],\n",
        "  )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZN5fCP6ppkir"
      },
      "source": [
        "def train_test_model(hparams):\n",
        "  #model = load_model('/content/drive/My Drive/Colab Notebooks/CS581-TestResults/AutoKerasFashionMNIST.h5')\n",
        "    model = models.Sequential()\n",
        "    model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "    model.add(layers.Conv2D(32, kernel_size=(3,3), padding='same', activation=hparams[HP_ACTIVATIONS1], input_shape=(28,28)))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    #model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "    model.add(layers.Conv2D(64, kernel_size=(3,3), padding='same', activation=hparams[HP_ACTIVATIONS2]))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    #model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(512, activation=hparams[HP_ACTIVATIONS3]))\n",
        "    #model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "    model.add(layers.Dropout(0.50))\n",
        "    model.add(layers.Dense(512, activation=hparams[HP_ACTIVATIONS4]))\n",
        "    #model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "    model.add(layers.Dropout(0.50))\n",
        "    model.add(layers.Dense(10))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss='sparse_categorical_crossentropy',\n",
        "        metrics=['accuracy'],\n",
        "    )\n",
        "    model.fit(x_train, y_train, epochs=15) # Run with 1 epoch to speed things up for demo purposes\n",
        "    _, accuracy = model.evaluate(x_test, y_test)\n",
        "    return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtIa_l7sPqku"
      },
      "source": [
        "def run(run_dir, hparams):\n",
        "  with tf.summary.create_file_writer(run_dir).as_default():\n",
        "    hp.hparams(hparams)  # record the values used in this trial\n",
        "    accuracy = train_test_model(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)\n",
        "  return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZO0dmlQT1ue",
        "outputId": "1c50e5c8-22c7-4dea-de35-c654de01ea43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "session_num = 0\n",
        "actdict_4layer = {}\n",
        "for act1 in HP_ACTIVATIONS1.domain.values:\n",
        "    for act2 in HP_ACTIVATIONS2.domain.values:\n",
        "        for act3 in HP_ACTIVATIONS3.domain.values:\n",
        "            for act4 in HP_ACTIVATIONS4.domain.values:            \n",
        "                hparams = {\n",
        "                    HP_ACTIVATIONS1: act1,\n",
        "                    HP_ACTIVATIONS2: act2,\n",
        "                    HP_ACTIVATIONS3: act3,\n",
        "                    HP_ACTIVATIONS4: act4\n",
        "                }\n",
        "                run_name = \"run-%d\" % session_num\n",
        "                print('--- Starting trial: %s' % run_name)\n",
        "                print({h.name: hparams[h] for h in hparams})\n",
        "                actdict_4layer[(act1,act2,act3,act4)] = run('logs/hparam_tuning_acts/' + run_name, hparams)\n",
        "                session_num += 1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Starting trial: run-0\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_49_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_49_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3448 - accuracy: 0.1344\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1042\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1049\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1044\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1029\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 12.9098 - accuracy: 0.1004\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 9.9241 - accuracy: 0.1002\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3516 - accuracy: 0.0983\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3057 - accuracy: 0.0983\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3575 - accuracy: 0.1017\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0996\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3057 - accuracy: 0.1013\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3046 - accuracy: 0.1017\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1032\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_49_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-1\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_50_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_50_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3848 - accuracy: 0.1691\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1516\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1483\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3077 - accuracy: 0.1060\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0979\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0982\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1006\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1000\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_50_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-2\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_51_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_51_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3382 - accuracy: 0.1247\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.9561 - accuracy: 0.0973\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 3.8096 - accuracy: 0.0986\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 6.7991 - accuracy: 0.0984\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 8.7801 - accuracy: 0.0983\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3095 - accuracy: 0.0995\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3103 - accuracy: 0.0988\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3081 - accuracy: 0.0986\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3084 - accuracy: 0.0989\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3100 - accuracy: 0.0990\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3136 - accuracy: 0.0991\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3121 - accuracy: 0.0991\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3115 - accuracy: 0.0987\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3124 - accuracy: 0.1001\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3135 - accuracy: 0.0978\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_51_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-3\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_52_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_52_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3270 - accuracy: 0.1145\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1135\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1052\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1091\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1076\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1078\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1065\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1077\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3668 - accuracy: 0.1037\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3038 - accuracy: 0.1011\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.0985\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3044 - accuracy: 0.0999\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0997\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3049 - accuracy: 0.0992\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3046 - accuracy: 0.0992\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_52_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-4\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_53_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_53_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3906 - accuracy: 0.1193\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3059 - accuracy: 0.1013\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1033\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1028\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1010\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1041\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1029\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1015\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.1003\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1005\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1004\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_53_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-5\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_54_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_54_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3135 - accuracy: 0.1537\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1002\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3030 - accuracy: 0.1000\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0997\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3104 - accuracy: 0.1017\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0974\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0975\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0977\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0977\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0967\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0952\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0966\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_54_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-6\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_55_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_55_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3291 - accuracy: 0.1175\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1121\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1129\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4452 - accuracy: 0.1029\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3112 - accuracy: 0.0987\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3128 - accuracy: 0.0999\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3130 - accuracy: 0.0993\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3119 - accuracy: 0.1005\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3088 - accuracy: 0.1001\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3119 - accuracy: 0.1020\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3054 - accuracy: 0.1010\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3057 - accuracy: 0.1012\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.1019\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1008\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1002\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_55_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-7\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_56_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_56_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3396 - accuracy: 0.1128\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3099 - accuracy: 0.0999\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0997\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1017\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0988\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1000\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1010\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1000\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0981\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0985\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0986\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_56_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-8\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_57_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_57_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3207 - accuracy: 0.1045\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1006\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0988\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3254 - accuracy: 0.0997\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3059 - accuracy: 0.0993\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3062 - accuracy: 0.1018\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3050 - accuracy: 0.0980\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3356 - accuracy: 0.0973\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0973\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0998\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0985\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0987\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_57_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-9\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_58_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_58_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3474 - accuracy: 0.1911\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1440\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1448\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.1436\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1332\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1024\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3042 - accuracy: 0.1023\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1030\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1010\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1043\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1034\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1029\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1037\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1007\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1037\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_58_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-10\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_59_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_59_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3971 - accuracy: 0.1412\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1309\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1307\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1300\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3049 - accuracy: 0.1224\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3077 - accuracy: 0.1050\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3064 - accuracy: 0.1013\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3066 - accuracy: 0.0997\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1000\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3288 - accuracy: 0.0987\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0994\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0998\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1008\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_59_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-11\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_60_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_60_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.6399 - accuracy: 0.1845\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1964\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3024 - accuracy: 0.1994\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1976\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1985\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.2000\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1985\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1962\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3141 - accuracy: 0.1745\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3048 - accuracy: 0.0989\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0967\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0979\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0972\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0980\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_60_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.0140\n",
            "--- Starting trial: run-12\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_61_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_61_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4108 - accuracy: 0.1246\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.1149\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1247\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3042 - accuracy: 0.1082\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1022\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1022\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1045\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1056\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1053\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1043\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1045\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1035\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1058\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1040\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_61_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.0937\n",
            "--- Starting trial: run-13\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_62_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_62_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3285 - accuracy: 0.1040\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0993\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1000\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0994\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3029 - accuracy: 0.1001\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0972\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0972\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0960\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0969\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0988\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0971\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_62_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-14\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_63_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_63_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3575 - accuracy: 0.1090\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3040 - accuracy: 0.1121\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1099\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1104\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1067\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3167 - accuracy: 0.1002\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3062 - accuracy: 0.0981\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3059 - accuracy: 0.0991\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3053 - accuracy: 0.0991\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3045 - accuracy: 0.0976\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0982\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1008\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0992\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0987\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_63_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-15\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_64_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_64_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4718 - accuracy: 0.1740\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1140\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1137\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1124\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1145\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1119\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1135\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1131\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1147\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1131\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1130\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1127\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1132\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1119\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1116\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_64_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1444\n",
            "--- Starting trial: run-16\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_65_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_65_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3574 - accuracy: 0.1137\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3045 - accuracy: 0.0994\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1004\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0989\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0989\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0997\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0992\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0994\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1001\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1002\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0975\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0994\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_65_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-17\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_66_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_66_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4088 - accuracy: 0.2504\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3048 - accuracy: 0.1324\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1337\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1101\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0981\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0985\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0992\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0988\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0986\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1024\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3114 - accuracy: 0.0992\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3193 - accuracy: 0.1028\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3055 - accuracy: 0.1005\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_66_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-18\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_67_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_67_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4403 - accuracy: 0.3768\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.5415 - accuracy: 0.2785\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.2309\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3022 - accuracy: 0.2324\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.2319\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1964\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.1389\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1388\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1425\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1401\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1388\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1401\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1400\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1397\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1382\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_67_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.2052\n",
            "--- Starting trial: run-19\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_68_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_68_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3306 - accuracy: 0.1088\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1027\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1030\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1080\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1033\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0991\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0995\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1018\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1008\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0992\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0992\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1009\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1008\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1006\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0975\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_68_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-20\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_69_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_69_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3438 - accuracy: 0.1439\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3051 - accuracy: 0.1000\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.0953\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0935\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0960\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0945\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0954\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0951\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0953\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0944\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0953\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0953\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0955\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0944\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0952\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_69_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-21\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_70_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_70_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3268 - accuracy: 0.1131\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1083\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1104\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1090\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1105\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1087\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1071\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1092\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1083\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1092\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1088\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1082\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1094\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1090\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1086\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_70_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "--- Starting trial: run-22\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_71_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_71_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3240 - accuracy: 0.1051\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3044 - accuracy: 0.1043\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3424 - accuracy: 0.1018\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3261 - accuracy: 0.0986\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3181 - accuracy: 0.0989\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3133 - accuracy: 0.0994\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3110 - accuracy: 0.0990\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3135 - accuracy: 0.0997\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3117 - accuracy: 0.1007\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3085 - accuracy: 0.1009\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3064 - accuracy: 0.0994\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3085 - accuracy: 0.1003\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3078 - accuracy: 0.0997\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3080 - accuracy: 0.1006\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3085 - accuracy: 0.1007\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_71_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-23\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_72_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_72_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3677 - accuracy: 0.1519\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3056 - accuracy: 0.1095\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1008\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0978\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3052 - accuracy: 0.1030\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3045 - accuracy: 0.1017\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3051 - accuracy: 0.1011\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.0993\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0989\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0988\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1005\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0991\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1005\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1009\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0990\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_72_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-24\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_73_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_73_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.6309 - accuracy: 0.2376\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1506\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3031 - accuracy: 0.1507\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3029 - accuracy: 0.1516\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.1442\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1077\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1067\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1081\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1074\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1087\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1085\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1070\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1074\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1098\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1070\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_73_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1004\n",
            "--- Starting trial: run-25\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_74_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_74_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3391 - accuracy: 0.1092\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1057\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1063\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3049 - accuracy: 0.1062\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3047 - accuracy: 0.1033\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3050 - accuracy: 0.1005\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3047 - accuracy: 0.1009\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3051 - accuracy: 0.0996\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3053 - accuracy: 0.1006\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3060 - accuracy: 0.0999\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3057 - accuracy: 0.0995\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3051 - accuracy: 0.1011\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3049 - accuracy: 0.0988\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.1003\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_74_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-26\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_75_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_75_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 3.1865 - accuracy: 0.2985\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3230 - accuracy: 0.1562\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3087 - accuracy: 0.1009\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1045\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1044\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1007\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1033\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1026\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1016\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1026\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1021\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1013\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_75_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1765\n",
            "--- Starting trial: run-27\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_76_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_76_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4227 - accuracy: 0.3174\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3843 - accuracy: 0.3788\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1153\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1166\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1156\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1144\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1162\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.1168\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1161\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1150\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1160\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1155\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1162\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1156\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1163\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_76_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1973\n",
            "--- Starting trial: run-28\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_77_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_77_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3938 - accuracy: 0.1286\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1186\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1156\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1172\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1169\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1149\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3060 - accuracy: 0.1058\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0994\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.0977\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0973\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0991\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0998\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_77_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-29\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_78_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_78_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3229 - accuracy: 0.0951\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0955\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0940\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3188 - accuracy: 0.0962\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0971\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.0980\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0981\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0975\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0977\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0985\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0979\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0974\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_78_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.0938\n",
            "--- Starting trial: run-30\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_79_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_79_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3195 - accuracy: 0.0961\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0917\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0936\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0902\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0926\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0948\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0900\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0931\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0893\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0923\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3086 - accuracy: 0.0968\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.0989\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1005\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_79_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-31\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_80_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_80_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3391 - accuracy: 0.1147\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1127\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1144\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3098 - accuracy: 0.1052\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0992\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1004\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0981\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0998\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0979\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.0969\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0979\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0989\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0977\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0979\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_80_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-32\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_81_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_81_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3248 - accuracy: 0.1106\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1025\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1077\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3065 - accuracy: 0.1019\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0991\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0994\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0991\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0989\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_81_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-33\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_82_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_82_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3299 - accuracy: 0.1175\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3080 - accuracy: 0.1114\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1101\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1114\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1079\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3069 - accuracy: 0.1002\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.0986\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0993\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1007\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1004\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1010\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1004\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0988\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0982\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_82_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-34\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_83_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_83_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.7751 - accuracy: 0.2643\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1542\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.1264\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1244\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1241\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1229\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1233\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1269\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1224\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1259\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1232\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1258\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1220\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1232\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1212\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_83_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-35\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_84_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_84_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3710 - accuracy: 0.1533\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1480\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3031 - accuracy: 0.1472\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1314\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1302\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1297\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1297\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1302\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1307\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1278\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3808 - accuracy: 0.1113\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1013\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1016\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1012\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0999\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_84_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-36\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_85_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_85_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3210 - accuracy: 0.1051\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1040\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1043\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1031\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1039\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1040\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1066\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1042\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1043\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1064\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1041\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1051\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1063\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1036\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_85_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-37\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_86_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_86_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3310 - accuracy: 0.1142\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1097\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1067\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1068\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1082\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1085\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1099\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1091\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1046\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1085\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1081\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1072\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1079\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1085\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1067\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_86_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-38\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_87_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_87_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.6265 - accuracy: 0.1916\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3144 - accuracy: 0.1056\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3082 - accuracy: 0.1093\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1067\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3049 - accuracy: 0.1091\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1001\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1003\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3046 - accuracy: 0.1012\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1006\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0988\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3044 - accuracy: 0.1007\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1006\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0994\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1012\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_87_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-39\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_88_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_88_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3240 - accuracy: 0.1089\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1036\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1073\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1083\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1098\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1085\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1085\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1069\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1072\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1093\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1093\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1093\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1088\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1110\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_88_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1937\n",
            "--- Starting trial: run-40\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_89_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_89_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3256 - accuracy: 0.1542\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1155\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1143\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1154\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1141\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1150\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1155\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1182\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1108\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1013\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0980\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0982\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_89_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-41\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_90_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_90_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3250 - accuracy: 0.1093\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3038 - accuracy: 0.0962\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1037\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1024\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1021\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1010\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1026\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0993\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0995\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1016\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1012\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_90_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-42\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_91_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_91_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3591 - accuracy: 0.1357\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1390\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3025 - accuracy: 0.1411\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1416\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1412\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1397\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1416\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1407\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1392\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1417\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1434\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1406\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1422\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1419\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1387\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_91_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.2867\n",
            "--- Starting trial: run-43\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_92_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_92_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4908 - accuracy: 0.2341\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1293\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1169\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1141\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1137\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1142\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1149\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1148\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1139\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1140\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1139\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1145\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1148\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1136\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1141\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_92_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "--- Starting trial: run-44\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_93_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_93_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3456 - accuracy: 0.1155\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1091\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1045\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1048\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1030\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1041\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1058\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1040\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1029\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1046\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1041\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1049\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3062 - accuracy: 0.1022\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_93_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-45\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_94_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_94_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4145 - accuracy: 0.1188\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1102\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3060 - accuracy: 0.1059\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3079 - accuracy: 0.0995\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1016\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1001\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.0979\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1006\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.0982\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1000\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1003\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1010\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0973\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.0991\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_94_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-46\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_95_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_95_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3324 - accuracy: 0.1110\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3039 - accuracy: 0.1021\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1017\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1016\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1024\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1031\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1037\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1031\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1040\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1035\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1030\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1027\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1038\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1018\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1033\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_95_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-47\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_96_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_96_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3219 - accuracy: 0.1087\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3080 - accuracy: 0.0983\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3040 - accuracy: 0.0991\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0985\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3047 - accuracy: 0.0991\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1004\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0992\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1005\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1016\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_96_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-48\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_97_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_97_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3246 - accuracy: 0.1091\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3072 - accuracy: 0.1013\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1005\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1010\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1015\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1006\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1004\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0998\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0989\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1011\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_97_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-49\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_98_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_98_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3619 - accuracy: 0.1060\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3033 - accuracy: 0.1067\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.1047\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1005\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0994\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1000\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1002\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1004\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1010\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1002\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1008\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_98_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-50\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_99_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_99_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3365 - accuracy: 0.1190\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1169\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1158\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1150\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3057 - accuracy: 0.1028\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1029\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1036\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1038\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1026\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1033\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1029\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1035\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1033\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_99_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-51\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_100_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_100_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4755 - accuracy: 0.1960\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3033 - accuracy: 0.1430\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1451\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1453\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1426\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1433\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1436\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1437\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1469\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1453\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1449\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1444\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1430\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1455\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1456\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_100_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.2385\n",
            "--- Starting trial: run-52\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_101_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_101_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3918 - accuracy: 0.1248\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3116 - accuracy: 0.1082\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1004\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1005\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1016\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0998\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1018\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1015\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0995\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1016\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_101_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1045\n",
            "--- Starting trial: run-53\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_102_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_102_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3372 - accuracy: 0.1501\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3127 - accuracy: 0.1373\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0998\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0960\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0979\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0992\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3109 - accuracy: 0.1003\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3068 - accuracy: 0.0995\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3056 - accuracy: 0.1000\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3065 - accuracy: 0.1011\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3082 - accuracy: 0.1009\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3062 - accuracy: 0.1004\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3074 - accuracy: 0.1007\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3076 - accuracy: 0.1015\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_102_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-54\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_103_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_103_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 3.6300 - accuracy: 0.2587\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1091\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1102\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1100\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1095\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1105\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1081\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1101\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1104\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1095\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1077\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1080\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1070\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1072\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1075\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_103_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1723\n",
            "--- Starting trial: run-55\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_104_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_104_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3469 - accuracy: 0.1226\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1209\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1242\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3221 - accuracy: 0.1113\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3090 - accuracy: 0.1016\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3076 - accuracy: 0.1018\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3064 - accuracy: 0.1034\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1016\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1029\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1032\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1019\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1011\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1032\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1016\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1030\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_104_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-56\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_105_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_105_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3851 - accuracy: 0.1207\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1171\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1152\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3188 - accuracy: 0.1139\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.0990\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0997\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0995\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0980\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.1006\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3046 - accuracy: 0.1001\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0989\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0982\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_105_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.0987\n",
            "--- Starting trial: run-57\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_106_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_106_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3267 - accuracy: 0.1077\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1007\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1058\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1057\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1038\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1051\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1024\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1040\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1021\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1067\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1053\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1027\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1030\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_106_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1937\n",
            "--- Starting trial: run-58\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_107_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_107_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4009 - accuracy: 0.0989\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.0986\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.0997\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.0993\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0979\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0994\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1008\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0988\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1022\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0985\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0995\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0989\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1001\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0982\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0988\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_107_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-59\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_108_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_108_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3273 - accuracy: 0.1083\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.0995\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3139 - accuracy: 0.1113\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.0994\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3046 - accuracy: 0.1004\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3046 - accuracy: 0.0995\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1024\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1006\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3044 - accuracy: 0.0992\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1002\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1019\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3042 - accuracy: 0.0993\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1005\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1005\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0986\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_108_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "--- Starting trial: run-60\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_109_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_109_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3198 - accuracy: 0.1083\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3087 - accuracy: 0.1098\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.0978\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3104 - accuracy: 0.1022\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1029\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1036\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1047\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1019\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1026\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1023\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1042\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1028\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1041\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1038\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_109_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-61\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_110_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_110_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3283 - accuracy: 0.1150\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.1027\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1017\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0987\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0955\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0969\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0981\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0971\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.0957\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0975\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0964\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3161 - accuracy: 0.0961\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1015\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_110_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-62\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_111_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_111_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4078 - accuracy: 0.0881\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3100 - accuracy: 0.0945\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3053 - accuracy: 0.0988\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1009\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3042 - accuracy: 0.1003\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3048 - accuracy: 0.1007\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.0969\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.1007\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1101\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1077\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1067\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1100\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1092\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1089\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1080\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_111_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-63\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_112_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_112_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3394 - accuracy: 0.1027\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0974\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0939\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0974\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0980\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0959\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0974\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0975\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0960\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0974\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3411 - accuracy: 0.0984\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1023\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3046 - accuracy: 0.1024\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3048 - accuracy: 0.1029\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1026\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_112_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-64\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_113_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_113_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 3.0629 - accuracy: 0.1750\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1318\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1300\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1303\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1323\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1302\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1299\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1301\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1291\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1268\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0962\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0884\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0913\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0895\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0887\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_113_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-65\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_114_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_114_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3452 - accuracy: 0.1176\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3059 - accuracy: 0.1064\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1057\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1024\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1048\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1037\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1038\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1038\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1046\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1051\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1051\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1055\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1016\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1025\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_114_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-66\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_115_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_115_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3369 - accuracy: 0.2196\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3047 - accuracy: 0.1200\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3050 - accuracy: 0.0995\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.1023\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0995\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0978\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1022\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1003\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1013\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0983\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.0982\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0994\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0995\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0991\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_115_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-67\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_116_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_116_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3280 - accuracy: 0.1051\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1007\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1021\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1036\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1042\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1027\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1054\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1036\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1049\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1044\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1035\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1025\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1022\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1031\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_116_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-68\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_117_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_117_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3420 - accuracy: 0.1181\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1046\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1062\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1044\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1054\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1054\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1073\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1067\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1055\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1040\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1042\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1044\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1048\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1064\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_117_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-69\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_118_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_118_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3780 - accuracy: 0.1709\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1648\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1640\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1655\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1641\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1653\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1638\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1648\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1640\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1720\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3167 - accuracy: 0.1246\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3042 - accuracy: 0.1002\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3044 - accuracy: 0.1002\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1008\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1013\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_118_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1470\n",
            "--- Starting trial: run-70\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_119_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_119_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3250 - accuracy: 0.0983\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1085\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1069\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1063\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1092\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0996\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3044 - accuracy: 0.0989\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3052 - accuracy: 0.0989\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3083 - accuracy: 0.1014\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3091 - accuracy: 0.1014\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_119_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-71\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_120_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_120_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3274 - accuracy: 0.1074\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1085\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1091\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1014\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0994\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.0944\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.0940\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.0981\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1010\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1005\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1018\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1015\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_120_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-72\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_121_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_121_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4388 - accuracy: 0.1481\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3042 - accuracy: 0.1025\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1041\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1050\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.0997\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3048 - accuracy: 0.1000\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.0991\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3109 - accuracy: 0.0990\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1002\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1009\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0980\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1012\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1001\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0997\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0996\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_121_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-73\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_122_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_122_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3291 - accuracy: 0.1153\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1007\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0959\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0961\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1028\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3025 - accuracy: 0.1000\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0978\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0986\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0967\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0966\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0987\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1004\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.0972\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0977\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0997\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_122_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-74\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'elu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_123_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_123_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3273 - accuracy: 0.1247\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3080 - accuracy: 0.1300\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1041\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1038\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1059\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1053\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1042\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1062\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1073\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1027\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1055\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1052\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_123_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1138\n",
            "--- Starting trial: run-75\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_124_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_124_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.4060 - accuracy: 0.1447\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1569\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1523\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1319\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1299\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.1310\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3294 - accuracy: 0.1022\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.0983\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0974\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0988\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1012\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1008\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1024\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_124_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-76\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_125_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_125_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3840 - accuracy: 0.1448\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3089 - accuracy: 0.1054\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3042 - accuracy: 0.1041\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1038\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.0988\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1014\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.0994\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1018\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1003\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.0992\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1001\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1007\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0991\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1018\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0998\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_125_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-77\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'relu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_126_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_126_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3355 - accuracy: 0.1191\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3038 - accuracy: 0.1070\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1008\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1007\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_126_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.0933\n",
            "--- Starting trial: run-78\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'elu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_127_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_127_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3514 - accuracy: 0.1418\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3076 - accuracy: 0.1261\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3037 - accuracy: 0.1101\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1015\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3225 - accuracy: 0.1023\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1009\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1004\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1016\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3042 - accuracy: 0.1029\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3029 - accuracy: 0.1017\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1018\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1037\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1021\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1024\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.1036\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_127_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-79\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'relu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_128_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_128_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3414 - accuracy: 0.1082\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3056 - accuracy: 0.1003\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1025\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0998\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1041\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1039\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0995\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1007\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_128_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.0662\n",
            "--- Starting trial: run-80\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'selu', 'activation4': 'selu'}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_129_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_129_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3381 - accuracy: 0.1157\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1006\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1001\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0981\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1007\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1010\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1008\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1008\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3758 - accuracy: 0.0979\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3302 - accuracy: 0.0993\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3347 - accuracy: 0.1009\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3195 - accuracy: 0.1008\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_129_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dq7R_ID6a1UU",
        "outputId": "6ce7f8f0-ac58-40b8-a4a0-ee6f1842d33d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        " print(f\"Number of Sessions: {session_num}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Sessions: 81\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFX9QEhBUT09",
        "outputId": "4897a0e0-6630-47dd-8e69-5596f16f9506",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "actdict_4layer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('elu', 'elu', 'elu', 'elu'): 0.10000000149011612,\n",
              " ('elu', 'elu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'elu', 'elu', 'selu'): 0.10000000149011612,\n",
              " ('elu', 'elu', 'relu', 'elu'): 0.10000000149011612,\n",
              " ('elu', 'elu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'elu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('elu', 'elu', 'selu', 'elu'): 0.10000000149011612,\n",
              " ('elu', 'elu', 'selu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'elu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('elu', 'relu', 'elu', 'elu'): 0.10000000149011612,\n",
              " ('elu', 'relu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'relu', 'elu', 'selu'): 0.014000000432133675,\n",
              " ('elu', 'relu', 'relu', 'elu'): 0.09369999915361404,\n",
              " ('elu', 'relu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'relu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('elu', 'relu', 'selu', 'elu'): 0.1444000005722046,\n",
              " ('elu', 'relu', 'selu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'relu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('elu', 'selu', 'elu', 'elu'): 0.20520000159740448,\n",
              " ('elu', 'selu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'selu', 'elu', 'selu'): 0.10000000149011612,\n",
              " ('elu', 'selu', 'relu', 'elu'): 0.10189999639987946,\n",
              " ('elu', 'selu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'selu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('elu', 'selu', 'selu', 'elu'): 0.10040000081062317,\n",
              " ('elu', 'selu', 'selu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'selu', 'selu', 'selu'): 0.17649999260902405,\n",
              " ('relu', 'elu', 'elu', 'elu'): 0.1973000019788742,\n",
              " ('relu', 'elu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'elu', 'elu', 'selu'): 0.09380000084638596,\n",
              " ('relu', 'elu', 'relu', 'elu'): 0.10000000149011612,\n",
              " ('relu', 'elu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'elu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'elu', 'selu', 'elu'): 0.10000000149011612,\n",
              " ('relu', 'elu', 'selu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'elu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'relu', 'elu', 'elu'): 0.10000000149011612,\n",
              " ('relu', 'relu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'relu', 'elu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'relu', 'relu', 'elu'): 0.19370000064373016,\n",
              " ('relu', 'relu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'relu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'relu', 'selu', 'elu'): 0.2867000102996826,\n",
              " ('relu', 'relu', 'selu', 'relu'): 0.10019999742507935,\n",
              " ('relu', 'relu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'elu', 'elu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'elu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'relu', 'elu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'selu', 'elu'): 0.23849999904632568,\n",
              " ('relu', 'selu', 'selu', 'relu'): 0.10450000315904617,\n",
              " ('relu', 'selu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('selu', 'elu', 'elu', 'elu'): 0.17229999601840973,\n",
              " ('selu', 'elu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'elu', 'elu', 'selu'): 0.09870000183582306,\n",
              " ('selu', 'elu', 'relu', 'elu'): 0.19370000064373016,\n",
              " ('selu', 'elu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'elu', 'relu', 'selu'): 0.10010000318288803,\n",
              " ('selu', 'elu', 'selu', 'elu'): 0.10000000149011612,\n",
              " ('selu', 'elu', 'selu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'elu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'elu', 'elu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'elu', 'selu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'relu', 'elu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'selu', 'elu'): 0.1469999998807907,\n",
              " ('selu', 'relu', 'selu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('selu', 'selu', 'elu', 'elu'): 0.10000000149011612,\n",
              " ('selu', 'selu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'selu', 'elu', 'selu'): 0.11379999667406082,\n",
              " ('selu', 'selu', 'relu', 'elu'): 0.10000000149011612,\n",
              " ('selu', 'selu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'selu', 'relu', 'selu'): 0.093299999833107,\n",
              " ('selu', 'selu', 'selu', 'elu'): 0.10000000149011612,\n",
              " ('selu', 'selu', 'selu', 'relu'): 0.06620000302791595,\n",
              " ('selu', 'selu', 'selu', 'selu'): 0.10000000149011612}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yws71OcNbBqb",
        "outputId": "87b5db5e-cf35-4243-d310-e8f465593602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(sorted(actdict_4layer.items(),key=lambda x: (x[1],x[0])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(('elu', 'relu', 'elu', 'selu'), 0.014000000432133675), (('selu', 'selu', 'selu', 'relu'), 0.06620000302791595), (('selu', 'selu', 'relu', 'selu'), 0.093299999833107), (('elu', 'relu', 'relu', 'elu'), 0.09369999915361404), (('relu', 'elu', 'elu', 'selu'), 0.09380000084638596), (('selu', 'elu', 'elu', 'selu'), 0.09870000183582306), (('elu', 'elu', 'elu', 'elu'), 0.10000000149011612), (('elu', 'elu', 'elu', 'relu'), 0.10000000149011612), (('elu', 'elu', 'elu', 'selu'), 0.10000000149011612), (('elu', 'elu', 'relu', 'elu'), 0.10000000149011612), (('elu', 'elu', 'relu', 'relu'), 0.10000000149011612), (('elu', 'elu', 'relu', 'selu'), 0.10000000149011612), (('elu', 'elu', 'selu', 'elu'), 0.10000000149011612), (('elu', 'elu', 'selu', 'relu'), 0.10000000149011612), (('elu', 'elu', 'selu', 'selu'), 0.10000000149011612), (('elu', 'relu', 'elu', 'elu'), 0.10000000149011612), (('elu', 'relu', 'elu', 'relu'), 0.10000000149011612), (('elu', 'relu', 'relu', 'relu'), 0.10000000149011612), (('elu', 'relu', 'relu', 'selu'), 0.10000000149011612), (('elu', 'relu', 'selu', 'relu'), 0.10000000149011612), (('elu', 'relu', 'selu', 'selu'), 0.10000000149011612), (('elu', 'selu', 'elu', 'relu'), 0.10000000149011612), (('elu', 'selu', 'elu', 'selu'), 0.10000000149011612), (('elu', 'selu', 'relu', 'relu'), 0.10000000149011612), (('elu', 'selu', 'relu', 'selu'), 0.10000000149011612), (('elu', 'selu', 'selu', 'relu'), 0.10000000149011612), (('relu', 'elu', 'elu', 'relu'), 0.10000000149011612), (('relu', 'elu', 'relu', 'elu'), 0.10000000149011612), (('relu', 'elu', 'relu', 'relu'), 0.10000000149011612), (('relu', 'elu', 'relu', 'selu'), 0.10000000149011612), (('relu', 'elu', 'selu', 'elu'), 0.10000000149011612), (('relu', 'elu', 'selu', 'relu'), 0.10000000149011612), (('relu', 'elu', 'selu', 'selu'), 0.10000000149011612), (('relu', 'relu', 'elu', 'elu'), 0.10000000149011612), (('relu', 'relu', 'elu', 'relu'), 0.10000000149011612), (('relu', 'relu', 'elu', 'selu'), 0.10000000149011612), (('relu', 'relu', 'relu', 'relu'), 0.10000000149011612), (('relu', 'relu', 'relu', 'selu'), 0.10000000149011612), (('relu', 'relu', 'selu', 'selu'), 0.10000000149011612), (('relu', 'selu', 'elu', 'elu'), 0.10000000149011612), (('relu', 'selu', 'elu', 'relu'), 0.10000000149011612), (('relu', 'selu', 'elu', 'selu'), 0.10000000149011612), (('relu', 'selu', 'relu', 'elu'), 0.10000000149011612), (('relu', 'selu', 'relu', 'relu'), 0.10000000149011612), (('relu', 'selu', 'relu', 'selu'), 0.10000000149011612), (('relu', 'selu', 'selu', 'selu'), 0.10000000149011612), (('selu', 'elu', 'elu', 'relu'), 0.10000000149011612), (('selu', 'elu', 'relu', 'relu'), 0.10000000149011612), (('selu', 'elu', 'selu', 'elu'), 0.10000000149011612), (('selu', 'elu', 'selu', 'relu'), 0.10000000149011612), (('selu', 'elu', 'selu', 'selu'), 0.10000000149011612), (('selu', 'relu', 'elu', 'elu'), 0.10000000149011612), (('selu', 'relu', 'elu', 'relu'), 0.10000000149011612), (('selu', 'relu', 'elu', 'selu'), 0.10000000149011612), (('selu', 'relu', 'relu', 'elu'), 0.10000000149011612), (('selu', 'relu', 'relu', 'relu'), 0.10000000149011612), (('selu', 'relu', 'relu', 'selu'), 0.10000000149011612), (('selu', 'relu', 'selu', 'relu'), 0.10000000149011612), (('selu', 'relu', 'selu', 'selu'), 0.10000000149011612), (('selu', 'selu', 'elu', 'elu'), 0.10000000149011612), (('selu', 'selu', 'elu', 'relu'), 0.10000000149011612), (('selu', 'selu', 'relu', 'elu'), 0.10000000149011612), (('selu', 'selu', 'relu', 'relu'), 0.10000000149011612), (('selu', 'selu', 'selu', 'elu'), 0.10000000149011612), (('selu', 'selu', 'selu', 'selu'), 0.10000000149011612), (('selu', 'elu', 'relu', 'selu'), 0.10010000318288803), (('relu', 'relu', 'selu', 'relu'), 0.10019999742507935), (('elu', 'selu', 'selu', 'elu'), 0.10040000081062317), (('elu', 'selu', 'relu', 'elu'), 0.10189999639987946), (('relu', 'selu', 'selu', 'relu'), 0.10450000315904617), (('selu', 'selu', 'elu', 'selu'), 0.11379999667406082), (('elu', 'relu', 'selu', 'elu'), 0.1444000005722046), (('selu', 'relu', 'selu', 'elu'), 0.1469999998807907), (('selu', 'elu', 'elu', 'elu'), 0.17229999601840973), (('elu', 'selu', 'selu', 'selu'), 0.17649999260902405), (('relu', 'relu', 'relu', 'elu'), 0.19370000064373016), (('selu', 'elu', 'relu', 'elu'), 0.19370000064373016), (('relu', 'elu', 'elu', 'elu'), 0.1973000019788742), (('elu', 'selu', 'elu', 'elu'), 0.20520000159740448), (('relu', 'selu', 'selu', 'elu'), 0.23849999904632568), (('relu', 'relu', 'selu', 'elu'), 0.2867000102996826)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jahLPMPo__GR",
        "outputId": "d9f996d6-f711-4e80-e3ac-137365b159d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "plotvar2 = sorted(actdict_4layer.items(),key=lambda x: (x[1],x[0]))\n",
        "plt.bar([str(x[0]) for x in plotvar2],[x[1] for x in plotvar2])\n",
        "f = plt.gcf()\n",
        "f.set_size_inches(25,25)\n",
        "f.autofmt_xdate()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOicR65vAigC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ehx8dDrjAjcy"
      },
      "source": [
        "# Number of Units Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RP8t1bWwneuD"
      },
      "source": [
        "HP_FILTER1 = hp.HParam('filter1', hp.Discrete([32,64,128]))\n",
        "HP_FILTER2 = hp.HParam('filter2', hp.Discrete([32,64,128]))\n",
        "HP_NUM_UNITS1 = hp.HParam('num_units1', hp.Discrete([256,512,1024]))\n",
        "\n",
        "METRIC_ACCURACY = 'accuracy'\n",
        "\n",
        "with tf.summary.create_file_writer('logs/hparam_tuning').as_default():\n",
        "  hp.hparams_config(\n",
        "    hparams=[HP_FILTER1,HP_FILTER2,HP_NUM_UNITS1],\n",
        "    metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],\n",
        "  )"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXrTGDegn23z"
      },
      "source": [
        "def train_test_model(hparams):\n",
        "  #model = load_model('/content/drive/My Drive/Colab Notebooks/CS581-TestResults/AutoKerasFashionMNIST.h5')\n",
        "    model = models.Sequential()\n",
        "    model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "    model.add(layers.Conv2D(hparams[HP_FILTER1], kernel_size=(3,3), padding='same', activation='relu', input_shape=(28,28)))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model.add(layers.Conv2D(hparams[HP_FILTER2], kernel_size=(3,3), padding='same', activation='elu'))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(hparams[HP_NUM_UNITS1], activation='relu'))\n",
        "    model.add(layers.Dropout(0.50))\n",
        "    model.add(layers.Dense(10))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "        metrics=['accuracy'],\n",
        "    )\n",
        "    model.fit(x_train, y_train, epochs=15) # Run with 1 epoch to speed things up for demo purposes\n",
        "    _, accuracy = model.evaluate(x_test, y_test)\n",
        "    return accuracy"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6D3yCI-0t3z8"
      },
      "source": [
        "def run(run_dir, hparams):\n",
        "  with tf.summary.create_file_writer(run_dir).as_default():\n",
        "    hp.hparams(hparams)  # record the values used in this trial\n",
        "    accuracy = train_test_model(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)\n",
        "  return accuracy"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hR1JoRtEt8mJ",
        "outputId": "afe90f1a-ca0e-4fc6-88c6-c139a273752d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "session_num = 0\n",
        "act2pop = {}\n",
        "for filter1 in HP_FILTER1.domain.values:\n",
        "    for filter2 in HP_FILTER2.domain.values:\n",
        "        for units1 in HP_NUM_UNITS1.domain.values:\n",
        "            hparams = {\n",
        "                HP_FILTER1: filter1,\n",
        "                HP_FILTER2: filter2,\n",
        "                HP_NUM_UNITS1: units1\n",
        "            }\n",
        "            run_name = \"run-%d\" % session_num\n",
        "            print('--- Starting trial: %s' % run_name)\n",
        "            print({h.name: hparams[h] for h in hparams})\n",
        "            act2pop[(filter1,filter2,units1)] = run('logs/hparam_opt_tuning/' + run_name, hparams)\n",
        "            session_num += 1\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Starting trial: run-0\n",
            "{'filter1': 32, 'filter2': 32, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_27_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_27_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4445 - accuracy: 0.8389\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2923 - accuracy: 0.8928\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2550 - accuracy: 0.9056\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2310 - accuracy: 0.9151\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2109 - accuracy: 0.9207\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1923 - accuracy: 0.9283\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1800 - accuracy: 0.9337\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1654 - accuracy: 0.9378\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1536 - accuracy: 0.9427\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1405 - accuracy: 0.9471\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1336 - accuracy: 0.9490\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1258 - accuracy: 0.9524\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1183 - accuracy: 0.9562\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1107 - accuracy: 0.9585\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1039 - accuracy: 0.9611\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_27_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2670 - accuracy: 0.9228\n",
            "--- Starting trial: run-1\n",
            "{'filter1': 32, 'filter2': 32, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_28_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_28_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4208 - accuracy: 0.8466\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2768 - accuracy: 0.8985\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2381 - accuracy: 0.9118\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2097 - accuracy: 0.9209\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1882 - accuracy: 0.9305\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1687 - accuracy: 0.9367\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1525 - accuracy: 0.9430\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1383 - accuracy: 0.9481\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1259 - accuracy: 0.9523\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1152 - accuracy: 0.9569\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1017 - accuracy: 0.9617\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0989 - accuracy: 0.9630\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0895 - accuracy: 0.9666\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0813 - accuracy: 0.9697\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0807 - accuracy: 0.9703\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_28_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3210 - accuracy: 0.9221\n",
            "--- Starting trial: run-2\n",
            "{'filter1': 32, 'filter2': 32, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_29_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_29_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4005 - accuracy: 0.8546\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2681 - accuracy: 0.9000\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2253 - accuracy: 0.9156\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1981 - accuracy: 0.9270\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1726 - accuracy: 0.9355\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1533 - accuracy: 0.9435\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1340 - accuracy: 0.9499\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1209 - accuracy: 0.9547\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1044 - accuracy: 0.9612\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0940 - accuracy: 0.9653\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0843 - accuracy: 0.9696\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0773 - accuracy: 0.9721\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0726 - accuracy: 0.9735\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0654 - accuracy: 0.9761\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0618 - accuracy: 0.9782\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_29_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4220 - accuracy: 0.9160\n",
            "--- Starting trial: run-3\n",
            "{'filter1': 32, 'filter2': 64, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_30_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_30_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4296 - accuracy: 0.8456\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2842 - accuracy: 0.8967\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2463 - accuracy: 0.9097\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2184 - accuracy: 0.9194\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1936 - accuracy: 0.9279\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1766 - accuracy: 0.9343\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1567 - accuracy: 0.9418\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1471 - accuracy: 0.9449\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1343 - accuracy: 0.9491\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1222 - accuracy: 0.9536\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1136 - accuracy: 0.9572\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1034 - accuracy: 0.9614\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0950 - accuracy: 0.9637\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0936 - accuracy: 0.9656\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0871 - accuracy: 0.9671\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_30_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3000 - accuracy: 0.9181\n",
            "--- Starting trial: run-4\n",
            "{'filter1': 32, 'filter2': 64, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_31_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_31_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3967 - accuracy: 0.8542\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2637 - accuracy: 0.9029\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2253 - accuracy: 0.9158\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1994 - accuracy: 0.9257\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1738 - accuracy: 0.9361\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1524 - accuracy: 0.9440\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1368 - accuracy: 0.9493\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1204 - accuracy: 0.9547\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1079 - accuracy: 0.9591\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1012 - accuracy: 0.9623\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0911 - accuracy: 0.9661\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0831 - accuracy: 0.9689\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0758 - accuracy: 0.9721\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0705 - accuracy: 0.9739\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0677 - accuracy: 0.9757\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_31_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3575 - accuracy: 0.9217\n",
            "--- Starting trial: run-5\n",
            "{'filter1': 32, 'filter2': 64, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_32_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_32_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3842 - accuracy: 0.8605\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2573 - accuracy: 0.9052\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2196 - accuracy: 0.9190\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1865 - accuracy: 0.9308\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1640 - accuracy: 0.9390\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1402 - accuracy: 0.9491\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1226 - accuracy: 0.9536\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1056 - accuracy: 0.9599\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0914 - accuracy: 0.9661\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0859 - accuracy: 0.9680\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0768 - accuracy: 0.9722\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0706 - accuracy: 0.9742\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0646 - accuracy: 0.9767\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0606 - accuracy: 0.9778\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0560 - accuracy: 0.9800\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_32_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4041 - accuracy: 0.9243\n",
            "--- Starting trial: run-6\n",
            "{'filter1': 32, 'filter2': 128, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_33_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_33_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4149 - accuracy: 0.8505\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2806 - accuracy: 0.8964\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2412 - accuracy: 0.9108\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2147 - accuracy: 0.9208\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1885 - accuracy: 0.9298\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1704 - accuracy: 0.9365\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1536 - accuracy: 0.9424\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1376 - accuracy: 0.9485\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1293 - accuracy: 0.9516\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1159 - accuracy: 0.9554\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1061 - accuracy: 0.9598\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0990 - accuracy: 0.9627\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0915 - accuracy: 0.9654\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0844 - accuracy: 0.9690\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0831 - accuracy: 0.9687\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_33_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3344 - accuracy: 0.9165\n",
            "--- Starting trial: run-7\n",
            "{'filter1': 32, 'filter2': 128, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_34_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_34_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3978 - accuracy: 0.8558\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2681 - accuracy: 0.9020\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2288 - accuracy: 0.9156\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1986 - accuracy: 0.9264\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1740 - accuracy: 0.9354\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1505 - accuracy: 0.9439\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1337 - accuracy: 0.9499\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1169 - accuracy: 0.9563\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1041 - accuracy: 0.9611\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0955 - accuracy: 0.9647\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0861 - accuracy: 0.9678\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0801 - accuracy: 0.9706\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0768 - accuracy: 0.9724\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0661 - accuracy: 0.9759\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0653 - accuracy: 0.9768\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_34_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3494 - accuracy: 0.9181\n",
            "--- Starting trial: run-8\n",
            "{'filter1': 32, 'filter2': 128, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_35_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_35_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3730 - accuracy: 0.8645\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2553 - accuracy: 0.9053\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2124 - accuracy: 0.9212\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1815 - accuracy: 0.9330\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1563 - accuracy: 0.9416\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1315 - accuracy: 0.9513\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1170 - accuracy: 0.9570\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0976 - accuracy: 0.9641\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0925 - accuracy: 0.9663\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0839 - accuracy: 0.9693\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0755 - accuracy: 0.9719\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0693 - accuracy: 0.9752\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0642 - accuracy: 0.9767\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0619 - accuracy: 0.9788\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0613 - accuracy: 0.9793\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_35_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4446 - accuracy: 0.9198\n",
            "--- Starting trial: run-9\n",
            "{'filter1': 64, 'filter2': 32, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_36_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_36_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4508 - accuracy: 0.8356\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3002 - accuracy: 0.8898\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2595 - accuracy: 0.9045\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2316 - accuracy: 0.9149\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2109 - accuracy: 0.9226\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1930 - accuracy: 0.9284\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1764 - accuracy: 0.9337\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1619 - accuracy: 0.9396\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1496 - accuracy: 0.9442\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1405 - accuracy: 0.9476\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1302 - accuracy: 0.9506\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1256 - accuracy: 0.9527\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1161 - accuracy: 0.9571\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1094 - accuracy: 0.9594\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1042 - accuracy: 0.9617\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_36_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2829 - accuracy: 0.9247\n",
            "--- Starting trial: run-10\n",
            "{'filter1': 64, 'filter2': 32, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_37_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_37_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4105 - accuracy: 0.8505\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2764 - accuracy: 0.8983\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2390 - accuracy: 0.9115\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2103 - accuracy: 0.9204\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1871 - accuracy: 0.9305\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1662 - accuracy: 0.9379\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1542 - accuracy: 0.9419\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1385 - accuracy: 0.9479\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1237 - accuracy: 0.9540\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1149 - accuracy: 0.9574\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1051 - accuracy: 0.9609\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0952 - accuracy: 0.9646\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0900 - accuracy: 0.9665\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0836 - accuracy: 0.9685\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0789 - accuracy: 0.9704\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_37_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3033 - accuracy: 0.9235\n",
            "--- Starting trial: run-11\n",
            "{'filter1': 64, 'filter2': 32, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_38_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_38_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3950 - accuracy: 0.8563\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2663 - accuracy: 0.9007\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2242 - accuracy: 0.9169\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1946 - accuracy: 0.9278\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1714 - accuracy: 0.9365\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1516 - accuracy: 0.9427\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1331 - accuracy: 0.9518\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1179 - accuracy: 0.9566\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1044 - accuracy: 0.9612\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0977 - accuracy: 0.9635\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0849 - accuracy: 0.9687\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0779 - accuracy: 0.9711\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0713 - accuracy: 0.9739\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0677 - accuracy: 0.9756\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0632 - accuracy: 0.9771\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_38_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3655 - accuracy: 0.9222\n",
            "--- Starting trial: run-12\n",
            "{'filter1': 64, 'filter2': 64, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_39_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_39_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4273 - accuracy: 0.8460\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2824 - accuracy: 0.8966\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2461 - accuracy: 0.9104\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2150 - accuracy: 0.9215\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1983 - accuracy: 0.9255\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1772 - accuracy: 0.9341\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1588 - accuracy: 0.9399\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1449 - accuracy: 0.9457\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1345 - accuracy: 0.9489\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1228 - accuracy: 0.9535\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1134 - accuracy: 0.9570\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1059 - accuracy: 0.9601\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0977 - accuracy: 0.9626\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0904 - accuracy: 0.9658\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0881 - accuracy: 0.9660\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_39_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2865 - accuracy: 0.9202\n",
            "--- Starting trial: run-13\n",
            "{'filter1': 64, 'filter2': 64, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_40_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_40_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4011 - accuracy: 0.8544\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2683 - accuracy: 0.9021\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2302 - accuracy: 0.9148\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1982 - accuracy: 0.9268\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1746 - accuracy: 0.9343\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1547 - accuracy: 0.9414\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1380 - accuracy: 0.9486\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1237 - accuracy: 0.9538\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1089 - accuracy: 0.9589\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0980 - accuracy: 0.9639\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0897 - accuracy: 0.9665\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0807 - accuracy: 0.9707\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0799 - accuracy: 0.9711\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0700 - accuracy: 0.9739\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0691 - accuracy: 0.9752\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_40_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3311 - accuracy: 0.9220\n",
            "--- Starting trial: run-14\n",
            "{'filter1': 64, 'filter2': 64, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_41_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_41_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3760 - accuracy: 0.8630\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2578 - accuracy: 0.9038\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2169 - accuracy: 0.9196\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1874 - accuracy: 0.9303\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1610 - accuracy: 0.9412\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1393 - accuracy: 0.9478\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1169 - accuracy: 0.9559\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1029 - accuracy: 0.9619\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0943 - accuracy: 0.9650\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0831 - accuracy: 0.9694\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0752 - accuracy: 0.9729\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0679 - accuracy: 0.9753\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0647 - accuracy: 0.9774\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0620 - accuracy: 0.9782\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0592 - accuracy: 0.9799\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_41_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3476 - accuracy: 0.9200\n",
            "--- Starting trial: run-15\n",
            "{'filter1': 64, 'filter2': 128, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_42_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_42_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4207 - accuracy: 0.8479\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2849 - accuracy: 0.8955\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2422 - accuracy: 0.9113\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2154 - accuracy: 0.9197\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1906 - accuracy: 0.9290\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1678 - accuracy: 0.9372\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1547 - accuracy: 0.9411\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1399 - accuracy: 0.9474\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1272 - accuracy: 0.9520\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1126 - accuracy: 0.9577\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1060 - accuracy: 0.9605\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0975 - accuracy: 0.9631\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0902 - accuracy: 0.9660\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0860 - accuracy: 0.9679\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0801 - accuracy: 0.9704\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_42_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3564 - accuracy: 0.9191\n",
            "--- Starting trial: run-16\n",
            "{'filter1': 64, 'filter2': 128, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_43_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_43_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3937 - accuracy: 0.8577\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2671 - accuracy: 0.9016\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2260 - accuracy: 0.9157\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1947 - accuracy: 0.9274\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1707 - accuracy: 0.9357\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1478 - accuracy: 0.9451\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1325 - accuracy: 0.9506\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1173 - accuracy: 0.9559\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1025 - accuracy: 0.9627\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0943 - accuracy: 0.9650\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0838 - accuracy: 0.9695\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0793 - accuracy: 0.9708\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0720 - accuracy: 0.9740\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0667 - accuracy: 0.9756\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0673 - accuracy: 0.9767\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_43_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4235 - accuracy: 0.9228\n",
            "--- Starting trial: run-17\n",
            "{'filter1': 64, 'filter2': 128, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_44_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_44_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3752 - accuracy: 0.8634\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2566 - accuracy: 0.9065\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2122 - accuracy: 0.9214\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1786 - accuracy: 0.9337\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1558 - accuracy: 0.9417\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1328 - accuracy: 0.9509\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1133 - accuracy: 0.9591\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1003 - accuracy: 0.9632\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0896 - accuracy: 0.9668\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0794 - accuracy: 0.9719\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0752 - accuracy: 0.9735\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0681 - accuracy: 0.9764\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0627 - accuracy: 0.9786\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0587 - accuracy: 0.9799\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0599 - accuracy: 0.9805\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_44_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4361 - accuracy: 0.9249\n",
            "--- Starting trial: run-18\n",
            "{'filter1': 128, 'filter2': 32, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_45_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_45_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4382 - accuracy: 0.8403\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2936 - accuracy: 0.8927\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2550 - accuracy: 0.9071\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2268 - accuracy: 0.9158\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2077 - accuracy: 0.9232\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1876 - accuracy: 0.9292\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1712 - accuracy: 0.9353\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1585 - accuracy: 0.9403\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1458 - accuracy: 0.9444\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1356 - accuracy: 0.9488\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1256 - accuracy: 0.9538\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1212 - accuracy: 0.9548\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1143 - accuracy: 0.9568\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1045 - accuracy: 0.9603\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1000 - accuracy: 0.9626\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_45_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2962 - accuracy: 0.9210\n",
            "--- Starting trial: run-19\n",
            "{'filter1': 128, 'filter2': 32, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_46_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_46_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4163 - accuracy: 0.8493\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2766 - accuracy: 0.8978\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2385 - accuracy: 0.9117\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2102 - accuracy: 0.9215\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1874 - accuracy: 0.9302\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1683 - accuracy: 0.9365\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1524 - accuracy: 0.9432\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1353 - accuracy: 0.9497\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1222 - accuracy: 0.9539\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1110 - accuracy: 0.9586\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1031 - accuracy: 0.9616\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0941 - accuracy: 0.9650\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0910 - accuracy: 0.9653\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0815 - accuracy: 0.9691\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0771 - accuracy: 0.9715\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_46_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3360 - accuracy: 0.9180\n",
            "--- Starting trial: run-20\n",
            "{'filter1': 128, 'filter2': 32, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_47_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_47_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3922 - accuracy: 0.8562\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2637 - accuracy: 0.9028\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2248 - accuracy: 0.9158\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1949 - accuracy: 0.9289\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1720 - accuracy: 0.9363\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1503 - accuracy: 0.9440\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1305 - accuracy: 0.9505\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1174 - accuracy: 0.9560\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1067 - accuracy: 0.9602\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0944 - accuracy: 0.9649\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0829 - accuracy: 0.9695\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0796 - accuracy: 0.9708\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0700 - accuracy: 0.9744\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0649 - accuracy: 0.9763\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0622 - accuracy: 0.9777\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_47_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3787 - accuracy: 0.9233\n",
            "--- Starting trial: run-21\n",
            "{'filter1': 128, 'filter2': 64, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_48_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_48_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4212 - accuracy: 0.8468\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2861 - accuracy: 0.8949\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2464 - accuracy: 0.9092\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2189 - accuracy: 0.9198\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1955 - accuracy: 0.9272\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1748 - accuracy: 0.9345\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1602 - accuracy: 0.9392\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1449 - accuracy: 0.9466\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1337 - accuracy: 0.9500\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1208 - accuracy: 0.9549\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1131 - accuracy: 0.9578\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1024 - accuracy: 0.9615\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0927 - accuracy: 0.9642\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0910 - accuracy: 0.9662\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0879 - accuracy: 0.9671\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_48_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3223 - accuracy: 0.9245\n",
            "--- Starting trial: run-22\n",
            "{'filter1': 128, 'filter2': 64, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_49_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_49_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3995 - accuracy: 0.8538\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2697 - accuracy: 0.8996\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.2292 - accuracy: 0.9147\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2002 - accuracy: 0.9249\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1761 - accuracy: 0.9337\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1554 - accuracy: 0.9408\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1387 - accuracy: 0.9480\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1234 - accuracy: 0.9536\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1123 - accuracy: 0.9585\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0996 - accuracy: 0.9627\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0902 - accuracy: 0.9668\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0853 - accuracy: 0.9684\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0792 - accuracy: 0.9700\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0719 - accuracy: 0.9736\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0700 - accuracy: 0.9748\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_49_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3902 - accuracy: 0.9212\n",
            "--- Starting trial: run-23\n",
            "{'filter1': 128, 'filter2': 64, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_50_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_50_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3834 - accuracy: 0.8603\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2613 - accuracy: 0.9043\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2190 - accuracy: 0.9187\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1907 - accuracy: 0.9287\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1625 - accuracy: 0.9398\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1398 - accuracy: 0.9481\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1213 - accuracy: 0.9540\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1055 - accuracy: 0.9610\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0920 - accuracy: 0.9664\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0856 - accuracy: 0.9686\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0793 - accuracy: 0.9714\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0703 - accuracy: 0.9748\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0643 - accuracy: 0.9769\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0618 - accuracy: 0.9787\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0551 - accuracy: 0.9808\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_50_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4422 - accuracy: 0.9215\n",
            "--- Starting trial: run-24\n",
            "{'filter1': 128, 'filter2': 128, 'num_units1': 256}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_51_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_51_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.4111 - accuracy: 0.8508\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2777 - accuracy: 0.8980\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2367 - accuracy: 0.9120\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2102 - accuracy: 0.9217\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1861 - accuracy: 0.9299\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1658 - accuracy: 0.9380\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1493 - accuracy: 0.9439\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1320 - accuracy: 0.9505\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1211 - accuracy: 0.9541\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1103 - accuracy: 0.9594\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1035 - accuracy: 0.9612\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0953 - accuracy: 0.9645\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0924 - accuracy: 0.9647\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0830 - accuracy: 0.9693\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0802 - accuracy: 0.9705\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_51_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3233 - accuracy: 0.9255\n",
            "--- Starting trial: run-25\n",
            "{'filter1': 128, 'filter2': 128, 'num_units1': 512}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_52_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_52_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3898 - accuracy: 0.8593\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2654 - accuracy: 0.9032\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2258 - accuracy: 0.9171\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1977 - accuracy: 0.9269\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1690 - accuracy: 0.9370\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1505 - accuracy: 0.9441\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1295 - accuracy: 0.9513\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1168 - accuracy: 0.9559\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1038 - accuracy: 0.9615\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0932 - accuracy: 0.9657\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0879 - accuracy: 0.9672\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0782 - accuracy: 0.9703\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0714 - accuracy: 0.9740\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0701 - accuracy: 0.9747\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0654 - accuracy: 0.9766\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_52_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4066 - accuracy: 0.9237\n",
            "--- Starting trial: run-26\n",
            "{'filter1': 128, 'filter2': 128, 'num_units1': 1024}\n",
            "Epoch 1/15\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_53_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_53_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.3755 - accuracy: 0.8632\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.2576 - accuracy: 0.9046\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.2168 - accuracy: 0.9194\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1856 - accuracy: 0.9310\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1593 - accuracy: 0.9422\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1350 - accuracy: 0.9495\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1158 - accuracy: 0.9565\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1046 - accuracy: 0.9621\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0962 - accuracy: 0.9658\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0820 - accuracy: 0.9703\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0767 - accuracy: 0.9734\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0679 - accuracy: 0.9756\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0662 - accuracy: 0.9771\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0649 - accuracy: 0.9778\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0625 - accuracy: 0.9784\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_53_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4946 - accuracy: 0.9195\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2cxgAYD63gm",
        "outputId": "9f5ef4e1-041a-48a7-897b-8b2381f7cfbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(f'Number of Sessions: {session_num}')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Sessions: 27\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4srgS1Z61CT",
        "outputId": "041bd08b-303a-447c-ee68-8620ec27fa1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "act2pop"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{(32, 32, 256): 0.9228000044822693,\n",
              " (32, 32, 512): 0.9221000075340271,\n",
              " (32, 32, 1024): 0.9160000085830688,\n",
              " (32, 64, 256): 0.9180999994277954,\n",
              " (32, 64, 512): 0.9217000007629395,\n",
              " (32, 64, 1024): 0.9243000149726868,\n",
              " (32, 128, 256): 0.9164999723434448,\n",
              " (32, 128, 512): 0.9180999994277954,\n",
              " (32, 128, 1024): 0.9197999835014343,\n",
              " (64, 32, 256): 0.9247000217437744,\n",
              " (64, 32, 512): 0.9235000014305115,\n",
              " (64, 32, 1024): 0.9222000241279602,\n",
              " (64, 64, 256): 0.920199990272522,\n",
              " (64, 64, 512): 0.921999990940094,\n",
              " (64, 64, 1024): 0.9200000166893005,\n",
              " (64, 128, 256): 0.9190999865531921,\n",
              " (64, 128, 512): 0.9228000044822693,\n",
              " (64, 128, 1024): 0.9248999953269958,\n",
              " (128, 32, 256): 0.9210000038146973,\n",
              " (128, 32, 512): 0.9179999828338623,\n",
              " (128, 32, 1024): 0.92330002784729,\n",
              " (128, 64, 256): 0.9244999885559082,\n",
              " (128, 64, 512): 0.9211999773979187,\n",
              " (128, 64, 1024): 0.921500027179718,\n",
              " (128, 128, 256): 0.9254999756813049,\n",
              " (128, 128, 512): 0.9236999750137329,\n",
              " (128, 128, 1024): 0.9194999933242798}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3NuHzQD6_ga",
        "outputId": "cee73fd4-e7e3-4447-edc7-c13811bab763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(sorted(act2pop.items(),key=lambda x: (x[1],x[0])))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[((32, 32, 1024), 0.9160000085830688), ((32, 128, 256), 0.9164999723434448), ((128, 32, 512), 0.9179999828338623), ((32, 64, 256), 0.9180999994277954), ((32, 128, 512), 0.9180999994277954), ((64, 128, 256), 0.9190999865531921), ((128, 128, 1024), 0.9194999933242798), ((32, 128, 1024), 0.9197999835014343), ((64, 64, 1024), 0.9200000166893005), ((64, 64, 256), 0.920199990272522), ((128, 32, 256), 0.9210000038146973), ((128, 64, 512), 0.9211999773979187), ((128, 64, 1024), 0.921500027179718), ((32, 64, 512), 0.9217000007629395), ((64, 64, 512), 0.921999990940094), ((32, 32, 512), 0.9221000075340271), ((64, 32, 1024), 0.9222000241279602), ((32, 32, 256), 0.9228000044822693), ((64, 128, 512), 0.9228000044822693), ((128, 32, 1024), 0.92330002784729), ((64, 32, 512), 0.9235000014305115), ((128, 128, 512), 0.9236999750137329), ((32, 64, 1024), 0.9243000149726868), ((128, 64, 256), 0.9244999885559082), ((64, 32, 256), 0.9247000217437744), ((64, 128, 1024), 0.9248999953269958), ((128, 128, 256), 0.9254999756813049)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9jmXEm67GeA",
        "outputId": "b8d32a7b-6523-4520-bc38-02e2179c98db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823
        }
      },
      "source": [
        "plotvarB = sorted(act2pop.items(),key=lambda x: (x[1],x[0]))\n",
        "plt.bar([str(x[0]) for x in plotvarB],[x[1] for x in plotvarB])\n",
        "f = plt.gcf()\n",
        "f.set_size_inches(15,15)\n",
        "f.autofmt_xdate()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jz0Y0clqAEry"
      },
      "source": [
        "# END"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CkN5kM5AFod"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}