EDL_7_5_Evo_CNN.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_7_5_Evo_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "FG2xwCBkZT7O"
      },
      "outputs": [],
      "source": [
        "#@title Install Packages\n",
        "!pip install livelossplot --quiet\n",
        "!pip install deap --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "FFxv9NclYSvb"
      },
      "outputs": [],
      "source": [
        "#@title Imports\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import numpy as np\n",
        "import math\n",
        "import time\n",
        "import random\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from livelossplot import PlotLossesKeras, PlotLosses\n",
        "from IPython.display import clear_output\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": "code",
      "execution_count": 35,
      "metadata": {
        "id": "8d0CpCJaYXk8"
      },
      "outputs": [],
      "source": [
        "#@title Load Data\n",
        "dataset = datasets.fashion_mnist\n",
        "(x_train, y_train), (x_test, y_test) = dataset.load_data()\n",
        "\n",
        "# normalize and reshape data\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype(\"float32\") / 255.0\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype(\"float32\") / 255.0\n",
        "\n",
        "x_train = x_train[:1000]\n",
        "y_train= y_train[:1000]\n",
        "x_test = x_test[:100]\n",
        "y_test= y_test[:100]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "mUZD3klmed57"
      },
      "outputs": [],
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "_2WS35U8YkJQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "45f86fbd-74b8-4bc4-e33a-3292644e1a52"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "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, x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Constants\n",
        "max_layers = 5\n",
        "max_neurons = 128\n",
        "min_neurons = 16\n",
        "max_kernel = 5\n",
        "min_kernel = 2\n",
        "max_pool = 3\n",
        "min_pool = 2\n",
        "\n",
        "CONV_LAYER = -1\n",
        "CONV_LAYER_LEN = 4\n",
        "POOLING_LAYER = -2\n",
        "POOLING_LAYER_LEN = 3\n",
        "BN_LAYER = -3\n",
        "BN_LAYER_LEN = 1\n",
        "DENSE_LAYER = -4\n",
        "DENSE_LAYER_LEN = 2"
      ],
      "metadata": {
        "id": "2oLtk5CwZ2Dg"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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_pooling_layer():\n",
        "  part = [POOLING_LAYER] \n",
        "  part.append(random.randint(min_pool, max_pool))\n",
        "  part.append(random.randint(min_pool, max_pool))\n",
        "  return part\n",
        "\n",
        "def generate_dense_layer():\n",
        "  part = [DENSE_LAYER] \n",
        "  part.append(generate_neurons())  \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 create_offspring():\n",
        "  ind = []\n",
        "  for i in range(max_layers):\n",
        "    if i==0: #first layer always convolutational\n",
        "      ind.extend(generate_conv_layer())\n",
        "    elif random.uniform(0,1)<.5:\n",
        "      #add convolution layer\n",
        "      ind.extend(generate_conv_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",
        "        #add max pooling layer\n",
        "        ind.extend(generate_pooling_layer())\n",
        "  ind.extend(generate_dense_layer())\n",
        "  return ind\n",
        "        \n",
        "individual = create_offspring()\n",
        "print(individual)\n"
      ],
      "metadata": {
        "id": "6pArO3IdXeH8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cfb54ba-3f50-40fc-f282-b810c2dd7212"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1, 96, 5, 5, -1, 107, 4, 3, -3, -2, 3, 2, -1, 97, 2, 4, -3, -2, 3, 3, -4, 76]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(individual):\n",
        "  model = models.Sequential()\n",
        "  il = len(individual)\n",
        "  i = 0\n",
        "  while i < il:\n",
        "    if individual[i] == CONV_LAYER: \n",
        "      n = individual[i+1]\n",
        "      k = (individual[i+2], individual[i+3])\n",
        "      i += CONV_LAYER_LEN\n",
        "      if i == 0: #first layer, add input shape      \n",
        "        model.add(layers.Conv2D(n, k, activation='relu', padding=\"same\", input_shape=(28, 28, 1)))      \n",
        "      else:\n",
        "        model.add(layers.Conv2D(n, k, activation='relu', padding=\"same\"))    \n",
        "    elif individual[i] == POOLING_LAYER: #add pooling layer\n",
        "      k = k = (individual[i+1], individual[i+2])\n",
        "      i += POOLING_LAYER_LEN\n",
        "      model.add(layers.MaxPooling2D(k, padding=\"same\"))      \n",
        "    elif individual[i] == BN_LAYER: #add batchnormal layer\n",
        "      model.add(layers.BatchNormalization())\n",
        "      i += 1      \n",
        "    elif individual[i] == DENSE_LAYER: #add dense layer\n",
        "      model.add(layers.Flatten())      \n",
        "      model.add(layers.Dense(individual[i+1], activation='relu'))\n",
        "      i += 2\n",
        "    else:\n",
        "      break\n",
        "  model.add(layers.Dense(10))\n",
        "  return model\n",
        "\n",
        "model = build_model(individual) \n"
      ],
      "metadata": {
        "id": "NUlDrjKqedTA"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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, POOLING_LAYER, POOLING_LAYER_LEN)\n",
        "  ind1, ind2 = swap_layers(ind1, ind2, BN_LAYER, BN_LAYER_LEN)\n",
        "  ind1, ind2 = swap_layers(ind1, ind2, DENSE_LAYER, DENSE_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)"
      ],
      "metadata": {
        "id": "6lqH6fMqlYJO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e37717e-cec5-4da1-f51c-fc1b81bbee48"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1, 64, 5, 5, -1, 78, 3, 5, -3, -2, 2, 3, -1, 87, 2, 4, -3, -2, 2, 2, -1, 22, 2, 3, -4, 82]\n",
            "[-1, 34, 3, 4, -1, 55, 2, 5, -4, 128]\n",
            "[-1, 55, 2, 5, -1, 78, 3, 5, -3, -2, 2, 3, -1, 34, 3, 4, -3, -2, 2, 2, -1, 22, 2, 3, -4, 128]\n",
            "[-1, 87, 2, 4, -1, 64, 5, 5, -4, 82]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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 == POOLING_LAYER and len(part)==POOLING_LAYER_LEN:\n",
        "    part[1] = random.randint(min_kernel, max_kernel)\n",
        "    part[2] = random.randint(min_kernel, max_kernel)\n",
        "  elif layer_type == DENSE_LAYER and len(part)==DENSE_LAYER_LEN:\n",
        "    part[1] = int(part[1] * random.uniform(.9, 1.1)) \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(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",
        "  if len(ind) > CONV_LAYER_LEN: #only mutate conv individuals\n",
        "    ind = mutate_layers(ind, CONV_LAYER, CONV_LAYER_LEN)\n",
        "    ind = mutate_layers(ind, DENSE_LAYER, DENSE_LAYER_LEN)\n",
        "    ind = mutate_layers(ind, POOLING_LAYER, POOLING_LAYER_LEN)\n",
        "  return ind,\n",
        "\n",
        "ind, = mutation(ind1)\n",
        "print(ind)"
      ],
      "metadata": {
        "id": "vn3-FyMQ2VHY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61a3760f-5cac-4f0a-d402-a996b23a2a8c"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1, 55, 2, 5, -1, 78, 3, 5, -3, -2, 2, 3, -1, 34, 3, 4, -3, -2, 2, 2, -1, 22, 2, 3, -4, 128]\n",
            "[-1, 53, 3, 5, -1, 74, 5, 3, -3, -2, 2, 4, -1, 32, 2, 2, -3, -2, 2, 3, -1, 21, 2, 4, -4, 117]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Build, Compile and Train the model\n",
        "def build_compile_train(ind, epochs=3):\n",
        "  model = build_model(ind) \n",
        "\n",
        "  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=epochs, \n",
        "                      validation_data=(x_test, y_test),\n",
        "                      callbacks=[PlotLossesKeras()],\n",
        "                      verbose=0)\n",
        "\n",
        "  model.summary()\n",
        "  model.evaluate(x_test, y_test)\n",
        "\n",
        "build_compile_train(ind)"
      ],
      "metadata": {
        "id": "Ca7sBT1ZhyOK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dde82156-5675-49e3-d8cb-5ab9afabc83e"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy\n",
            "\ttraining         \t (min:    0.641, max:    0.862, cur:    0.862)\n",
            "\tvalidation       \t (min:    0.090, max:    0.460, cur:    0.090)\n",
            "Loss\n",
            "\ttraining         \t (min:    0.411, max:    1.067, cur:    0.411)\n",
            "\tvalidation       \t (min:    2.143, max:    2.163, cur:    2.143)\n",
            "Model: \"sequential_149\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_397 (Conv2D)         (None, 28, 28, 53)        848       \n",
            "                                                                 \n",
            " conv2d_398 (Conv2D)         (None, 28, 28, 74)        58904     \n",
            "                                                                 \n",
            " batch_normalization_139 (Ba  (None, 28, 28, 74)       296       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_150 (MaxPooli  (None, 14, 7, 74)        0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " conv2d_399 (Conv2D)         (None, 14, 7, 32)         9504      \n",
            "                                                                 \n",
            " batch_normalization_140 (Ba  (None, 14, 7, 32)        128       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_151 (MaxPooli  (None, 7, 3, 32)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " conv2d_400 (Conv2D)         (None, 7, 3, 21)          5397      \n",
            "                                                                 \n",
            " flatten_137 (Flatten)       (None, 441)               0         \n",
            "                                                                 \n",
            " dense_281 (Dense)           (None, 117)               51714     \n",
            "                                                                 \n",
            " dense_282 (Dense)           (None, 10)                1180      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 127,971\n",
            "Trainable params: 127,759\n",
            "Non-trainable params: 212\n",
            "_________________________________________________________________\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 2.1433 - accuracy: 0.0900\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evolutionary CNN"
      ],
      "metadata": {
        "id": "QcthDR0Tg-j_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Setting up the Creator\n",
        "creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))\n",
        "creator.create(\"Individual\", list, fitness=creator.FitnessMin)"
      ],
      "metadata": {
        "id": "Tq-0NumDXGis",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9381924e-0986-48d8-f09f-e328897496c4"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/deap/creator.py:141: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
            "  RuntimeWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Create Individual and Population\n",
        "toolbox = base.Toolbox()\n",
        "toolbox.register(\"network\", create_offspring)\n",
        "toolbox.register(\"individual\", tools.initIterate, creator.Individual, toolbox.network)\n",
        "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n",
        "\n",
        "toolbox.register(\"select\", tools.selTournament, tournsize=5)"
      ],
      "metadata": {
        "id": "m9yV4JthXcyu"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Register Crossover and Mutation\n",
        "toolbox.register(\"mate\", crossover)\n",
        "toolbox.register(\"mutate\", mutation)"
      ],
      "metadata": {
        "id": "6TF1GaVYYN8t"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Register Evaluation\n",
        "def clamp(num, min_value, max_value):\n",
        "   return max(min(num, max_value), min_value)\n",
        "\n",
        "def compile_train(model):\n",
        "  model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "  model.fit(x_train, y_train, epochs=3,                     \n",
        "                    verbose=0)\n",
        "  return model\n",
        "\n",
        "def evaluate(individual):  \n",
        "  try:\n",
        "    model = build_model(individual)\n",
        "    model = compile_train(model)\n",
        "    print('.', end='')    \n",
        "    return 1/clamp(model.evaluate(x_test, y_test, verbose=0)[1], .00001, 1),\n",
        "  except:\n",
        "    return 1/.5,  \n",
        "\n",
        "toolbox.register(\"evaluate\", evaluate)   "
      ],
      "metadata": {
        "id": "CDNiOkwaYezR"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Optimize the Weights { run: \"auto\" }\n",
        "MU = 10 #@param {type:\"slider\", min:5, max:1000, step:5}\n",
        "NGEN = 5 #@param {type:\"slider\", min:5, max:100, step:1}\n",
        "RGEN = 1 #@param {type:\"slider\", min:1, max:100, 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)"
      ],
      "metadata": {
        "id": "axod3PrwZb6Z"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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': 1/l[\"max\"], 'max': 1/l[\"min\"]})\n",
        "  plotlosses.send()  # draw, update logs, etc\n",
        "   "
      ],
      "metadata": {
        "id": "AsWPp-UhZc_a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "outputId": "feda4076-7d6a-4adc-89ec-e9d18fbe69e4"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fitness\n",
            "\tmax              \t (min:    0.790, max:    0.810, cur:    0.810)\n",
            "\tmin              \t (min:    0.100, max:    0.720, cur:    0.720)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "build_compile_train(best, epochs=5)"
      ],
      "metadata": {
        "id": "xmEgZLexfFPz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f17e8d4e-2168-47e1-8464-f97658e13760"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy\n",
            "\ttraining         \t (min:    0.615, max:    0.917, cur:    0.917)\n",
            "\tvalidation       \t (min:    0.680, max:    0.770, cur:    0.730)\n",
            "Loss\n",
            "\ttraining         \t (min:    0.263, max:    1.258, cur:    0.263)\n",
            "\tvalidation       \t (min:    1.645, max:    2.041, cur:    1.645)\n",
            "Model: \"sequential_192\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_503 (Conv2D)         (None, 28, 28, 36)        468       \n",
            "                                                                 \n",
            " conv2d_504 (Conv2D)         (None, 28, 28, 127)       41275     \n",
            "                                                                 \n",
            " batch_normalization_184 (Ba  (None, 28, 28, 127)      508       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_195 (MaxPooli  (None, 10, 10, 127)      0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " flatten_179 (Flatten)       (None, 12700)             0         \n",
            "                                                                 \n",
            " dense_366 (Dense)           (None, 37)                469937    \n",
            "                                                                 \n",
            " dense_367 (Dense)           (None, 10)                380       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 512,568\n",
            "Trainable params: 512,314\n",
            "Non-trainable params: 254\n",
            "_________________________________________________________________\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 1.6447 - accuracy: 0.7300\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "EDL_7_5_Evo_CNN.ipynb",
      "provenance": [],
      "toc_visible": true,
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOHFoBECQnaeBn1cx5s+boP",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}