_mnist.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xD0S-aVqbh54"
      },
      "source": [
        "# **1. Requrired libraries and parameters**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s55XzYZLbg7P",
        "outputId": "35ce66de-67de-4239-f825-0519f40ddd05"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting colorama\n",
            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
            "Installing collected packages: colorama\n",
            "Successfully installed colorama-0.4.4\n"
          ]
        }
      ],
      "source": [
        "#  required lib for colorful plot\n",
        "! pip install colorama\n",
        "import colorama\n",
        "from colorama import Fore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFA9ukDxZoN3"
      },
      "source": [
        "✅ **This code needs utils.py file  to be executed**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ZMPZdqmaxwS"
      },
      "source": [
        "✅ **Enable GPU**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eX6mVxxvakg4",
        "outputId": "d1f85553-db16-4b00-82e5-b79090d112e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mDevice 0: b'Tesla K80', Memory : (100.00% free): 11996954624(total), 11996954624 (free), 0 (used)\n"
          ]
        }
      ],
      "source": [
        "#!!!!!!!!!!!!!\n",
        "  # If GPU is not activated, The following code will return an error\n",
        "# !!!!!!!!!!!!\n",
        "import nvidia_smi\n",
        "nvidia_smi.nvmlInit()\n",
        "deviceCount = nvidia_smi.nvmlDeviceGetCount()\n",
        "for i in range(deviceCount):\n",
        "    handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)\n",
        "    info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)\n",
        "    print(Fore.RED+\"Device {}: {}, Memory : ({:.2f}% free): {}(total), {} (free), {} (used)\".format(i, nvidia_smi.nvmlDeviceGetName(handle), 100*info.free/info.total, info.total, info.free, info.used))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1CLSR5GKBlQO"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "import time\n",
        "import os\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from copy import deepcopy\n",
        "import utils\n",
        "from tensorflow.keras import backend as k\n",
        "import keras.backend\n",
        "import tensorflow as tf\n",
        "from keras.utils import np_utils\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers import Input, Add, Dense, Dropout, Flatten\n",
        "from keras.layers import Activation, Conv2D, MaxPooling2D, AveragePooling2D\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras import regularizers\n",
        "from tensorflow.keras.optimizers import Adam, Nadam\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "import gc\n",
        "from tensorflow.keras.callbacks import Callback\n",
        "from keras.utils.vis_utils import plot_model\n",
        "class ClearMemory(Callback):\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        gc.collect()\n",
        "        k.clear_session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i_FWsAxhwNdy"
      },
      "outputs": [],
      "source": [
        "\n",
        "output_dim = 10\n",
        "dataset = 'mnist'\n",
        "input_width =28\n",
        "input_height = 28\n",
        "input_channels = 1\n",
        "results_path = \"./results/\" + dataset + \"/\"\n",
        "if not os.path.exists(results_path):\n",
        "        os.makedirs(results_path)\n",
        "\n",
        "# run parameteres\n",
        "number_runs = 1\n",
        "number_iterations =10\n",
        "population_size = 20\n",
        "batch_size_pso =    128\n",
        "psofit_steps= 20\n",
        "psoeval_steps = 20\n",
        "batch_size_full_training = 128\n",
        "epochs_pso = 1\n",
        "epochs_full_training = 10\n",
        "dropout = 0.3\n",
        "dropout_pso=0.3\n",
        "\n",
        "#  CNN parameters\n",
        "max_conv_output_channels = 200\n",
        "max_fully_connected_neurons = 500\n",
        "min_layer =8\n",
        "max_layer = 14\n",
        "probability_convolution = 0.4\n",
        "probability_pooling = 0.3\n",
        "probability_fully_connected = 0.3\n",
        "max_conv_kernel_size = 5\n",
        "\n",
        "#\n",
        "Percentile_of_sample =  40  #1-100\n",
        "Cg = 0.5\n",
        "all_gBest_metrics = np.zeros((number_runs, 2))\n",
        "runs_time = []\n",
        "all_gbest_par = []\n",
        "best_gBest_acc = 0\n",
        "\n",
        "#  specify a path to save final model\n",
        "path = F\"/content/gdrive/My Drive/27ord/mnist\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6rpBsn3dYbBI"
      },
      "source": [
        "# **2. Loading dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSV6zpvhvtXA",
        "outputId": "591a7001-925b-4a74-fbb0-1d95bbae4590"
      },
      "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test)  = fashion_mnist.load_data()\n",
        "y_train = keras.utils.np_utils.to_categorical(y_train, output_dim)\n",
        "y_test = keras.utils.np_utils.to_categorical(y_test, output_dim)\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)\n",
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "so-UqHAm_CQ7"
      },
      "outputs": [],
      "source": [
        "datagen = ImageDataGenerator(validation_split=0.1)\n",
        "\n",
        "data_test = ImageDataGenerator()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_len_x_train = len(x_train)*(Percentile_of_sample/100)\n",
        "x_train = x_train[:int(new_len_x_train),:,:]\n",
        "y_train = y_train[:int(new_len_x_train)]\n",
        "\n",
        "new_len_x_test = len(x_test)*(Percentile_of_sample/100)\n",
        "x_test = x_test[:int(new_len_x_test),:,:]\n",
        "y_test = y_test[:int(new_len_x_test)]\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDv6EUe6fXsQ",
        "outputId": "4febbb0e-4a4e-442b-de43-5033f5878049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(24000, 28, 28, 1)\n",
            "(24000, 10)\n",
            "(4000, 28, 28, 1)\n",
            "(4000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPyA7yfRYOpw"
      },
      "source": [
        "# **3. Defining particle class**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aSjnVlkgzVIm"
      },
      "outputs": [],
      "source": [
        "# Hide Tensorflow INFOS and WARNINGS\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
        "\n",
        "class Particle:\n",
        "    def __init__(self, min_layer, max_layer, max_pool_layers, input_width, input_height, input_channels,\n",
        "        conv_prob, pool_prob, fc_prob, max_conv_kernel, max_out_ch, max_fc_neurons, output_dim):\n",
        "        self.input_width = input_width\n",
        "        self.input_height = input_height\n",
        "        self.input_channels = input_channels\n",
        "\n",
        "        self.num_pool_layers = 0\n",
        "        self.max_pool_layers = max_pool_layers\n",
        "\n",
        "        self.feature_width = input_width\n",
        "        self.feature_height = input_height\n",
        "\n",
        "        self.depth = np.random.randint(min_layer, max_layer)\n",
        "        self.conv_prob = conv_prob\n",
        "        self.pool_prob = pool_prob\n",
        "        self.fc_prob = fc_prob\n",
        "        self.max_conv_kernel = max_conv_kernel\n",
        "        self.max_out_ch = max_out_ch\n",
        "\n",
        "        self.max_fc_neurons = max_fc_neurons\n",
        "        self.output_dim = output_dim\n",
        "\n",
        "        self.layers = []\n",
        "        self.acc = None\n",
        "        self.vel = [] # Initial velocity\n",
        "        self.pBest = []\n",
        "\n",
        "        # Build particle architecture\n",
        "        self.initialization()\n",
        "\n",
        "        # Update initial velocity\n",
        "        for i in range(len(self.layers)):\n",
        "            if self.layers[i][\"type\"] != \"fc\":\n",
        "                self.vel.append({\"type\": \"keep\"})\n",
        "            else:\n",
        "                self.vel.append({\"type\": \"keep_fc\"})\n",
        "\n",
        "        self.model = None\n",
        "        self.pBest = deepcopy(self)\n",
        "\n",
        "\n",
        "    def __str__(self):\n",
        "        string = \"\"\n",
        "        for z in range(len(self.layers)):\n",
        "            string = string + self.layers[z][\"type\"] + \" | \"\n",
        "\n",
        "        return string\n",
        "\n",
        "    def initialization(self):\n",
        "        out_channel = np.random.randint(3, self.max_out_ch)\n",
        "        conv_kernel = np.random.randint(3, self.max_conv_kernel)\n",
        "\n",
        "        # First layer is always a convolution layer\n",
        "        self.layers.append({\"type\": \"conv\", \"ou_c\": out_channel, \"kernel\": conv_kernel})\n",
        "\n",
        "        conv_prob = self.conv_prob\n",
        "        pool_prob = conv_prob + self.pool_prob\n",
        "        fc_prob = pool_prob\n",
        "\n",
        "        # conv_prob =  self.conv_prob\n",
        "        # pool_prob =  self.pool_prob+self.conv_prob\n",
        "        # fc_prob =    self.fc_prob\n",
        "\n",
        "        for i in range(1, self.depth):\n",
        "            if self.layers[-1][\"type\"] == \"fc\":\n",
        "                layer_type = 1.1\n",
        "            elif i>(self.depth -4):\n",
        "               layer_type = 1.1\n",
        "\n",
        "            else:\n",
        "                layer_type = np.random.rand()\n",
        "\n",
        "            if layer_type < conv_prob:\n",
        "                self.layers = utils.add_conv(self.layers, self.max_out_ch, self.max_conv_kernel)\n",
        "\n",
        "            elif layer_type >= conv_prob and layer_type <= pool_prob and self.layers[-1][\"type\"] != \"max_pool\" and self.layers[-1][\"type\"]!= 'avg_pool' and (self.depth -4)>0 :\n",
        "\n",
        "                print(self.layers[-1][\"type\"])\n",
        "\n",
        "                self.layers, self.num_pool_layers = utils.add_pool(self.layers, self.fc_prob, self.num_pool_layers, self.max_pool_layers, self.max_out_ch, self.max_conv_kernel, self.max_fc_neurons, self.output_dim)\n",
        "\n",
        "            elif i>0.70*self.depth and layer_type >= fc_prob:\n",
        "                self.layers = utils.add_fc(self.layers, self.max_fc_neurons)\n",
        "            else:\n",
        "              self.layers = utils.add_conv(self.layers, self.max_out_ch, self.max_conv_kernel)\n",
        "\n",
        "\n",
        "        self.layers[-1] = {\"type\": \"fc\", \"ou_c\": self.output_dim, \"kernel\": -1}\n",
        "\n",
        "\n",
        "    def velocity(self, gBest, Cg):\n",
        "        self.vel = utils.computeVelocity(gBest, self.pBest.layers, self.layers, Cg)\n",
        "\n",
        "    def update(self):\n",
        "        new_p = utils.updateParticle(self.layers, self.vel)\n",
        "        new_p = self.validate(new_p)\n",
        "\n",
        "        self.layers = new_p\n",
        "        self.model = None\n",
        "\n",
        "    def validate(self, list_layers):\n",
        "        # Last layer should always be a fc with number of neurons equal to the number of outputs\n",
        "        list_layers[-1] = {\"type\": \"fc\", \"ou_c\": self.output_dim, \"kernel\": -1}\n",
        "\n",
        "        # Remove excess of Pooling layers\n",
        "        self.num_pool_layers = 0\n",
        "        for i in range(len(list_layers)):\n",
        "            if list_layers[i][\"type\"] == \"max_pool\" or list_layers[i][\"type\"] == \"avg_pool\":\n",
        "                self.num_pool_layers += 1\n",
        "\n",
        "                if self.num_pool_layers >= self.max_pool_layers:\n",
        "                    list_layers[i][\"type\"] = \"remove\"\n",
        "\n",
        "\n",
        "        # Now, fix the inputs of each conv and pool layers\n",
        "        updated_list_layers = []\n",
        "\n",
        "        for i in range(0, len(list_layers)):\n",
        "            if list_layers[i][\"type\"] != \"remove\":\n",
        "                if list_layers[i][\"type\"] == \"conv\":\n",
        "                    updated_list_layers.append({\"type\": \"conv\", \"ou_c\": list_layers[i][\"ou_c\"], \"kernel\": list_layers[i][\"kernel\"]})\n",
        "\n",
        "                if list_layers[i][\"type\"] == \"fc\":\n",
        "                    updated_list_layers.append(list_layers[i])\n",
        "\n",
        "                if list_layers[i][\"type\"] == \"max_pool\":\n",
        "                    updated_list_layers.append({\"type\": \"max_pool\", \"ou_c\": -1, \"kernel\": 2})\n",
        "\n",
        "                if list_layers[i][\"type\"] == \"avg_pool\":\n",
        "                    updated_list_layers.append({\"type\": \"avg_pool\", \"ou_c\": -1, \"kernel\": 2})\n",
        "\n",
        "        return updated_list_layers\n",
        "\n",
        "    ##### Model methods ####\n",
        "    def model_compile(self, dropout_rate):\n",
        "        list_layers = self.layers\n",
        "        self.model = Sequential()\n",
        "\n",
        "        for i in range(len(list_layers)):\n",
        "            if list_layers[i][\"type\"] == \"conv\":\n",
        "                n_out_filters = list_layers[i][\"ou_c\"]\n",
        "                kernel_size = list_layers[i][\"kernel\"]\n",
        "\n",
        "                if i == 0:\n",
        "                    in_w = self.input_width\n",
        "                    in_h = self.input_height\n",
        "                    in_c = self.input_channels\n",
        "                    self.model.add(Conv2D(n_out_filters, kernel_size, strides=(1,1), padding=\"same\", data_format=\"channels_last\", kernel_initializer='he_normal', bias_initializer='he_normal', activation=None, input_shape=(in_w, in_h, in_c)))\n",
        "                    self.model.add(BatchNormalization())\n",
        "                    self.model.add(Activation(\"relu\"))\n",
        "                else:\n",
        "                    self.model.add(Dropout(dropout_rate))\n",
        "                    self.model.add(Conv2D(n_out_filters, kernel_size, strides=(1,1), padding=\"same\", kernel_initializer='he_normal', bias_initializer='he_normal', activation=None))\n",
        "                    self.model.add(BatchNormalization())\n",
        "                    self.model.add(Activation(\"relu\"))\n",
        "\n",
        "            if list_layers[i][\"type\"] == \"max_pool\":\n",
        "                kernel_size = list_layers[i][\"kernel\"]\n",
        "\n",
        "                self.model.add(MaxPooling2D(pool_size=(3, 3), strides=2))\n",
        "\n",
        "            if list_layers[i][\"type\"] == \"avg_pool\":\n",
        "                kernel_size = list_layers[i][\"kernel\"]\n",
        "\n",
        "                self.model.add(AveragePooling2D(pool_size=(3, 3), strides=2))\n",
        "\n",
        "            if list_layers[i][\"type\"] == \"fc\":\n",
        "                if list_layers[i-1][\"type\"] != \"fc\":\n",
        "\n",
        "                    self.model.add(Flatten())\n",
        "\n",
        "                self.model.add(Dropout(dropout_rate))\n",
        "\n",
        "                if i == len(list_layers) - 1:\n",
        "                    self.model.add(Dense(list_layers[i][\"ou_c\"], kernel_initializer='he_normal', bias_initializer='he_normal', activation=None))\n",
        "                    self.model.add(BatchNormalization())\n",
        "                    self.model.add(Activation(\"softmax\"))\n",
        "                else:\n",
        "                    self.model.add(Dense(list_layers[i][\"ou_c\"], kernel_initializer='he_normal', bias_initializer='he_normal', kernel_regularizer=regularizers.l2(0.01), activation=None))\n",
        "                    self.model.add(BatchNormalization())\n",
        "                    self.model.add(Activation(\"relu\"))\n",
        "\n",
        "        adam = Adam( learning_rate=0.001, beta_1=0.9, beta_2=0.999, decay=0.0)\n",
        "#         print(self.model.summary())\n",
        "\n",
        "        self.model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=[\"accuracy\"],run_eagerly=True)\n",
        "\n",
        "\n",
        "    def model_fit(self, x_train, y_train,data_gen, batch_size, epochs,psofit_steps):\n",
        "        # TODO: add option to only use a sample size of the dataset\n",
        "\n",
        "        # hist = self.model.fit(x=x_train, y=y_train, validation_split=0.2, batch_size=batch_size, epochs=epochs, callbacks=ClearMemory())\n",
        "\n",
        "        hist = self.model.fit(data_gen.flow(x_train, y_train, batch_size=batch_size ,subset='training'),\n",
        "                              validation_data=datagen.flow(x_train, y_train,\n",
        "                                                           batch_size=64, subset='validation'),\n",
        "         steps_per_epoch=psofit_steps, epochs=epochs, callbacks=ClearMemory())\n",
        "\n",
        "\n",
        "\n",
        "        return hist\n",
        "\n",
        "    def model_fit_complete(self, x_train, y_train,data_gen, batch_size, epochs):\n",
        "\n",
        "        # hist = self.model.fit(x=x_train, y=y_train, validation_split=0.2, batch_size=batch_size, epochs=epochs)\n",
        "\n",
        "        hist = self.model.fit(data_gen.flow(x_train, y_train, batch_size=batch_size  ),\n",
        "               steps_per_epoch=0.9*len(x_train) /batch_size, epochs=epochs, callbacks=ClearMemory())\n",
        "\n",
        "        return hist\n",
        "\n",
        "    def model_delete(self):\n",
        "        # This is used to free up memory during PSO training\n",
        "        del self.model\n",
        "        keras.backend.clear_session()\n",
        "        self.model = None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "stgseCCZzyxv"
      },
      "outputs": [],
      "source": [
        "max_pool_layers = 0\n",
        "in_w = input_width\n",
        "while in_w > 4:\n",
        "    max_pool_layers += 1\n",
        "    in_w = in_w/2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OyKtqYv-YIHJ"
      },
      "source": [
        "# **4. PSO code**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_TaU_DWi2yx5",
        "outputId": "bfa0aaf9-46ec-4c93-dfde-4a0fecd40332"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initializing population...\n",
            "conv\n",
            "conv\n",
            "1\n",
            "conv | conv | avg_pool | conv | avg_pool | conv | conv | conv | fc | fc | \n",
            "conv\n",
            "conv\n",
            "2\n",
            "conv | conv | avg_pool | conv | avg_pool | conv | conv | conv | fc | fc | \n",
            "conv\n",
            "3\n",
            "conv | conv | avg_pool | conv | conv | conv | fc | fc | \n",
            "conv\n",
            "4\n",
            "conv | conv | conv | conv | conv | conv | conv | conv | avg_pool | fc | fc | fc | \n",
            "conv\n",
            "5\n",
            "conv | conv | conv | conv | conv | conv | avg_pool | conv | conv | conv | fc | fc | fc | \n",
            "conv\n",
            "conv\n",
            "6\n",
            "conv | max_pool | conv | conv | conv | max_pool | conv | conv | fc | fc | fc | \n",
            "conv\n",
            "conv\n",
            "7\n",
            "conv | conv | conv | avg_pool | conv | conv | avg_pool | conv | conv | conv | fc | fc | fc | \n",
            "conv\n",
            "8\n",
            "conv | conv | avg_pool | conv | conv | conv | conv | conv | fc | fc | \n",
            "conv\n",
            "conv\n",
            "9\n",
            "conv | max_pool | conv | conv | max_pool | conv | conv | fc | fc | \n",
            "conv\n",
            "10\n",
            "conv | conv | max_pool | conv | conv | conv | conv | conv | fc | fc | fc | \n",
            "conv\n",
            "11\n",
            "conv | avg_pool | conv | conv | conv | conv | conv | conv | conv | fc | fc | fc | \n",
            "conv\n",
            "12\n",
            "conv | conv | conv | conv | max_pool | conv | fc | fc | \n",
            "conv\n",
            "13\n",
            "conv | conv | conv | conv | conv | max_pool | conv | fc | fc | \n",
            "14\n",
            "conv | conv | conv | conv | conv | conv | conv | conv | fc | fc | fc | \n",
            "conv\n",
            "15\n",
            "conv | conv | conv | conv | max_pool | conv | conv | fc | fc | \n",
            "conv\n",
            "conv\n",
            "16\n",
            "conv | avg_pool | conv | avg_pool | conv | conv | conv | fc | fc | \n",
            "conv\n",
            "17\n",
            "conv | conv | conv | avg_pool | conv | conv | conv | fc | fc | \n",
            "conv\n",
            "18\n",
            "conv | conv | max_pool | conv | conv | conv | conv | conv | conv | conv | fc | fc | fc | \n",
            "conv\n",
            "conv\n",
            "19\n",
            "conv | conv | conv | conv | conv | max_pool | conv | conv | avg_pool | conv | fc | fc | fc | \n",
            "conv\n",
            "20\n",
            "conv | conv | conv | avg_pool | conv | conv | conv | conv | fc | fc | \n",
            "Verifying accuracy of the current gBest...\n",
            "20/20 [==============================] - 15s 207ms/step - loss: 3.2138 - accuracy: 0.4441 - val_loss: 12.1715 - val_accuracy: 0.2763\n",
            "20/20 [==============================] - 1s 49ms/step - loss: 12.3988 - accuracy: 0.2836\n",
            "\u001b[32m Current gBest acc: 0.2762500047683716\n",
            "\n",
            "\u001b[34m Current gBest test acc: 0.2835937440395355\n",
            "\n",
            "\u001b[31mDevice 0: b'Tesla K80', Memory : (88.34% free): 11996954624(total), 10598678528 (free), 1398276096 (used)\n",
            "\u001b[33m Looking for a new gBest in the population...\n",
            "\u001b[36m Initialization - Particle: 2\n",
            "conv | conv | avg_pool | conv | avg_pool | conv | conv | conv | fc | fc | \n",
            "20/20 [==============================] - 8s 315ms/step - loss: 9.5318 - accuracy: 0.4395 - val_loss: 13.3174 - val_accuracy: 0.2996\n",
            "\u001b[31mDevice 0: b'Tesla K80', Memory : (79.39% free): 11996954624(total), 9524936704 (free), 2472017920 (used)\n",
            "Found a new gBest.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-dffdbc6e61a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     63\u001b[0m           \u001b[0mgBest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdropout_pso\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m           \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'rn '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mki\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'iter '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'par '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m           \u001b[0mgBest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m           \u001b[0mtest_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgBest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size_pso\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpsoeval_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m           \u001b[0mgBest_test_acc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_metrics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filepath, overwrite, include_optimizer, save_format, signatures, options, save_traces)\u001b[0m\n\u001b[1;32m   2383\u001b[0m     \u001b[0;31m# pylint: enable=line-too-long\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2384\u001b[0m     save.save_model(self, filepath, overwrite, include_optimizer, save_format,\n\u001b[0;32m-> 2385\u001b[0;31m                     signatures, options, save_traces)\n\u001b[0m\u001b[1;32m   2386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2387\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mtraceback_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter_traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/saving/save.py\u001b[0m in \u001b[0;36msave_model\u001b[0;34m(model, filepath, overwrite, include_optimizer, save_format, signatures, options, save_traces)\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mgeneric_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSharedObjectSavingScope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m       saved_model_save.save(model, filepath, overwrite, include_optimizer,\n\u001b[0;32m--> 152\u001b[0;31m                             signatures, options, save_traces)\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/saving/saved_model/save.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(model, filepath, overwrite, include_optimizer, signatures, options, save_traces)\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras_option_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_traces\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m       saved_nodes, node_paths = save_lib.save_and_return_nodes(\n\u001b[0;32m---> 94\u001b[0;31m           model, filepath, signatures, options)\n\u001b[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;31m# Save all metadata to a separate file in the SavedModel directory.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36msave_and_return_nodes\u001b[0;34m(obj, export_dir, signatures, options, experimental_skip_checkpoint)\u001b[0m\n\u001b[1;32m   1367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1368\u001b[0m   _, exported_graph, object_saver, asset_info, saved_nodes, node_paths = (\n\u001b[0;32m-> 1369\u001b[0;31m       _build_meta_graph(obj, signatures, options, meta_graph_def))\n\u001b[0m\u001b[1;32m   1370\u001b[0m   saved_model.saved_model_schema_version = (\n\u001b[1;32m   1371\u001b[0m       constants.SAVED_MODEL_SCHEMA_VERSION)\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_build_meta_graph\u001b[0;34m(obj, signatures, options, meta_graph_def)\u001b[0m\n\u001b[1;32m   1534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1535\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0msave_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1536\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_build_meta_graph_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta_graph_def\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_build_meta_graph_impl\u001b[0;34m(obj, signatures, options, meta_graph_def)\u001b[0m\n\u001b[1;32m   1479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1480\u001b[0m   signatures, wrapped_functions = (\n\u001b[0;32m-> 1481\u001b[0;31m       signature_serialization.canonicalize_signatures(signatures))\n\u001b[0m\u001b[1;32m   1482\u001b[0m   \u001b[0msignature_serialization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_saveable_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint_graph_view\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m   \u001b[0msignature_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignature_serialization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_signature_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/signature_serialization.py\u001b[0m in \u001b[0;36mcanonicalize_signatures\u001b[0;34m(signatures)\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignature_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnum_normalized_signatures_counter\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0m_NUM_DISPLAY_NORMALIZED_SIGNATURES\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m       \u001b[0msignature_name_changes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_signature_name_changes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignature_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0msignature_name_changes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0mnum_normalized_signatures_counter\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/signature_serialization.py\u001b[0m in \u001b[0;36m_get_signature_name_changes\u001b[0;34m(concrete_function)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m       user_specified_name = compat.as_str(\n\u001b[0;32m---> 84\u001b[0;31m           graph_input.op.get_attr(\"_user_specified_name\"))\n\u001b[0m\u001b[1;32m     85\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0msignature_input_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0muser_specified_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0mname_changes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0muser_specified_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignature_input_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2688\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2689\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mc_api_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2690\u001b[0;31m         \u001b[0mpywrap_tf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_OperationGetAttrValueProto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2691\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpywrap_tf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2692\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# A.Initilazation\n",
        "run_info=[]\n",
        "for ki in range(number_runs):\n",
        "  particle = []\n",
        "  print(\"Initializing population...\")\n",
        "  for i in range(population_size):\n",
        "    particle.append(Particle(min_layer, max_layer, max_pool_layers, input_width, input_height, input_channels, probability_convolution,\n",
        "                            probability_pooling, probability_fully_connected, max_conv_kernel_size, max_conv_output_channels,\n",
        "                             max_fully_connected_neurons, output_dim))\n",
        "\n",
        "    print(i+1)\n",
        "    print(particle[i])\n",
        "\n",
        "  print(\"Verifying accuracy of the current gBest...\")\n",
        "  gBest = deepcopy(particle[0])\n",
        "  gBest.model_compile(dropout_pso)\n",
        "  hist = gBest.model_fit(x_train, y_train,datagen, batch_size=batch_size_pso, epochs=epochs_pso,psofit_steps=psofit_steps)\n",
        "  # test_metrics =gBest.model.evaluate(x_test, y_test, data_test, batch_size=batch_size_pso)\n",
        "  test_metrics =gBest.model.evaluate(data_test.flow(x_test, y_test, batch_size=batch_size_pso ),steps=psoeval_steps)\n",
        "  gBest.model_delete()\n",
        "  gBest_acc = np.zeros(number_iterations)\n",
        "  gBest_test_acc = np.zeros(number_iterations)\n",
        "  gBest_acc[0] = hist.history['val_accuracy'][-1]\n",
        "  gBest_test_acc[0] = test_metrics[1]\n",
        "  particle[0].acc = hist.history['val_accuracy'][-1]\n",
        "  particle[0].pBest.acc = hist.history['val_accuracy'][-1]\n",
        "  print(Fore.GREEN ,\"Current gBest acc: \" + str(gBest_acc[0]) + \"\\n\")\n",
        "  print(Fore.BLUE ,\"Current gBest test acc: \" + str(gBest_test_acc[0]) + \"\\n\")\n",
        "\n",
        "  for v in range(deviceCount):\n",
        "      handle = nvidia_smi.nvmlDeviceGetHandleByIndex(v)\n",
        "      info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)\n",
        "      print(Fore.RED+\"Device {}: {}, Memory : ({:.2f}% free): {}(total), {} (free), {} (used)\".format(v, nvidia_smi.nvmlDeviceGetName(handle), 100*info.free/info.total, info.total, info.free, info.used))\n",
        "\n",
        "  print(Fore.YELLOW ,\"Looking for a new gBest in the population...\")\n",
        "\n",
        "\n",
        "# B.Finding initial best\n",
        "\n",
        "  for i in range(1, population_size):\n",
        "\n",
        "      print(Fore.CYAN ,'Initialization - Particle: ' + str(i+1))\n",
        "      print(particle[i])\n",
        "\n",
        "\n",
        "\n",
        "      particle[i].model_compile(dropout_pso)\n",
        "      hist = particle[i].model_fit(x_train, y_train,datagen, batch_size=batch_size_pso, epochs=epochs_pso,psofit_steps=psofit_steps)\n",
        "\n",
        "\n",
        "      particle[i].acc = hist.history['val_accuracy'][-1]\n",
        "      particle[i].pBest.acc = hist.history['val_accuracy'][-1]\n",
        "      particle[i].model_delete()\n",
        "      for v in range(deviceCount):\n",
        "              handle = nvidia_smi.nvmlDeviceGetHandleByIndex(v)\n",
        "              info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)\n",
        "              print(Fore.RED+\"Device {}: {}, Memory : ({:.2f}% free): {}(total), {} (free), {} (used)\".format(v, nvidia_smi.nvmlDeviceGetName(handle), 100*info.free/info.total, info.total, info.free, info.used))\n",
        "\n",
        "      if particle[i].pBest.acc >= gBest_acc[0]:\n",
        "          print(\"Found a new gBest.\")\n",
        "          gBest = deepcopy(particle[i])\n",
        "          gBest_acc[0] = particle[i].pBest.acc\n",
        "          gBest.model_compile(dropout_pso)\n",
        "          name='rn '+str(ki)+'iter '+str(0)+'par '+str(i)\n",
        "          gBest.model.save(name)\n",
        "          test_metrics = gBest.model.evaluate(data_test.flow(x_test, y_test, batch_size=batch_size_pso ),steps=psoeval_steps)\n",
        "          gBest_test_acc[0] = test_metrics[1]\n",
        "          print(Fore.GREEN+\"New gBest acc: \" + str(gBest_acc[0]))\n",
        "          print(Fore.GREEN+\"New gBest test acc: \" + str(gBest_test_acc[0]))\n",
        "\n",
        "      gBest.model_delete()\n",
        "\n",
        "# C.Updating particles\n",
        "  for i in range(1, number_iterations):\n",
        "\n",
        "          gBest_acc_t = gBest_acc[i-1]\n",
        "          gBest_test_acc_t =gBest_test_acc[i-1]\n",
        "\n",
        "          for j in range(population_size):\n",
        "\n",
        "              print(Fore.CYAN ,'run_num: '+str(ki) +'  -Iteration: ' + str(i) + ' - Particle: ' + str(j+1))\n",
        "              for v in range(deviceCount):\n",
        "                handle = nvidia_smi.nvmlDeviceGetHandleByIndex(v)\n",
        "                info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)\n",
        "                print(Fore.RED+\"Device {}: {}, Memory : ({:.2f}% free): {}(total), {} (free), {} (used)\".format(v, nvidia_smi.nvmlDeviceGetName(handle), 100*info.free/info.total, info.total, info.free, info.used))\n",
        "\n",
        "              # Update particle velocity\n",
        "              particle[j].velocity(gBest.layers, Cg)\n",
        "\n",
        "              # Update particle architecture\n",
        "              particle[j].update()\n",
        "\n",
        "              print('Particle NEW architecture: ')\n",
        "              print(particle[j])\n",
        "\n",
        "              # Compute the acc in the updated particle\n",
        "              particle[j].model_compile(dropout)\n",
        "              hist = particle[j].model_fit(x_train, y_train,datagen, batch_size=batch_size_pso,\n",
        "                                          epochs=epochs_pso,psofit_steps=psofit_steps)\n",
        "              particle[j].acc = hist.history['val_accuracy'][-1]\n",
        "\n",
        "              f_test = particle[j].acc\n",
        "              pBest_acc = particle[j].pBest.acc\n",
        "\n",
        "              particle[j].model_delete()\n",
        "\n",
        "              if f_test >= pBest_acc:\n",
        "                  print(Fore.BLACK+\"Found a new pBest.\")\n",
        "                  print(Fore.BLACK+\"Current acc: \" + str(f_test))\n",
        "                  print(Fore.BLACK+\"Past pBest acc: \" + str(pBest_acc))\n",
        "                  pBest_acc = f_test\n",
        "                  particle[j].pBest = deepcopy(particle[j])\n",
        "\n",
        "# D.Update new best\n",
        "                  if pBest_acc >= gBest_acc_t:\n",
        "                          print(Fore.GREEN+\"Found a new gBest.\")\n",
        "                          gBest_acc_t = pBest_acc\n",
        "\n",
        "                          gBest = deepcopy(particle[j])\n",
        "\n",
        "                          gBest.model_compile(dropout)\n",
        "                          hist = gBest.model_fit(x_train, y_train,datagen, batch_size=batch_size_pso, epochs=epochs_pso,psofit_steps=psofit_steps)\n",
        "\n",
        "\n",
        "                          test_metrics = gBest.model.evaluate(data_test.flow(x_test, y_test, batch_size=batch_size_pso ),steps=psoeval_steps)\n",
        "                          name='rn'+str(ki)+'iter'+str(i)+'par'+str(j)\n",
        "                          gBest.model.save(name)\n",
        "\n",
        "\n",
        "                          gBest.model_delete()\n",
        "                          gBest_test_acc_t = test_metrics[1]\n",
        "\n",
        "\n",
        "              gBest_acc[i] = gBest_acc_t\n",
        "              gBest_test_acc[i] = gBest_test_acc_t\n",
        "              run_info.append([ki,i,gBest_acc_t,gBest_test_acc_t])\n",
        "\n",
        "              print(Fore.CYAN+\"Current gBest acc: \" + str(gBest_acc[i]))\n",
        "              print(Fore.CYAN+\"Current gBest test acc: \" + str(gBest_test_acc[i]))\n",
        "\n",
        "\n",
        "          print(Fore.MAGENTA+\"Current gBest acc: \" + str(gBest_acc[i]))\n",
        "          print(Fore.MAGENTA+\"Current gBest test acc: \" + str(gBest_test_acc[i]))\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-mNHt1-Y01q"
      },
      "source": [
        "# **5. Final model training and evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gKdzvETnGfgv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1906456-14d2-492a-c849-456059a2ae18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Further training gBest model...\n",
            "gBest's number of trainable parameters: 6680943\n",
            "Epoch 1/10\n",
            "421/421 [==============================] - 24s 56ms/step - loss: 1.6843 - accuracy: 0.8325\n",
            "Epoch 2/10\n",
            "421/421 [==============================] - 23s 55ms/step - loss: 0.8930 - accuracy: 0.8719\n",
            "Epoch 3/10\n",
            "421/421 [==============================] - 23s 55ms/step - loss: 0.8281 - accuracy: 0.8805\n",
            "Epoch 4/10\n",
            "421/421 [==============================] - 23s 55ms/step - loss: 0.7906 - accuracy: 0.8868\n",
            "Epoch 5/10\n",
            "421/421 [==============================] - 23s 55ms/step - loss: 0.7556 - accuracy: 0.8921\n",
            "Epoch 6/10\n",
            "421/421 [==============================] - 23s 54ms/step - loss: 0.7316 - accuracy: 0.8961\n",
            "Epoch 7/10\n",
            "421/421 [==============================] - 23s 55ms/step - loss: 0.7079 - accuracy: 0.8985\n",
            "Epoch 8/10\n",
            "421/421 [==============================] - 24s 56ms/step - loss: 0.6770 - accuracy: 0.9022\n",
            "Epoch 9/10\n",
            "421/421 [==============================] - 24s 57ms/step - loss: 0.6531 - accuracy: 0.9054\n",
            "Epoch 10/10\n",
            "421/421 [==============================] - 24s 57ms/step - loss: 0.6359 - accuracy: 0.9058\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(\"\\nFurther training gBest model...\")\n",
        "gBest.model_compile(dropout)\n",
        "trainable_count = 0\n",
        "for i in range(len(gBest.model.trainable_weights)):\n",
        "    trainable_count += keras.backend.count_params(gBest.model.trainable_weights[i])\n",
        "\n",
        "print(\"gBest's number of trainable parameters: \" + str(trainable_count))\n",
        "\n",
        "hist = gBest.model_fit_complete(x_train, y_train,datagen, batch_size=batch_size_full_training, epochs=epochs_full_training)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "def plot_history(history):\n",
        "    acc = history.history['accuracy']\n",
        "    # val_acc = history.history['val_accuracy']\n",
        "    loss = history.history['loss']\n",
        "    # val_loss = history.history['val_loss']\n",
        "    x = range(1, len(acc) + 1)\n",
        "\n",
        "    plt.figure(figsize=(12, 5))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(x, acc, 'b', label='Training acc')\n",
        "    # plt.plot(x, val_acc, 'r', label='Validation acc')\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.legend()\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(x, loss, 'b', label='Training loss')\n",
        "    # plt.plot(x, val_loss, 'r', label='Validation loss')\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.legend()"
      ],
      "metadata": {
        "id": "qYNOmY45bbXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(hist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "oBpB04apbcj6",
        "outputId": "789dcec4-ca89-4dc8-bead-240abb4572eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4XiVW67NIS_J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ca2171db-a0c2-4baf-e46a-0155232bdc7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "plot_model(gBest.model, show_shapes=True, show_layer_names=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uw5R_mt5VRcP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dffc8855-0673-47e1-96cb-33750bb130a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable_count ;  6680943\n",
            "gBest architecture: \n",
            "conv | max_pool | conv | conv | conv | fc | fc | \n"
          ]
        }
      ],
      "source": [
        "print(\"trainable_count ; \",trainable_count)\n",
        "\n",
        "matplotlib.use('Agg')\n",
        "plt.plot(gBest_acc)\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"gBest acc\")\n",
        "plt.savefig(results_path + \"gBest-iter-\" + str(i) + \".png\")\n",
        "plt.close()\n",
        "\n",
        "print('gBest architecture: ')\n",
        "print(gBest)\n",
        "\n",
        "np.save(results_path + \"gBest_inter_\" + str(i) + \"_acc_history.npy\", gBest_acc)\n",
        "\n",
        "np.save(results_path + \"gBest_iter_\" + str(i) + \"_test_acc_history.npy\", gBest_test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gBest.model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHr9yYfbbuKr",
        "outputId": "15758620-96c7-401c-db4e-7fec7276f39e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 28, 28, 147)       1470      \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 28, 28, 147)      588       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 28, 28, 147)       0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 147)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 13, 13, 147)       0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 13, 13, 128)       169472    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 13, 13, 128)      512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 13, 13, 128)       0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 13, 13, 128)       0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 13, 13, 78)        159822    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 13, 13, 78)       312       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 13, 13, 78)        0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 13, 13, 78)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 13, 13, 81)        101169    \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 13, 13, 81)       324       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 13, 13, 81)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 13689)             0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 13689)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 456)               6242640   \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 456)              1824      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 456)               0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 456)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                4570      \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 10)               40        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 10)                0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,682,743\n",
            "Trainable params: 6,680,943\n",
            "Non-trainable params: 1,800\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5sVAUxLPXRJO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44779853-20de-4f27-e2f7-b6b589ae9156"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Evaluating gBest model on the test set...\n",
            "79/79 [==============================] - 2s 21ms/step - loss: 0.6127 - accuracy: 0.9130\n",
            "\n",
            "gBest model loss in the test set: 0.6126753091812134 - Test set accuracy: 0.9129999876022339\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(\"\\nEvaluating gBest model on the test set...\")\n",
        "\n",
        "metrics = gBest.model.evaluate(data_test.flow(x_test, y_test, batch_size=batch_size_full_training ), batch_size=batch_size_full_training)\n",
        "\n",
        "print(\"\\ngBest model loss in the test set: \" + str(metrics[0]) + \" - Test set accuracy: \" + str(metrics[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yzH_yV58IRbT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82e5e8cf-c279-4f9f-ac35-5e6ca87995cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            "INFO:tensorflow:Assets written to: /content/gdrive/My Drive/pso_second_step/Mnist/assets\n"
          ]
        }
      ],
      "source": [
        "#  saving the model\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "gBest.model.save('/content/gdrive/My Drive/pso_second_step/Mnist')\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}