EDL_8_4_EvoAutoencoder.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/EvolutionaryDeepLearning/blob/main/EDL_8_4_EvoAutoencoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMGfjIfw7rmD"
      },
      "source": [
        "Imports and other setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLvPw4XcYfVW",
        "outputId": "402dc1ed-43a9-496f-fe46-ff694ed5df2c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |██                              | 10 kB 18.0 MB/s eta 0:00:01\r\u001b[K     |████                            | 20 kB 12.7 MB/s eta 0:00:01\r\u001b[K     |██████                          | 30 kB 6.6 MB/s eta 0:00:01\r\u001b[K     |████████▏                       | 40 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |██████████▏                     | 51 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |████████████▏                   | 61 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |██████████████▎                 | 71 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▎               | 81 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████▎             | 92 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▍           | 102 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▍         | 112 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▍       | 122 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▌     | 133 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 143 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 153 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 160 kB 4.8 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install livelossplot --quiet\n",
        "!pip install deap --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "pDtDvs-_7qN1",
        "outputId": "bb20a3e5-4f45-4a94-f425-7849a3548587"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import random\n",
        "\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras import layers, models, Input, Model\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "from IPython import display\n",
        "from IPython.display import clear_output\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from livelossplot import PlotLosses\n",
        "plt.gray()\n",
        "\n",
        "#DEAP\n",
        "from deap import algorithms\n",
        "from deap import base\n",
        "from deap import benchmarks\n",
        "from deap import creator\n",
        "from deap import tools"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0RfbGcU8q6D"
      },
      "source": [
        "CONSTANTS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ew02NVRk8gzm"
      },
      "source": [
        "Load Fashion Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVt0CcAj8jii",
        "outputId": "77696cbc-964d-4dd4-8385-0843e48d5e9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# load dataset\n",
        "(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "# split dataset\n",
        "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype(\"float32\") / 255.0\n",
        "test_images = test_images.reshape(test_images.shape[0], 28, 28, 1).astype(\"float32\") / 255.0\n",
        "\n",
        "# reduce dataset for demonstration\n",
        "#train_images = train_images[1000:]\n",
        "#test_images = test_images[100:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4a6JK-amsHi"
      },
      "source": [
        "Setup class names and labels for visualization, not training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m_tNwyn0mxJr"
      },
      "outputs": [],
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoUkmn5Z9XpW"
      },
      "source": [
        "Plot some images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "n5hq3JD79aQv",
        "outputId": "14fcc407-e360-49ae-f0f3-c098fb110d56"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import math\n",
        "\n",
        "def plot_data(num_images, images, labels):\n",
        "  grid = math.ceil(math.sqrt(num_images))\n",
        "  plt.figure(figsize=(grid*2,grid*2))\n",
        "  for i in range(num_images):\n",
        "      plt.subplot(grid,grid,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)     \n",
        "      plt.imshow(images[i].reshape(28,28))\n",
        "      plt.xlabel(class_names[labels[i]])      \n",
        "  plt.show()\n",
        "\n",
        "plot_data(25, train_images, train_labels)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8odmpoCwtIO"
      },
      "source": [
        "STAGE 1: Auto-encoders"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRQ6OzSNpLJx"
      },
      "source": [
        "Build the Encoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "efscot4HpN3i"
      },
      "outputs": [],
      "source": [
        "# input layer\n",
        "input_layer = Input(shape=(28, 28, 1))\n",
        "\n",
        "# encoding architecture\n",
        "encoded_layer1 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(input_layer)\n",
        "encoded_layer1 = layers.MaxPool2D( (2, 2), padding='same')(encoded_layer1)\n",
        "encoded_layer2 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(encoded_layer1)\n",
        "encoded_layer2 = layers.MaxPool2D( (2, 2), padding='same')(encoded_layer2)\n",
        "encoded_layer3 = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(encoded_layer2)\n",
        "latent_view    = layers.MaxPool2D( (2, 2), padding='same')(encoded_layer3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4QFajN_qIKP"
      },
      "source": [
        "Build the Decoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PXuiAj9IqKbb"
      },
      "outputs": [],
      "source": [
        "#decoding architecture\n",
        "decoded_layer1 = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(latent_view)\n",
        "decoded_layer1 = layers.UpSampling2D((2, 2))(decoded_layer1)\n",
        "decoded_layer2 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(decoded_layer1)\n",
        "decoded_layer2 = layers.UpSampling2D((2, 2))(decoded_layer2)\n",
        "decoded_layer3 = layers.Conv2D(64, (3, 3), activation='relu')(decoded_layer2)\n",
        "decoded_layer3 = layers.UpSampling2D((2, 2))(decoded_layer3)\n",
        "#output layer\n",
        "output_layer   = layers.Conv2D(1, (3, 3), padding='same')(decoded_layer3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ks0eEtdbqas0"
      },
      "source": [
        "Build the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8eV6oxGlqdWp",
        "outputId": "fdbc946b-a9a9-466b-efab-7a48605258f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 28, 28, 64)        640       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 32)        18464     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 7, 7, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 7, 7, 16)          4624      \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 4, 4, 16)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 4, 4, 16)          2320      \n",
            "                                                                 \n",
            " up_sampling2d (UpSampling2D  (None, 8, 8, 16)         0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 8, 8, 32)          4640      \n",
            "                                                                 \n",
            " up_sampling2d_1 (UpSampling  (None, 16, 16, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 14, 14, 64)        18496     \n",
            "                                                                 \n",
            " up_sampling2d_2 (UpSampling  (None, 28, 28, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 28, 28, 1)         577       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 49,761\n",
            "Trainable params: 49,761\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# compile the model\n",
        "model = Model(input_layer, output_layer)\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "model.summary()\n",
        "plot_model(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0NDsxe9LnQX-"
      },
      "outputs": [],
      "source": [
        "history_loss = []\n",
        "history_val_loss = []\n",
        "\n",
        "def add_history(history):\n",
        "  history_loss.append(history.history[\"loss\"])\n",
        "  history_val_loss.append(history.history[\"val_loss\"])\n",
        "\n",
        "def reset_history():\n",
        "  global history_loss\n",
        "  global history_val_loss\n",
        "  history_loss = []\n",
        "  history_val_loss = []\n",
        "  return []\n",
        "\n",
        "def plot_results(num_images, images, labels, history):\n",
        "  add_history(history)\n",
        "  grid = math.ceil(math.sqrt(num_images))\n",
        "  plt.figure(figsize=(grid*2,grid*2))\n",
        "  for i in range(num_images):\n",
        "      plt.subplot(grid,grid,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)     \n",
        "      plt.imshow(images[i].reshape(28,28))\n",
        "      plt.xlabel(class_names[labels[i]])      \n",
        "  plt.show()\n",
        "  plt.plot(history_loss, label='loss')\n",
        "  plt.plot(history_val_loss, label='val_loss')\n",
        "  plt.legend()\n",
        "  plt.show()  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "id": "Km0n4knukEc6",
        "outputId": "d17dd2fb-8f3a-4b4c-c999-1c79b80de6b0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "EPOCHS = 3\n",
        "history = reset_history()\n",
        "\n",
        "for i in range(EPOCHS):\n",
        "  history = model.fit(train_images, train_images, epochs=1, batch_size=2048, validation_data=(test_images, test_images))\n",
        "  pred_images = model.predict(test_images[:25])\n",
        "  clear_output()\n",
        "  plot_results(25, pred_images[:25], test_labels[:25], history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "klAM5onsvSx4"
      },
      "outputs": [],
      "source": [
        "#@title Constants\n",
        "max_layers = 10\n",
        "max_neurons = 128\n",
        "min_neurons = 16\n",
        "max_kernel = 3\n",
        "min_kernel = 3\n",
        "max_pool = 2\n",
        "min_pool = 2\n",
        "\n",
        "CONV_LAYER = -1\n",
        "CONV_LAYER_LEN = 4\n",
        "BN_LAYER = -3\n",
        "BN_LAYER_LEN = 1\n",
        "DROPOUT_LAYER = -4\n",
        "DROPOUT_LAYER_LEN = 2\n",
        "UPCONV_LAYER = -2\n",
        "UPCONV_LAYER_LEN = 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRlGpaMYvZK8",
        "outputId": "a683eb5e-ba1f-4bb1-cc55-a1dee746022d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1, 77, 3, 3, -1, 120, 3, 3, -3, -1, 65, 3, 3, -4, 0.1457169739400423, -1, 125, 3, 3, -4, 0.4112446879141588, -1, 20, 3, 3, -3, -4, 0.4427076677284294, -1, 120, 3, 3, -3, -4, 0.32406907420755815, -2, 89, 3, 3, -2, 126, 3, 3, -3, -4, 0.38140167072851344, -2, 47, 3, 3, -2, 91, 3, 3, -2, 108, 3, 3, -3, -2, 53, 3, 3, -4, 0.37112359234255]\n"
          ]
        }
      ],
      "source": [
        "#@title Encoding scheme\n",
        "def generate_neurons():\n",
        "  return random.randint(min_neurons, max_neurons)\n",
        "\n",
        "def generate_kernel():\n",
        "  part = []\n",
        "  part.append(random.randint(min_kernel, max_kernel))\n",
        "  part.append(random.randint(min_kernel, max_kernel))\n",
        "  return part\n",
        "\n",
        "def generate_bn_layer():\n",
        "  part = [BN_LAYER] \n",
        "  return part\n",
        "\n",
        "def generate_dropout_layer():\n",
        "  part = [DROPOUT_LAYER] \n",
        "  part.append(random.uniform(0,.5))  \n",
        "  return part\n",
        "\n",
        "def generate_conv_layer():\n",
        "  part = [CONV_LAYER] \n",
        "  part.append(generate_neurons())\n",
        "  part.extend(generate_kernel())  \n",
        "  return part\n",
        "\n",
        "def generate_upconv_layer():\n",
        "  part = [UPCONV_LAYER] \n",
        "  part.append(generate_neurons())\n",
        "  part.extend(generate_kernel())  \n",
        "  return part\n",
        "\n",
        "def create_offspring():\n",
        "  ind = []\n",
        "  layers = 0\n",
        "  for i in range(max_layers):\n",
        "    if i==0: #first layer always convolutational\n",
        "      ind.extend(generate_conv_layer())      \n",
        "      layers += 1\n",
        "    elif random.uniform(0,1)<.5:\n",
        "      #add convolution layer\n",
        "      ind.extend(generate_conv_layer())\n",
        "      layers += 1\n",
        "      if random.uniform(0,1)<.5:\n",
        "        #add batchnormalization\n",
        "        ind.extend(generate_bn_layer())\n",
        "      if random.uniform(0,1) < .5:\n",
        "        ind.extend(generate_dropout_layer()) \n",
        "  for i in range(layers):\n",
        "    ind.extend(generate_upconv_layer())\n",
        "    if random.uniform(0,1)<.5:\n",
        "      #add batchnormalization\n",
        "      ind.extend(generate_bn_layer())\n",
        "    if random.uniform(0,1) < .5:\n",
        "      ind.extend(generate_dropout_layer())\n",
        "  return ind\n",
        "        \n",
        "individual = create_offspring()\n",
        "print(individual)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLU1Y1650PEe",
        "outputId": "e869aba0-04fc-421f-ee02-9b518170c4c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 28, 28, 77)        770       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 14, 14, 77)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 14, 14, 120)       83280     \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 7, 7, 120)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 7, 7, 120)        480       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 7, 7, 65)          70265     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 7, 7, 65)          0         \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 7, 7, 125)         73250     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 7, 7, 125)         0         \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 7, 7, 20)          22520     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 7, 7, 20)         80        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 7, 7, 20)          0         \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 7, 7, 120)         21720     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 7, 7, 120)        480       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 7, 7, 120)         0         \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 7, 7, 89)          96209     \n",
            "                                                                 \n",
            " up_sampling2d_3 (UpSampling  (None, 14, 14, 89)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_14 (Conv2D)          (None, 14, 14, 126)       101052    \n",
            "                                                                 \n",
            " up_sampling2d_4 (UpSampling  (None, 28, 28, 126)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_15 (Conv2D)          (None, 28, 28, 1)         1135      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 471,241\n",
            "Trainable params: 470,721\n",
            "Non-trainable params: 520\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def padding(gene):\n",
        "  return \"same\" if gene == 1 else \"valid\"\n",
        "\n",
        "def build_model(individual):\n",
        "  input_layer = Input(shape=(28, 28, 1))  \n",
        "  il = len(individual)\n",
        "  i = 0\n",
        "  x = input_layer\n",
        "  while i < il:    \n",
        "    if individual[i] == CONV_LAYER:      \n",
        "      pad=\"same\" \n",
        "      n = individual[i+1]\n",
        "      k = (individual[i+2], individual[i+3])\n",
        "      i += CONV_LAYER_LEN        \n",
        "      x = layers.Conv2D(n, k, activation='relu', padding=pad)(x) \n",
        "      if x.shape[1] > 7:\n",
        "        x = layers.MaxPool2D( (2, 2), padding='same')(x)\n",
        "    elif individual[i] == BN_LAYER: #add batchnormal layer\n",
        "      x = layers.BatchNormalization()(x)\n",
        "      i += BN_LAYER_LEN      \n",
        "    elif individual[i] == DROPOUT_LAYER: #add dropout layer            \n",
        "      x = layers.Dropout(individual[i+1])(x) \n",
        "      i += DROPOUT_LAYER_LEN\n",
        "    elif individual[i] == UPCONV_LAYER:\n",
        "      pad=\"same\"\n",
        "      n = individual[i+1]\n",
        "      k = (individual[i+2], individual[i+3])        \n",
        "      x = layers.Conv2D(n, k, activation='relu', padding=pad)(x)   \n",
        "      x = layers.UpSampling2D((2, 2))(x)   \n",
        "      i += CONV_LAYER_LEN   \n",
        "      if x.shape[1] == (28):\n",
        "        break #model is complete\n",
        "    else:\n",
        "      break\n",
        "  if x.shape[1] == 14:\n",
        "    x = layers.UpSampling2D((2, 2))(x)\n",
        "    \n",
        "  output_layer = layers.Conv2D(1, (3, 3), padding='same')(x)\n",
        "  model = Model(input_layer, output_layer)\n",
        "  model.compile(optimizer='adam', loss='mse')\n",
        "  return model\n",
        "\n",
        "model = build_model(individual) \n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MTbYKpYBBTz",
        "outputId": "db63afbc-1f25-48b3-c40a-4c992b6ac23c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_16 (Conv2D)          (None, 28, 28, 57)        570       \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 14, 14, 57)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_17 (Conv2D)          (None, 14, 14, 34)        17476     \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 7, 7, 34)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 7, 7, 34)         136       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 7, 7, 34)          0         \n",
            "                                                                 \n",
            " conv2d_18 (Conv2D)          (None, 7, 7, 61)          18727     \n",
            "                                                                 \n",
            " conv2d_19 (Conv2D)          (None, 7, 7, 43)          23650     \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 7, 7, 43)          0         \n",
            "                                                                 \n",
            " conv2d_20 (Conv2D)          (None, 7, 7, 33)          12804     \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 7, 7, 33)         132       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 7, 7, 33)          0         \n",
            "                                                                 \n",
            " conv2d_21 (Conv2D)          (None, 7, 7, 88)          26224     \n",
            "                                                                 \n",
            " up_sampling2d_5 (UpSampling  (None, 14, 14, 88)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 14, 14, 88)       352       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_22 (Conv2D)          (None, 14, 14, 26)        20618     \n",
            "                                                                 \n",
            " up_sampling2d_6 (UpSampling  (None, 28, 28, 26)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_23 (Conv2D)          (None, 28, 28, 1)         235       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 120,924\n",
            "Trainable params: 120,614\n",
            "Non-trainable params: 310\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "individual = create_offspring() \n",
        "model = build_model(individual)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIQD2pN7fID0"
      },
      "source": [
        "# Creating Mate/Mutation Operators"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkiLVueJWBdx",
        "outputId": "ce9d1d07-f5c6-4569-d466-00229b129742"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1, 93, 3, 3, -1, 67, 3, 3, -3, -4, 0.027071642365128212, -1, 77, 3, 3, -2, 21, 3, 3, -2, 17, 3, 3, -2, 52, 3, 3, -3]\n",
            "[-1, 109, 3, 3, -1, 40, 3, 3, -1, 23, 3, 3, -4, 0.44171814300243506, -1, 17, 3, 3, -3, -4, 0.4532032266493803, -1, 102, 3, 3, -2, 95, 3, 3, -3, -2, 33, 3, 3, -3, -2, 41, 3, 3, -4, 0.4287618783933327, -2, 111, 3, 3, -3, -2, 111, 3, 3, -3, -4, 0.33036928763087736]\n",
            "[-1, 109, 3, 3, -1, 17, 3, 3, -3, -4, 0.4532032266493803, -1, 102, 3, 3, -2, 95, 3, 3, -2, 41, 3, 3, -2, 111, 3, 3, -3]\n",
            "[-1, 93, 3, 3, -1, 40, 3, 3, -1, 23, 3, 3, -4, 0.44171814300243506, -1, 67, 3, 3, -3, -4, 0.027071642365128212, -1, 77, 3, 3, -2, 21, 3, 3, -3, -2, 33, 3, 3, -3, -2, 17, 3, 3, -4, 0.4287618783933327, -2, 111, 3, 3, -3, -2, 52, 3, 3, -3, -4, 0.33036928763087736]\n",
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_4 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_24 (Conv2D)          (None, 28, 28, 109)       1090      \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 14, 14, 109)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_25 (Conv2D)          (None, 14, 14, 17)        16694     \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 7, 7, 17)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 7, 7, 17)         68        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 7, 7, 17)          0         \n",
            "                                                                 \n",
            " conv2d_26 (Conv2D)          (None, 7, 7, 102)         15708     \n",
            "                                                                 \n",
            " conv2d_27 (Conv2D)          (None, 7, 7, 95)          87305     \n",
            "                                                                 \n",
            " up_sampling2d_7 (UpSampling  (None, 14, 14, 95)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_28 (Conv2D)          (None, 14, 14, 41)        35096     \n",
            "                                                                 \n",
            " up_sampling2d_8 (UpSampling  (None, 28, 28, 41)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_29 (Conv2D)          (None, 28, 28, 1)         370       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 156,331\n",
            "Trainable params: 156,297\n",
            "Non-trainable params: 34\n",
            "_________________________________________________________________\n",
            "Model: \"model_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_30 (Conv2D)          (None, 28, 28, 93)        930       \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 14, 14, 93)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_31 (Conv2D)          (None, 14, 14, 40)        33520     \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 7, 7, 40)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_32 (Conv2D)          (None, 7, 7, 23)          8303      \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 7, 7, 23)          0         \n",
            "                                                                 \n",
            " conv2d_33 (Conv2D)          (None, 7, 7, 67)          13936     \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 7, 7, 67)         268       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 7, 7, 67)          0         \n",
            "                                                                 \n",
            " conv2d_34 (Conv2D)          (None, 7, 7, 77)          46508     \n",
            "                                                                 \n",
            " conv2d_35 (Conv2D)          (None, 7, 7, 21)          14574     \n",
            "                                                                 \n",
            " up_sampling2d_9 (UpSampling  (None, 14, 14, 21)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_8 (Batc  (None, 14, 14, 21)       84        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_36 (Conv2D)          (None, 14, 14, 33)        6270      \n",
            "                                                                 \n",
            " up_sampling2d_10 (UpSamplin  (None, 28, 28, 33)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_37 (Conv2D)          (None, 28, 28, 1)         298       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 124,691\n",
            "Trainable params: 124,515\n",
            "Non-trainable params: 176\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#@title Start of Mate/Mutation Operators\n",
        "def get_layers(ind, layer_type):\n",
        "  return [a for a in range(len(ind)) if ind[a] == layer_type]\n",
        "\n",
        "def swap(ind1, iv1, ind2, iv2, ll):\n",
        "  ch1 = ind1[iv1:iv1+ll]\n",
        "  ch2 = ind2[iv2:iv2+ll] \n",
        "  ind1[iv1:iv1+ll] = ch2\n",
        "  ind2[iv2:iv2+ll] = ch1\n",
        "  return ind1, ind2\n",
        "\n",
        "def swap_layers(ind1, ind2, layer_type, layer_len):\n",
        "  c1, c2 = get_layers(ind1, layer_type), get_layers(ind2, layer_type) \n",
        "  min_c = min(len(c1), len(c2))\n",
        "  for i in range(min_c):\n",
        "    if random.random() < 1:\n",
        "      i1 = random.randint(0, len(c1)-1)\n",
        "      i2 = random.randint(0, len(c2)-1)      \n",
        "      iv1 = c1.pop(i1)\n",
        "      iv2 = c2.pop(i2)    \n",
        "      ind1, ind2 = swap(ind1, iv1, ind2, iv2, layer_len) \n",
        "  return ind1, ind2 \n",
        "\n",
        "def crossover(ind1, ind2):  \n",
        "  ind1, ind2 = swap_layers(ind1, ind2, CONV_LAYER, CONV_LAYER_LEN)\n",
        "  ind1, ind2 = swap_layers(ind1, ind2, UPCONV_LAYER, UPCONV_LAYER_LEN)\n",
        "  ind1, ind2 = swap_layers(ind1, ind2, BN_LAYER, BN_LAYER_LEN)\n",
        "  ind1, ind2 = swap_layers(ind1, ind2, DROPOUT_LAYER, DROPOUT_LAYER_LEN)\n",
        "  return ind1, ind2  \n",
        "\n",
        "ind1 = create_offspring()\n",
        "ind2 = create_offspring()\n",
        "print(ind1)\n",
        "print(ind2)\n",
        "\n",
        "ind1, ind2 = crossover(ind1, ind2)\n",
        "print(ind1)\n",
        "print(ind2)\n",
        "\n",
        "model = build_model(ind1)\n",
        "model.summary()\n",
        "model = build_model(ind2)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sImATzc4Wjp8",
        "outputId": "138e4e24-0f41-4b0d-ba8f-78d8bd57e803"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1, 109, 3, 3, -1, 17, 3, 3, -3, -4, 0.4532032266493803, -1, 102, 3, 3, -2, 95, 3, 3, -2, 41, 3, 3, -2, 111, 3, 3, -3]\n",
            "[-1, 114, 3, 3, -1, 16, 3, 3, -3, -4, 0.3591074600780886, -1, 102, 3, 3, -2, 3, 3, 3, -2, 3, 3, 3, -2, 3, 3, 3, -3]\n",
            "Model: \"model_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_6 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_38 (Conv2D)          (None, 28, 28, 114)       1140      \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 14, 14, 114)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_39 (Conv2D)          (None, 14, 14, 16)        16432     \n",
            "                                                                 \n",
            " max_pooling2d_12 (MaxPoolin  (None, 7, 7, 16)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " batch_normalization_9 (Batc  (None, 7, 7, 16)         64        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 7, 7, 16)          0         \n",
            "                                                                 \n",
            " conv2d_40 (Conv2D)          (None, 7, 7, 102)         14790     \n",
            "                                                                 \n",
            " conv2d_41 (Conv2D)          (None, 7, 7, 3)           2757      \n",
            "                                                                 \n",
            " up_sampling2d_11 (UpSamplin  (None, 14, 14, 3)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_42 (Conv2D)          (None, 14, 14, 3)         84        \n",
            "                                                                 \n",
            " up_sampling2d_12 (UpSamplin  (None, 28, 28, 3)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_43 (Conv2D)          (None, 28, 28, 1)         28        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 35,295\n",
            "Trainable params: 35,263\n",
            "Non-trainable params: 32\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#@title Mutation\n",
        "def mutate(part, layer_type):\n",
        "  if layer_type == CONV_LAYER and len(part)==CONV_LAYER_LEN:\n",
        "    part[1] = int(part[1] * random.uniform(.9, 1.1))\n",
        "    part[2] = random.randint(min_kernel, max_kernel)\n",
        "    part[3] = random.randint(min_kernel, max_kernel)\n",
        "  elif layer_type == UPCONV_LAYER and len(part)==UPCONV_LAYER_LEN:\n",
        "    part[1] = random.randint(min_kernel, max_kernel)\n",
        "    part[2] = random.randint(min_kernel, max_kernel)\n",
        "  elif layer_type == DROPOUT_LAYER and len(part)==DROPOUT_LAYER_LEN:\n",
        "    part[1] = random.uniform(0, .5) \n",
        "  else:\n",
        "    error = f\"mutate ERROR {part}\"    \n",
        "    raise Exception(error) \n",
        "  return part\n",
        "\n",
        "def mutate_layers(ind, layer_type, layer_len):\n",
        "  layers = get_layers(ind1, layer_type)\n",
        "  for layer in layers:\n",
        "    if random.random() < 1:\n",
        "      try:\n",
        "        ind[layer:layer+layer_len] = mutate(\n",
        "            ind[layer:layer+layer_len], layer_type) \n",
        "      except:\n",
        "        print(layers)\n",
        "  return ind      \n",
        "\n",
        "print(ind1)\n",
        "\n",
        "def mutation(ind):    \n",
        "  ind = mutate_layers(ind, CONV_LAYER, CONV_LAYER_LEN)\n",
        "  ind = mutate_layers(ind, DROPOUT_LAYER, DROPOUT_LAYER_LEN)    \n",
        "  ind = mutate_layers(ind, UPCONV_LAYER, UPCONV_LAYER_LEN)\n",
        "  return ind,\n",
        "\n",
        "ind, = mutation(ind1)\n",
        "print(ind)\n",
        "model = build_model(ind1)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P37ruhkcYy7x"
      },
      "outputs": [],
      "source": [
        "#@title Setting up the Creator\n",
        "creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))\n",
        "creator.create(\"Individual\", list, fitness=creator.FitnessMin)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-rWTwio4Y7tK"
      },
      "outputs": [],
      "source": [
        "#@title Create Individual and Population\n",
        "toolbox = base.Toolbox()\n",
        "toolbox.register(\"autoencoder\", create_offspring)\n",
        "toolbox.register(\"individual\", tools.initIterate, creator.Individual, toolbox.autoencoder)\n",
        "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n",
        "\n",
        "toolbox.register(\"select\", tools.selTournament, tournsize=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1YXkl6VGZBHE"
      },
      "outputs": [],
      "source": [
        "#@title Register Crossover and Mutation\n",
        "toolbox.register(\"mate\", crossover)\n",
        "toolbox.register(\"mutate\", mutation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "znaZtRebZKxJ"
      },
      "outputs": [],
      "source": [
        "#@title Register Evaluation\n",
        "def clamp(num, min_value, max_value):\n",
        "   return max(min(num, max_value), min_value)\n",
        "\n",
        "def train(model):  \n",
        "  history = model.fit(train_images, train_images, epochs=3,\n",
        "                      batch_size=2048, validation_data=(test_images, test_images),\n",
        "                      verbose=0)\n",
        "  return model, history\n",
        "\n",
        "fits = []\n",
        "\n",
        "def evaluate(individual): \n",
        "  global fits\n",
        "  try:\n",
        "    model = build_model(individual)\n",
        "    model, history = train(model)        \n",
        "    fitness = history.history[\"val_loss\"]\n",
        "    fits.append(fitness)\n",
        "    print(\".\", end='')    \n",
        "    return clamp(fitness, 0, np.nanmax(fits)),\n",
        "  except:\n",
        "    return np.nanmax(fits),  \n",
        "\n",
        "toolbox.register(\"evaluate\", evaluate)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tkfBghYPZlSp"
      },
      "outputs": [],
      "source": [
        "#@title Optimize the Weights { run: \"auto\" }\n",
        "MU = 100 #@param {type:\"slider\", min:5, max:100, step:1}\n",
        "NGEN = 1 #@param {type:\"slider\", min:1, max:10, step:1}\n",
        "RGEN = 1 #@param {type:\"slider\", min:1, max:5, step:1}\n",
        "CXPB = .6\n",
        "MUTPB = .3\n",
        "\n",
        "random.seed(64)\n",
        "\n",
        "pop = toolbox.population(n=MU)\n",
        "hof = tools.HallOfFame(1)\n",
        "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
        "stats.register(\"avg\", np.mean)\n",
        "stats.register(\"std\", np.std)\n",
        "stats.register(\"min\", np.min)\n",
        "stats.register(\"max\", np.max)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YeaIXkIZqGp",
        "outputId": "cfc643ab-6a8d-41d6-a530-f52e367373bd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fitness\n",
            "\tmax              \t (min:    0.562, max:    0.562, cur:    0.562)\n",
            "\tmin              \t (min:    0.132, max:    0.132, cur:    0.132)\n"
          ]
        }
      ],
      "source": [
        "best = None\n",
        "groups = { \"fitness\" : {\"min\", \"max\"}}\n",
        "plotlosses = PlotLosses(groups=groups)\n",
        "\n",
        "for g in range(NGEN):\n",
        "  pop, logbook = algorithms.eaSimple(pop, toolbox, \n",
        "            cxpb=CXPB, mutpb=MUTPB, ngen=RGEN, stats=stats, halloffame=hof, verbose=False)\n",
        "  best = hof[0] \n",
        "  \n",
        "  print(f\"Gen ({(g+1)*RGEN})\")      \n",
        "  for l in logbook:\n",
        "    plotlosses.update({'min': l[\"min\"], 'max': l[\"max\"]})\n",
        "  plotlosses.send()  # draw, update logs, etc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rfJpnbo6vq03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "outputId": "019b4211-a890-4d8c-cdd4-26c67021d77b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "model = build_model(best)\n",
        "\n",
        "EPOCHS = 10\n",
        "history = reset_history()\n",
        "\n",
        "for i in range(EPOCHS):\n",
        "  history = model.fit(train_images, train_images, epochs=1, batch_size=2048, validation_data=(test_images, test_images))\n",
        "  pred_images = model.predict(test_images[:25])\n",
        "  clear_output()\n",
        "  plot_results(25, pred_images[:25], test_labels[:25], history)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "EDL_8_4_EvoAutoencoder.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNRL2UUiIAOH2pXmIOnVkZL",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}