A2_Q2_FashionMNISTHandwritten_Hparams.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "A2-Q2-FashionMNISTHandwritten-Hparams.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QoVODpmI8aH",
        "colab_type": "toc"
      },
      "source": [
        ">[Imports](#scrollTo=Hjlzw-dmn0dv)\n",
        "\n",
        ">[Data Preprocessing](#scrollTo=X8d-bgUMn3ny)\n",
        "\n",
        ">[HandWritten Model](#scrollTo=puk-2jSmn_87)\n",
        "\n",
        ">[Loading AutoKeras Model](#scrollTo=hgi1B6uxoFEK)\n",
        "\n",
        ">[HParams Tuning](#scrollTo=1owSJxzqoTr9)\n",
        "\n",
        ">[Optimizer Tuning](#scrollTo=NN07Woq0oWqr)\n",
        "\n",
        ">[Activation function Tuning](#scrollTo=kJqyVJW9nyJL)\n",
        "\n",
        ">>>[3 - layer Handwritten Network - 27 Combinations](#scrollTo=OanuAAlbUZ0I)\n",
        "\n",
        ">>>[4 - Layer - Modified Network - 81 Combinations](#scrollTo=uBW9LIqMtYBb)\n",
        "\n",
        ">[Number of Units Training](#scrollTo=Ehx8dDrjAjcy)\n",
        "\n",
        ">[Re-Do Handwritten model with finetuned parameters](#scrollTo=lcv3K_BXEZcU)\n",
        "\n",
        ">[END](#scrollTo=jz0Y0clqAEry)\n",
        "\n"
      ]
    },
    {
      "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": 19,
      "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": 20,
      "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": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8d-bgUMn3ny"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awLhr3eynTjo"
      },
      "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": 22,
      "outputs": []
    },
    {
      "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": 23,
      "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": 24,
      "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": 25,
      "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": 26,
      "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": 27,
      "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": 28,
      "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": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRV3mzFnDHwE"
      },
      "source": [
        "from tensorboard.plugins.hparams import api as hp"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUMrTNj-VAXO"
      },
      "source": [
        "# Clear any logs from previous runs\n",
        "!rm -rf ./logs/ "
      ],
      "execution_count": 31,
      "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": 32,
      "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": 33,
      "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": 34,
      "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": 35,
      "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": 36,
      "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": 37,
      "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": "474cddd8-bdd8-44aa-d6f8-f001ec45e5b2",
        "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": 38,
      "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": "markdown",
      "metadata": {
        "id": "kJqyVJW9nyJL"
      },
      "source": [
        "# Activation function Tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OanuAAlbUZ0I"
      },
      "source": [
        "### 3 - layer Handwritten Network - 27 Combinations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1gWam-itxgv"
      },
      "source": [
        "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",
        "\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],\n",
        "    metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],\n",
        "  )"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ak5mKmr1tzzK"
      },
      "source": [
        "def train_test_model2(hparams):\n",
        "    model2 = models.Sequential()\n",
        "    model2.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "    model2.add(layers.Conv2D(32, kernel_size=(3,3), padding='same', activation=hparams[HP_ACTIVATIONS1], input_shape=(28,28)))\n",
        "    model2.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model2.add(layers.Conv2D(64, kernel_size=(3,3), padding='same', activation=hparams[HP_ACTIVATIONS2]))\n",
        "    model2.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    model2.add(layers.Flatten())\n",
        "    model2.add(layers.Dense(512, activation=hparams[HP_ACTIVATIONS3]))\n",
        "    model2.add(layers.Dropout(0.50))\n",
        "    model2.add(layers.Dense(10))\n",
        "    model2.compile(\n",
        "        optimizer='adam',\n",
        "        loss='sparse_categorical_crossentropy',\n",
        "        metrics=['accuracy'],\n",
        "    )\n",
        "    model2.fit(x_train, y_train, epochs=15) # Run with 1 epoch to speed things up for demo purposes\n",
        "    _, accuracy = model2.evaluate(x_test, y_test)\n",
        "    return accuracy"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLkymvkmt2PO"
      },
      "source": [
        "def run2(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_model2(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)\n",
        "  return accuracy"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ViAdXmQyt47R",
        "outputId": "9f9018cd-fd0a-498f-ceae-f02eda401cd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "session_num = 0\n",
        "actdict = {}\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",
        "            hparams = {\n",
        "                HP_ACTIVATIONS1: act1,\n",
        "                HP_ACTIVATIONS2: act2,\n",
        "                HP_ACTIVATIONS3: act3,\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[(act1,act2,act3)] = run2('logs/hparam_tuning_acts/' + run_name, hparams)\n",
        "            session_num += 1\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Starting trial: run-0\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 1.9126 - accuracy: 0.3884\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3057 - accuracy: 0.2604\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.2080\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.2079\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3024 - accuracy: 0.2060\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.2087\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3024 - accuracy: 0.2079\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3043 - accuracy: 0.1952\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3038 - accuracy: 0.1234\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3088 - accuracy: 0.1246\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1382\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.1345\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.1367\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3029 - accuracy: 0.1348\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.1374\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: 2.3026 - accuracy: 0.2385\n",
            "--- Starting trial: run-1\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3225 - accuracy: 0.1349\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3125 - accuracy: 0.1289\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1012\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3034 - accuracy: 0.1001\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1017\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1016\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1031\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1007\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1019\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1017\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.1015\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1028\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1053\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: 2.3026 - accuracy: 0.1395\n",
            "--- Starting trial: run-2\n",
            "{'activation1': 'elu', 'activation2': 'elu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.4415 - accuracy: 0.3361\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.9502 - accuracy: 0.4381\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.6911 - accuracy: 0.4388\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.2773 - accuracy: 0.3409\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3021 - accuracy: 0.3006\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3016 - accuracy: 0.2986\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3007 - accuracy: 0.2902\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3022 - accuracy: 0.2927\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3021 - accuracy: 0.2950\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3024 - accuracy: 0.2929\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3024 - accuracy: 0.2938\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2946\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3141 - accuracy: 0.2695\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3071 - accuracy: 0.1184\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3045 - accuracy: 0.1191\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: 2.3026 - accuracy: 0.1124\n",
            "--- Starting trial: run-3\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3188 - accuracy: 0.1245\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1185\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1158\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1055\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1027\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1035\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1044\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3174 - accuracy: 0.0991\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3037 - accuracy: 0.1023\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3042 - accuracy: 0.1011\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.0996\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.0987\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1001\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-4\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3761 - accuracy: 0.1462\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3045 - accuracy: 0.1222\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1094\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1131\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3076 - accuracy: 0.1172\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3050 - accuracy: 0.0984\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0971\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0972\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0988\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0963\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0978\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0971\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0969\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0973\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0975\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-5\n",
            "{'activation1': 'elu', 'activation2': 'relu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3188 - accuracy: 0.1443\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1027\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1047\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1037\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1031\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1029\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1035\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3176 - accuracy: 0.1017\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1013\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-6\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3019 - accuracy: 0.3698\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.1966 - accuracy: 0.4198\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.1722\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3036 - accuracy: 0.1664\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1641\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3034 - accuracy: 0.1415\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1084\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.1125\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3057 - accuracy: 0.1096\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3073 - accuracy: 0.0906\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3035 - accuracy: 0.0938\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3042 - accuracy: 0.0958\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.0923\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3042 - accuracy: 0.0931\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0972\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-7\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3381 - accuracy: 0.1133\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1090\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3062 - accuracy: 0.1067\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3037 - accuracy: 0.1055\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1056\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1012\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1034\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1026\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1030\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1017\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1009\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1022\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1012\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1033\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1013\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: 2.3026 - accuracy: 0.1005\n",
            "--- Starting trial: run-8\n",
            "{'activation1': 'elu', 'activation2': 'selu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3955 - accuracy: 0.1222\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1207\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1241\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1188\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1207\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3061 - accuracy: 0.0891\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0979\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0972\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0986\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0979\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0961\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0992\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0981\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.0973\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0986\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-9\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.0710 - accuracy: 0.3748\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3023 - accuracy: 0.2425\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.2082\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.1997\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1980\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.1999\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1988\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3025 - accuracy: 0.1980\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3025 - accuracy: 0.1991\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3150 - accuracy: 0.1540\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3060 - accuracy: 0.1007\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1008\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1031\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3035 - accuracy: 0.1043\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.1061\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: 2.3026 - accuracy: 0.1652\n",
            "--- Starting trial: run-10\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3351 - accuracy: 0.1316\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1062\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1007\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0982\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1015\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1021\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3084 - accuracy: 0.1014\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.0970\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0989\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-11\n",
            "{'activation1': 'relu', 'activation2': 'elu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3256 - accuracy: 0.1549\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3045 - accuracy: 0.1348\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.1114\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1090\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1094\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1097\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1094\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1095\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1098\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3074 - accuracy: 0.1039\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1033\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1047\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1037\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1056\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-12\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3490 - accuracy: 0.1588\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3024 - accuracy: 0.1688\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3045 - accuracy: 0.1393\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.1036\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1047\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1046\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1046\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1046\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1037\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1055\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1053\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1011\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0968\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0997\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: 2.3026 - accuracy: 0.0994\n",
            "--- Starting trial: run-13\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3325 - accuracy: 0.1089\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1099\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1087\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1081\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1056\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1097\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1084\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1071\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3053 - accuracy: 0.1056\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.0983\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3034 - accuracy: 0.1036\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1001\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-14\n",
            "{'activation1': 'relu', 'activation2': 'relu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.4406 - accuracy: 0.1896\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3029 - accuracy: 0.1217\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1153\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.1261\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1294\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3028 - accuracy: 0.1157\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1150\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1142\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1140\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1136\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1136\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1135\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1140\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1152\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1118\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: 2.3026 - accuracy: 0.1003\n",
            "--- Starting trial: run-15\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.4055 - accuracy: 0.1557\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1003\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0998\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0988\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0995\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0977\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3047 - accuracy: 0.1006\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3212 - accuracy: 0.1030\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-16\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.3179 - accuracy: 0.1014\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3080 - accuracy: 0.1039\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3035 - accuracy: 0.1042\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1012\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1025\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1018\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1021\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3095 - accuracy: 0.1017\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0994\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0993\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1021\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1001\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-17\n",
            "{'activation1': 'relu', 'activation2': 'selu', 'activation3': '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 [==============================] - 4s 2ms/step - loss: 2.4310 - accuracy: 0.3282\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.0541 - accuracy: 0.3423\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3036 - accuracy: 0.1636\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3047 - accuracy: 0.1598\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3042 - accuracy: 0.1600\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3300 - accuracy: 0.1178\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3051 - accuracy: 0.1040\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.1049\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.1048\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1034\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1034\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1055\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1042\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1059\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-18\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'elu'}\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 [==============================] - 4s 2ms/step - loss: 2.3342 - accuracy: 0.1260\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1235\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1237\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.1236\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1210\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1255\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1246\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1235\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1265\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1223\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1258\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1225\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1263\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1245\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1229\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: 2.3026 - accuracy: 0.1850\n",
            "--- Starting trial: run-19\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'relu'}\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 [==============================] - 4s 2ms/step - loss: 2.3398 - accuracy: 0.0996\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3036 - accuracy: 0.1099\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1042\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3030 - accuracy: 0.1044\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1055\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1046\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3037 - accuracy: 0.0985\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.1015\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0965\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0967\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0992\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0945\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0932\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0953\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0953\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-20\n",
            "{'activation1': 'selu', 'activation2': 'elu', 'activation3': 'selu'}\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 [==============================] - 4s 2ms/step - loss: 2.6424 - accuracy: 0.2944\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2823\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.2749\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.2754\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3100 - accuracy: 0.1151\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1047\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1042\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3066 - accuracy: 0.1038\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3107 - accuracy: 0.1034\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1022\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1018\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0986\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0994\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-21\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'elu'}\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 [==============================] - 4s 2ms/step - loss: 2.4065 - accuracy: 0.1111\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.0994\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1043\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1045\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1044\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1049\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1036\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1055\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1035\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1040\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1050\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: 2.3026 - accuracy: 0.1782\n",
            "--- Starting trial: run-22\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'relu'}\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 [==============================] - 4s 2ms/step - loss: 2.3488 - accuracy: 0.1462\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3031 - accuracy: 0.1278\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1176\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3035 - accuracy: 0.1174\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3034 - accuracy: 0.1002\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1023\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1010\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1016\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1021\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1031\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1030\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1032\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1029\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1132\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: 2.3026 - accuracy: 0.1673\n",
            "--- Starting trial: run-23\n",
            "{'activation1': 'selu', 'activation2': 'relu', 'activation3': 'selu'}\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 [==============================] - 4s 2ms/step - loss: 2.0415 - accuracy: 0.3112\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.2385 - accuracy: 0.1921\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1375\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1352\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3029 - accuracy: 0.1276\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1261\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1265\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.1302\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3055 - accuracy: 0.1024\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3041 - accuracy: 0.1021\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3051 - accuracy: 0.1023\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3051 - accuracy: 0.1041\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3071 - accuracy: 0.0954\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3078 - accuracy: 0.0994\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3050 - accuracy: 0.1024\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-24\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'elu'}\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 [==============================] - 4s 2ms/step - loss: 2.1100 - accuracy: 0.4897\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3039 - accuracy: 0.2118\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3031 - accuracy: 0.1685\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1205\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3069 - accuracy: 0.1337\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3029 - accuracy: 0.1213\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1196\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1197\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1217\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.5178 - accuracy: 0.1322\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.1397\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1333\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3034 - accuracy: 0.1328\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1331\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3036 - accuracy: 0.1349\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: 2.3026 - accuracy: 0.1822\n",
            "--- Starting trial: run-25\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'relu'}\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 [==============================] - 4s 2ms/step - loss: 2.3811 - accuracy: 0.1858\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1562\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1416\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3037 - accuracy: 0.1234\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1080\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3035 - accuracy: 0.1054\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0970\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0960\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0980\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.0981\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0995\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0979\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0980\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0986\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: 2.3026 - accuracy: 0.1072\n",
            "--- Starting trial: run-26\n",
            "{'activation1': 'selu', 'activation2': 'selu', 'activation3': 'selu'}\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 [==============================] - 4s 2ms/step - loss: 2.3632 - accuracy: 0.2242\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3036 - accuracy: 0.1241\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3128 - accuracy: 0.1042\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0991\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0989\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0978\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0992\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0989\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0981\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0991\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: 2.3026 - accuracy: 0.1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rV_6APnW2Xqc",
        "outputId": "2a0bb667-6c95-4115-d081-0d792d237170",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        }
      },
      "source": [
        "actdict"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('elu', 'elu', 'elu'): 0.23849999904632568,\n",
              " ('elu', 'elu', 'relu'): 0.13950000703334808,\n",
              " ('elu', 'elu', 'selu'): 0.11240000277757645,\n",
              " ('elu', 'relu', 'elu'): 0.10000000149011612,\n",
              " ('elu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('elu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('elu', 'selu', 'elu'): 0.10000000149011612,\n",
              " ('elu', 'selu', 'relu'): 0.10050000250339508,\n",
              " ('elu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'elu', 'elu'): 0.16519999504089355,\n",
              " ('relu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'elu', 'selu'): 0.10000000149011612,\n",
              " ('relu', 'relu', 'elu'): 0.09939999878406525,\n",
              " ('relu', 'relu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'relu', 'selu'): 0.10029999911785126,\n",
              " ('relu', 'selu', 'elu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'relu'): 0.10000000149011612,\n",
              " ('relu', 'selu', 'selu'): 0.10000000149011612,\n",
              " ('selu', 'elu', 'elu'): 0.1850000023841858,\n",
              " ('selu', 'elu', 'relu'): 0.10000000149011612,\n",
              " ('selu', 'elu', 'selu'): 0.10000000149011612,\n",
              " ('selu', 'relu', 'elu'): 0.17820000648498535,\n",
              " ('selu', 'relu', 'relu'): 0.1673000007867813,\n",
              " ('selu', 'relu', 'selu'): 0.10000000149011612,\n",
              " ('selu', 'selu', 'elu'): 0.18219999969005585,\n",
              " ('selu', 'selu', 'relu'): 0.10719999670982361,\n",
              " ('selu', 'selu', 'selu'): 0.10000000149011612}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "058XbUlQ2Xqk",
        "outputId": "b6f01370-08f8-4f9d-d227-de1adeab2039",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(sorted(actdict.items(),key=lambda x: (x[1],x[0])))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(('relu', 'relu', 'elu'), 0.09939999878406525), (('elu', 'relu', 'elu'), 0.10000000149011612), (('elu', 'relu', 'relu'), 0.10000000149011612), (('elu', 'relu', 'selu'), 0.10000000149011612), (('elu', 'selu', 'elu'), 0.10000000149011612), (('elu', 'selu', 'selu'), 0.10000000149011612), (('relu', 'elu', 'relu'), 0.10000000149011612), (('relu', 'elu', 'selu'), 0.10000000149011612), (('relu', 'relu', 'relu'), 0.10000000149011612), (('relu', 'selu', 'elu'), 0.10000000149011612), (('relu', 'selu', 'relu'), 0.10000000149011612), (('relu', 'selu', 'selu'), 0.10000000149011612), (('selu', 'elu', 'relu'), 0.10000000149011612), (('selu', 'elu', 'selu'), 0.10000000149011612), (('selu', 'relu', 'selu'), 0.10000000149011612), (('selu', 'selu', 'selu'), 0.10000000149011612), (('relu', 'relu', 'selu'), 0.10029999911785126), (('elu', 'selu', 'relu'), 0.10050000250339508), (('selu', 'selu', 'relu'), 0.10719999670982361), (('elu', 'elu', 'selu'), 0.11240000277757645), (('elu', 'elu', 'relu'), 0.13950000703334808), (('relu', 'elu', 'elu'), 0.16519999504089355), (('selu', 'relu', 'relu'), 0.1673000007867813), (('selu', 'relu', 'elu'), 0.17820000648498535), (('selu', 'selu', 'elu'), 0.18219999969005585), (('selu', 'elu', 'elu'), 0.1850000023841858), (('elu', 'elu', 'elu'), 0.23849999904632568)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJLe22Ul2Xqq",
        "outputId": "420a272d-576c-4174-c9f9-dd2b3dee84a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "plotvar2 = sorted(actdict.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": 45,
      "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": "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": "MbzHQBX0Asbf"
      },
      "source": [
        "HP_FILTER1 = hp.HParam('filters1', hp.Discrete([32, 64, 128]))\n",
        "HP_FILTER2 = hp.HParam('filters2', 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_units').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": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ic9bmW1zG6Wu"
      },
      "source": [
        "def train_test_model3(hparams):\n",
        "    model3 = models.Sequential()\n",
        "    model3.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "    model3.add(layers.Conv2D(hparams[HP_FILTER1], kernel_size=(3,3), padding='same', activation='relu', input_shape=(28,28)))\n",
        "    model3.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    \n",
        "    model3.add(layers.Conv2D(hparams[HP_FILTER2], kernel_size=(3,3), padding='same', activation='relu'))\n",
        "    model3.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    \n",
        "    model3.add(layers.Flatten())\n",
        "    model3.add(layers.Dense(hparams[HP_NUM_UNITS1], activation='relu'))\n",
        "    \n",
        "    model3.add(layers.Dropout(0.50))\n",
        "    model3.add(layers.Dense(10))\n",
        "\n",
        "    model3.compile(\n",
        "        optimizer='adam',\n",
        "        loss='sparse_categorical_crossentropy',\n",
        "        metrics=['accuracy'],\n",
        "    )\n",
        "    model3.fit(x_train, y_train, epochs=15) # Run with 1 epoch to speed things up for demo purposes\n",
        "    _, accuracy = model3.evaluate(x_test, y_test)\n",
        "    return accuracy"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPJ475FjbNm3"
      },
      "source": [
        "def run3(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_model3(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)\n",
        "  return accuracy"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHnO4ZoYbdo-",
        "outputId": "bcbc8b20-d0dd-486e-8a40-1eafb4bb16cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "session_num = 0\n",
        "unitdict = {}\n",
        "for unit1 in HP_FILTER1.domain.values:\n",
        "    for unit2 in HP_FILTER2.domain.values:\n",
        "        for unit3 in HP_NUM_UNITS1.domain.values:\n",
        "                    hparams = {\n",
        "                        HP_FILTER1: unit1,\n",
        "                        HP_FILTER2: unit2,\n",
        "                        HP_NUM_UNITS1: unit3,\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",
        "                    unitdict[(unit1,unit2,unit3)] = run3('logs/hparam_tuning_acts/' + run_name, hparams)\n",
        "                    session_num += 1\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Starting trial: run-0\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3407 - accuracy: 0.1232\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3031 - accuracy: 0.1176\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3046 - accuracy: 0.1117\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.0993\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0977\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.1000\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3031 - accuracy: 0.1034\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1036\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3029 - accuracy: 0.1044\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1001\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0986\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-1\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3187 - accuracy: 0.1140\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3035 - accuracy: 0.1055\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3035 - accuracy: 0.0943\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.0980\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0941\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0955\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0951\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0967\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0964\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0966\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0954\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0957\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0947\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0966\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0972\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 2ms/step - loss: 2.3026 - accuracy: 0.0558\n",
            "--- Starting trial: run-2\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3788 - accuracy: 0.2148\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2319\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2339\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2334\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2351\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2323\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2354\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2323\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2366\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2318\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2338\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2334\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2339\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2368\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.2325\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: 2.3026 - accuracy: 0.4647\n",
            "--- Starting trial: run-3\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3687 - accuracy: 0.1747\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3029 - accuracy: 0.1348\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3037 - accuracy: 0.1340\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3034 - accuracy: 0.1114\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3034 - accuracy: 0.1020\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.0953\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0955\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0952\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0935\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0970\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0954\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0970\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0969\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0957\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.0963\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-4\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3900 - accuracy: 0.1957\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3044 - accuracy: 0.1743\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3048 - accuracy: 0.0940\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0989\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0992\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1011\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0974\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0987\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0975\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0973\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0984\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0994\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0973\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-5\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 5s 3ms/step - loss: 2.3217 - accuracy: 0.1260\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1228\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3031 - accuracy: 0.1216\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3036 - accuracy: 0.1172\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1172\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3028 - accuracy: 0.1126\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1155\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1133\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1137\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1110\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1129\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1137\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1130\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1121\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1137\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: 2.3026 - accuracy: 0.1010\n",
            "--- Starting trial: run-6\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3289 - accuracy: 0.1045\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3038 - accuracy: 0.1029\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3036 - accuracy: 0.1011\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.1018\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3055 - accuracy: 0.1002\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.1026\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0990\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1022\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1020\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1017\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1026\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3029 - accuracy: 0.0998\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1022\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-7\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 5s 2ms/step - loss: 2.3237 - accuracy: 0.1108\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3038 - accuracy: 0.1017\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3028 - accuracy: 0.0964\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0989\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3034 - accuracy: 0.0968\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3035 - accuracy: 0.1002\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3028 - accuracy: 0.0999\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1024\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3031 - accuracy: 0.1019\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1006\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1017\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-8\n",
            "{'filters1': 32, 'filters2': 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 [==============================] - 5s 3ms/step - loss: 2.3705 - accuracy: 0.1219\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1024\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0994\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1023\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1016\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1030\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3031 - accuracy: 0.1058\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.1094\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1030\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1018\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1005\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1009\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: 0.1002\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 2ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-9\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3681 - accuracy: 0.1097\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3055 - accuracy: 0.1026\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1018\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1010\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1024\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1015\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1030\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1021\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1031\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1014\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.1036\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3027 - accuracy: 0.0963\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0984\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-10\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3335 - accuracy: 0.1192\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3044 - accuracy: 0.1127\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1047\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1051\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1035\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3051 - accuracy: 0.0992\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1041\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1045\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3029 - accuracy: 0.1007\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0991\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: 0.0999\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0983\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.0988\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-11\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3202 - accuracy: 0.1040\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1034\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1047\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1042\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1031\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1034\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1028\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1032\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1031\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1040\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: 2.3026 - accuracy: 0.0804\n",
            "--- Starting trial: run-12\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 4s 2ms/step - loss: 2.3674 - accuracy: 0.1115\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3069 - accuracy: 0.0985\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.0992\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3032 - accuracy: 0.0975\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.0984\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.0991\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1031\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1010\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3025 - accuracy: 0.1009\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3039 - accuracy: 0.1005\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3041 - accuracy: 0.1009\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3030 - accuracy: 0.0999\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.1016\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3026 - accuracy: 0.1019\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: 2.3026 - accuracy: 0.1000\n",
            "--- Starting trial: run-13\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 5s 2ms/step - loss: 2.3946 - accuracy: 0.2177\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3040 - accuracy: 0.1257\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3025 - accuracy: 0.1236\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1194\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1135\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3027 - accuracy: 0.1110\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3036 - accuracy: 0.1085\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1074\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1077\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1061\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1089\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1091\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.1081\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1103\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_49_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: 2.3026 - accuracy: 0.2188WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_test_batch_end` time: 0.0023s). Check your callbacks.\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3026 - accuracy: 0.1676\n",
            "--- Starting trial: run-14\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 5s 3ms/step - loss: 2.3541 - accuracy: 0.1103\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3034 - accuracy: 0.1046\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1053\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1067\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1055\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1063\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1044\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1042\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1046\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1063\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1063\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1065\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1055\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1071\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: 0.0993\n",
            "--- Starting trial: run-15\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 5s 3ms/step - loss: 2.3443 - accuracy: 0.1079\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3066 - accuracy: 0.1041\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3043 - accuracy: 0.1005\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0983\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\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: 0.0982\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0979\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0976\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: 0.1005\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0972\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-16\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 5s 3ms/step - loss: 2.3431 - accuracy: 0.1032\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.1056\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0996\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0999\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0982\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1000\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0989\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: 2.3052 - accuracy: 0.1002\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.1013\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3054 - accuracy: 0.1016\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: 2.3026 - accuracy: 0.0980\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: 0.1006\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-17\n",
            "{'filters1': 64, 'filters2': 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 [==============================] - 6s 3ms/step - loss: 2.4148 - accuracy: 0.1155\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3036 - accuracy: 0.1143\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1053\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1039\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3027 - accuracy: 0.1010\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.0949\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.0949\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.1029\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1007\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1019\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1018\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1009\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1030\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1037\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1030\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-18\n",
            "{'filters1': 128, 'filters2': 32, 'num_units1': 256}\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 2ms/step - loss: 2.3294 - accuracy: 0.0982\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3037 - accuracy: 0.1028\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3037 - accuracy: 0.1035\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3028 - accuracy: 0.1023\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3032 - accuracy: 0.1023\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3043 - accuracy: 0.1001\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3037 - accuracy: 0.0993\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0986\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0992\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3025 - accuracy: 0.0973\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3032 - accuracy: 0.0978\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0997\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3027 - accuracy: 0.1016\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1011\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3026 - accuracy: 0.0998\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-19\n",
            "{'filters1': 128, 'filters2': 32, 'num_units1': 512}\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: 2.3732 - accuracy: 0.1104\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1027\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1038\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1038\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3041 - accuracy: 0.1035\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0959\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: 0.0940\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0969\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1067\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1074\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1081\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1066\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1064\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3036 - accuracy: 0.1048\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-20\n",
            "{'filters1': 128, 'filters2': 32, 'num_units1': 1024}\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: 2.3354 - accuracy: 0.1344\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3046 - accuracy: 0.1084\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1052\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1051\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1051\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1068\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: 0.1059\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: 0.1062\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1057\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1059\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1064\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1082\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1058\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-21\n",
            "{'filters1': 128, 'filters2': 64, 'num_units1': 256}\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: 2.3360 - accuracy: 0.0965\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3045 - accuracy: 0.0957\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.1009\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1010\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3086 - accuracy: 0.1000\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3050 - accuracy: 0.0986\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.0998\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1024\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1012\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1037\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: 2.3028 - accuracy: 0.1001\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3035 - accuracy: 0.1028\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3032 - accuracy: 0.1002\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3039 - accuracy: 0.0986\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-22\n",
            "{'filters1': 128, 'filters2': 64, 'num_units1': 512}\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: 2.3163 - accuracy: 0.1102\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3031 - accuracy: 0.0882\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.0915\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0922\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0919\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0936\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3047 - accuracy: 0.0957\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1005\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3030 - accuracy: 0.0994\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3027 - accuracy: 0.0998\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0995\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.0986\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: 2.3026 - accuracy: 0.0993\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0996\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-23\n",
            "{'filters1': 128, 'filters2': 64, 'num_units1': 1024}\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: 2.3315 - accuracy: 0.1394\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1097\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1073\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3064 - accuracy: 0.1067\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1044\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1050\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1046\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1063\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: 0.1050\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1047\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: 0.1047\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1053\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3026 - accuracy: 0.1055\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-24\n",
            "{'filters1': 128, 'filters2': 128, 'num_units1': 256}\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 [==============================] - 6s 3ms/step - loss: 2.5490 - accuracy: 0.1871\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3034 - accuracy: 0.1067\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1080\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.1072\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3040 - accuracy: 0.1054\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3034 - accuracy: 0.1037\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3044 - accuracy: 0.1013\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3030 - accuracy: 0.0991\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0985\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3038 - accuracy: 0.1003\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3030 - accuracy: 0.0957\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0955\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3030 - accuracy: 0.0985\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3030 - accuracy: 0.0982\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1002\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: 0.0849\n",
            "--- Starting trial: run-25\n",
            "{'filters1': 128, 'filters2': 128, 'num_units1': 512}\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 [==============================] - 6s 3ms/step - loss: 2.3479 - accuracy: 0.1124\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3053 - accuracy: 0.1094\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1060\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1067\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1075\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1059\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1066\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1082\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1066\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1076\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1094\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1066\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1079\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3026 - accuracy: 0.1080\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.1059\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: 0.1001\n",
            "--- Starting trial: run-26\n",
            "{'filters1': 128, 'filters2': 128, 'num_units1': 1024}\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 [==============================] - 7s 4ms/step - loss: 2.3358 - accuracy: 0.1195\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3026 - accuracy: 0.1066\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3028 - accuracy: 0.1078\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3028 - accuracy: 0.1090\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3025 - accuracy: 0.1072\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3028 - accuracy: 0.1072\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3048 - accuracy: 0.1039\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3048 - accuracy: 0.1025\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3090 - accuracy: 0.0998\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3026 - accuracy: 0.1003\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3026 - accuracy: 0.1010\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3028 - accuracy: 0.1018\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3028 - accuracy: 0.0993\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3026 - accuracy: 0.1008\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3028 - accuracy: 0.1009\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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2cxgAYD63gm",
        "outputId": "5cc31996-914d-47bf-cedd-59fe9d438497",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(f'Number of Sessions: {session_num}')"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Sessions: 27\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4srgS1Z61CT",
        "outputId": "6c3139c2-b5eb-457e-b1c9-7c6f9ff5dd9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        }
      },
      "source": [
        "unitdict"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{(32, 32, 256): 0.10000000149011612,\n",
              " (32, 32, 512): 0.055799998342990875,\n",
              " (32, 32, 1024): 0.46470001339912415,\n",
              " (32, 64, 256): 0.10000000149011612,\n",
              " (32, 64, 512): 0.10000000149011612,\n",
              " (32, 64, 1024): 0.10100000351667404,\n",
              " (32, 128, 256): 0.10000000149011612,\n",
              " (32, 128, 512): 0.10000000149011612,\n",
              " (32, 128, 1024): 0.10000000149011612,\n",
              " (64, 32, 256): 0.10000000149011612,\n",
              " (64, 32, 512): 0.10000000149011612,\n",
              " (64, 32, 1024): 0.0803999975323677,\n",
              " (64, 64, 256): 0.10000000149011612,\n",
              " (64, 64, 512): 0.16760000586509705,\n",
              " (64, 64, 1024): 0.09929999709129333,\n",
              " (64, 128, 256): 0.10000000149011612,\n",
              " (64, 128, 512): 0.10000000149011612,\n",
              " (64, 128, 1024): 0.10000000149011612,\n",
              " (128, 32, 256): 0.10000000149011612,\n",
              " (128, 32, 512): 0.10000000149011612,\n",
              " (128, 32, 1024): 0.10000000149011612,\n",
              " (128, 64, 256): 0.10000000149011612,\n",
              " (128, 64, 512): 0.10000000149011612,\n",
              " (128, 64, 1024): 0.10000000149011612,\n",
              " (128, 128, 256): 0.08489999920129776,\n",
              " (128, 128, 512): 0.10010000318288803,\n",
              " (128, 128, 1024): 0.10000000149011612}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3NuHzQD6_ga",
        "outputId": "08f564e6-7d50-446c-923f-4577765260e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(sorted(unitdict.items(),key=lambda x: (x[1],x[0])))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[((32, 32, 512), 0.055799998342990875), ((64, 32, 1024), 0.0803999975323677), ((128, 128, 256), 0.08489999920129776), ((64, 64, 1024), 0.09929999709129333), ((32, 32, 256), 0.10000000149011612), ((32, 64, 256), 0.10000000149011612), ((32, 64, 512), 0.10000000149011612), ((32, 128, 256), 0.10000000149011612), ((32, 128, 512), 0.10000000149011612), ((32, 128, 1024), 0.10000000149011612), ((64, 32, 256), 0.10000000149011612), ((64, 32, 512), 0.10000000149011612), ((64, 64, 256), 0.10000000149011612), ((64, 128, 256), 0.10000000149011612), ((64, 128, 512), 0.10000000149011612), ((64, 128, 1024), 0.10000000149011612), ((128, 32, 256), 0.10000000149011612), ((128, 32, 512), 0.10000000149011612), ((128, 32, 1024), 0.10000000149011612), ((128, 64, 256), 0.10000000149011612), ((128, 64, 512), 0.10000000149011612), ((128, 64, 1024), 0.10000000149011612), ((128, 128, 1024), 0.10000000149011612), ((128, 128, 512), 0.10010000318288803), ((32, 64, 1024), 0.10100000351667404), ((64, 64, 512), 0.16760000586509705), ((32, 32, 1024), 0.46470001339912415)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9jmXEm67GeA",
        "outputId": "233ac2fe-f89a-4cee-db66-2467ebc8b289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "plotvar3 = sorted(unitdict.items(),key=lambda x: (x[1],x[0]))\n",
        "plt.bar([str(x[0]) for x in plotvar3],[x[1] for x in plotvar3])\n",
        "f = plt.gcf()\n",
        "f.set_size_inches(25,25)\n",
        "f.autofmt_xdate()"
      ],
      "execution_count": 57,
      "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": "markdown",
      "metadata": {
        "id": "lcv3K_BXEZcU"
      },
      "source": [
        "# Re-Do Handwritten model with finetuned parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jm_drsdTEdp2"
      },
      "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": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hs25BEC4Edp_"
      },
      "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='selu', input_shape=(28,28)))\n",
        "model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "model.add(layers.Conv2D(32, 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(1024, activation='elu'))\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": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84ognoYfEdqF",
        "outputId": "1d11dd73-ab8f-49b7-cfd6-17714a221169",
        "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": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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",
            "1868/1875 [============================>.] - ETA: 0s - loss: 0.4126 - accuracy: 0.8538WARNING: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",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4122 - accuracy: 0.8539 - val_loss: 0.3480 - val_accuracy: 0.8776\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.3053 - accuracy: 0.8920 - val_loss: 0.3570 - val_accuracy: 0.8717\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2767 - accuracy: 0.9010 - val_loss: 0.2941 - val_accuracy: 0.8977\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2527 - accuracy: 0.9097 - val_loss: 0.3332 - val_accuracy: 0.8876\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2411 - accuracy: 0.9133 - val_loss: 0.3371 - val_accuracy: 0.8851\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2307 - accuracy: 0.9170 - val_loss: 0.3124 - val_accuracy: 0.8973\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2196 - accuracy: 0.9212 - val_loss: 0.2962 - val_accuracy: 0.9016\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2097 - accuracy: 0.9246 - val_loss: 0.3322 - val_accuracy: 0.8909\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2052 - accuracy: 0.9269 - val_loss: 0.2943 - val_accuracy: 0.9063\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1991 - accuracy: 0.9284 - val_loss: 0.3152 - val_accuracy: 0.9055\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1916 - accuracy: 0.9315 - val_loss: 0.3492 - val_accuracy: 0.8971\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1882 - accuracy: 0.9328 - val_loss: 0.3356 - val_accuracy: 0.9058\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1821 - accuracy: 0.9352 - val_loss: 0.3654 - val_accuracy: 0.8918\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1793 - accuracy: 0.9370 - val_loss: 0.3697 - val_accuracy: 0.9032\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1707 - accuracy: 0.9409 - val_loss: 0.3431 - val_accuracy: 0.9081\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwTR1r8gEdqO",
        "outputId": "6e076f28-a8c5-4128-85a4-45def239b7ee",
        "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": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 1s - loss: 0.5852 - accuracy: 0.9232\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4zm349LEdqT",
        "outputId": "ab27428e-d249-40ca-ad24-c1410bd183eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 953
        }
      },
      "source": [
        "keras.utils.plot_model(model)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jz0Y0clqAEry"
      },
      "source": [
        "# END"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CkN5kM5AFod"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}