Copy_of_Yolo_Basic.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Yolo_Basic.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/krishnagorrepati/machine-learing-projects/blob/master/Copy_of_Yolo_Basic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "8edSiHPi2N5l",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# https://keras.io/\n",
        "!pip install -q keras\n",
        "import keras\n",
        "\n",
        "import keras\n",
        "from keras.datasets import cifar10\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Input, AveragePooling2D, merge, Activation\n",
        "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
        "from keras.layers import Concatenate\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.layers import Reshape, Activation, Conv2D, Input, MaxPooling2D, BatchNormalization, Flatten, Dense, Lambda\n",
        "from keras.layers.merge import concatenate\n",
        "\n",
        "# this part will prevent tensorflow to allocate all the avaliable GPU Memory\n",
        "# backend\n",
        "import tensorflow as tf\n",
        "from keras import backend as k\n",
        "\n",
        "# Don't pre-allocate memory; allocate as-needed\n",
        "config = tf.ConfigProto()\n",
        "config.gpu_options.allow_growth = True\n",
        "\n",
        "# Create a session with the above options specified.\n",
        "k.tensorflow_backend.set_session(tf.Session(config=config))\n",
        "\n",
        "# Hyperparameters\n",
        "batch_size = 128\n",
        "num_classes = 10\n",
        "epochs = 50\n",
        "l = 10\n",
        "num_filter = 20"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-hkvQwOH2cCO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load CIFAR10 Data\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "img_height, img_width, channel = x_train.shape[1],x_train.shape[2],x_train.shape[3]\n",
        "\n",
        "# convert to one hot encoing \n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ztoDypc63gEs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def space_to_depth_x2(x):\n",
        "    return tf.space_to_depth(x, block_size=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SNnGm8Tv2fR1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "input = Input(shape=(img_height, img_width, channel,))\n",
        "\n",
        "# Layer 1\n",
        "x = Conv2D(32, (3,3), strides=(1,1), padding='same', name='conv_1', use_bias=False)(input)\n",
        "x = BatchNormalization(name='norm_1')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "\n",
        "# Layer 2\n",
        "x = Conv2D(64, (3,3), strides=(1,1), padding='same', name='conv_2', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_2')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "\n",
        "# Layer 3\n",
        "x = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_3', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_3')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "\n",
        "# Layer 4\n",
        "x = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_4', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_4')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "\n",
        "# Layer 5\n",
        "x = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_5', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_5')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "\n",
        "# Layer 6\n",
        "x = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_6', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_6')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "\n",
        "# Layer 7\n",
        "x = Conv2D(128, (1,1), strides=(1,1), padding='same', name='conv_7', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_7')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "\n",
        "# Layer 8\n",
        "x = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_8', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_8')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "\n",
        "# Layer 9\n",
        "x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_9', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_9')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "\n",
        "# Layer 10\n",
        "x = Conv2D(256, (1,1), strides=(1,1), padding='same', name='conv_10', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_10')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "\n",
        "# Layer 11\n",
        "x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_11', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_11')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "\n",
        "# Layer 12\n",
        "x = Conv2D(256, (1,1), strides=(1,1), padding='same', name='conv_12', use_bias=False)(x)\n",
        "x = BatchNormalization(name='norm_12')(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "\n",
        "skip_connection = x\n",
        "\n",
        "# Layer 13\n",
        "layer13 = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_13', use_bias=False)(x)\n",
        "layer13 = BatchNormalization(name='norm_13')(layer13)\n",
        "layer13 = LeakyReLU(alpha=0.1)(layer13)\n",
        "\n",
        "#skip_connection = x\n",
        "\n",
        "layer13 = MaxPooling2D(pool_size=(2, 2))(layer13)\n",
        "\n",
        "# Layer 14\n",
        "layer14 = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_14', use_bias=False)(layer13)\n",
        "layer14 = BatchNormalization(name='norm_14')(layer14)\n",
        "layer14 = LeakyReLU(alpha=0.1)(layer14)\n",
        "\n",
        "# Layer 15\n",
        "layer15 = Conv2D(512, (1,1), strides=(1,1), padding='same', name='conv_15', use_bias=False)(layer14)\n",
        "layer15 = BatchNormalization(name='norm_15')(layer15)\n",
        "layer15 = LeakyReLU(alpha=0.1)(layer15)\n",
        "\n",
        "# Layer 16\n",
        "layer16 = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_16', use_bias=False)(layer15)\n",
        "layer16 = BatchNormalization(name='norm_16')(layer16)\n",
        "layer16 = LeakyReLU(alpha=0.1)(layer16)\n",
        "\n",
        "# Layer 17\n",
        "layer17 = Conv2D(512, (1,1), strides=(1,1), padding='same', name='conv_17', use_bias=False)(layer16)\n",
        "layer17 = BatchNormalization(name='norm_17')(layer17)\n",
        "layer17 = LeakyReLU(alpha=0.1)(layer17)\n",
        "\n",
        "# Layer 18\n",
        "layer18 = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_18', use_bias=False)(layer17)\n",
        "layer18 = BatchNormalization(name='norm_18')(layer18)\n",
        "layer18 = LeakyReLU(alpha=0.1)(layer18)\n",
        "\n",
        "# Layer 19\n",
        "layer19 = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_19', use_bias=False)(layer18)\n",
        "layer19 = BatchNormalization(name='norm_19')(layer19)\n",
        "layer19 = LeakyReLU(alpha=0.1)(layer19)\n",
        "\n",
        "# Layer 20\n",
        "layer20 = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_20', use_bias=False)(layer19)\n",
        "layer20 = BatchNormalization(name='norm_20')(layer20)\n",
        "layer20 = LeakyReLU(alpha=0.1)(layer20)\n",
        "\n",
        "# Layer 21\n",
        "skip_connection = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_21', use_bias=False)(skip_connection)\n",
        "skip_connection = BatchNormalization(name='norm_21')(skip_connection)\n",
        "skip_connection = LeakyReLU(alpha=0.1)(skip_connection)\n",
        "skip_connection = Lambda(space_to_depth_x2)(skip_connection)\n",
        "\n",
        "layer21 = concatenate([skip_connection, layer20])\n",
        "\n",
        "# Layer 22\n",
        "layer22 = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_22', use_bias=False)(layer21)\n",
        "layer22 = BatchNormalization(name='norm_22')(layer22)\n",
        "layer22 = LeakyReLU(alpha=0.1)(layer22)\n",
        "\n",
        "# Layer 23\n",
        "layer23 = Flatten()(layer22)\n",
        "\n",
        "output = Dense(num_classes, activation='softmax')(layer23)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Jeh0VAxy26NV",
        "colab_type": "code",
        "outputId": "ed745908-aab8-4009-c8c7-5353ab4dd6db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2773
        }
      },
      "cell_type": "code",
      "source": [
        "model = Model(inputs=[input], outputs=[output])\n",
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv_1 (Conv2D)                 (None, 32, 32, 32)   864         input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "norm_1 (BatchNormalization)     (None, 32, 32, 32)   128         conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_23 (LeakyReLU)      (None, 32, 32, 32)   0           norm_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2D)  (None, 16, 16, 32)   0           leaky_re_lu_23[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv_2 (Conv2D)                 (None, 16, 16, 64)   18432       max_pooling2d_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "norm_2 (BatchNormalization)     (None, 16, 16, 64)   256         conv_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_24 (LeakyReLU)      (None, 16, 16, 64)   0           norm_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2D)  (None, 8, 8, 64)     0           leaky_re_lu_24[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv_3 (Conv2D)                 (None, 8, 8, 128)    73728       max_pooling2d_7[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "norm_3 (BatchNormalization)     (None, 8, 8, 128)    512         conv_3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_25 (LeakyReLU)      (None, 8, 8, 128)    0           norm_3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_4 (Conv2D)                 (None, 8, 8, 64)     8192        leaky_re_lu_25[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_4 (BatchNormalization)     (None, 8, 8, 64)     256         conv_4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_26 (LeakyReLU)      (None, 8, 8, 64)     0           norm_4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_5 (Conv2D)                 (None, 8, 8, 128)    73728       leaky_re_lu_26[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_5 (BatchNormalization)     (None, 8, 8, 128)    512         conv_5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_27 (LeakyReLU)      (None, 8, 8, 128)    0           norm_5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2D)  (None, 4, 4, 128)    0           leaky_re_lu_27[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv_6 (Conv2D)                 (None, 4, 4, 256)    294912      max_pooling2d_8[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "norm_6 (BatchNormalization)     (None, 4, 4, 256)    1024        conv_6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_28 (LeakyReLU)      (None, 4, 4, 256)    0           norm_6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_7 (Conv2D)                 (None, 4, 4, 128)    32768       leaky_re_lu_28[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_7 (BatchNormalization)     (None, 4, 4, 128)    512         conv_7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_29 (LeakyReLU)      (None, 4, 4, 128)    0           norm_7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_8 (Conv2D)                 (None, 4, 4, 256)    294912      leaky_re_lu_29[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_8 (BatchNormalization)     (None, 4, 4, 256)    1024        conv_8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_30 (LeakyReLU)      (None, 4, 4, 256)    0           norm_8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2D)  (None, 2, 2, 256)    0           leaky_re_lu_30[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv_9 (Conv2D)                 (None, 2, 2, 512)    1179648     max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "norm_9 (BatchNormalization)     (None, 2, 2, 512)    2048        conv_9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_31 (LeakyReLU)      (None, 2, 2, 512)    0           norm_9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_10 (Conv2D)                (None, 2, 2, 256)    131072      leaky_re_lu_31[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_10 (BatchNormalization)    (None, 2, 2, 256)    1024        conv_10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_32 (LeakyReLU)      (None, 2, 2, 256)    0           norm_10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_11 (Conv2D)                (None, 2, 2, 512)    1179648     leaky_re_lu_32[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_11 (BatchNormalization)    (None, 2, 2, 512)    2048        conv_11[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_33 (LeakyReLU)      (None, 2, 2, 512)    0           norm_11[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_12 (Conv2D)                (None, 2, 2, 256)    131072      leaky_re_lu_33[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_12 (BatchNormalization)    (None, 2, 2, 256)    1024        conv_12[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_34 (LeakyReLU)      (None, 2, 2, 256)    0           norm_12[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_13 (Conv2D)                (None, 2, 2, 512)    1179648     leaky_re_lu_34[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_13 (BatchNormalization)    (None, 2, 2, 512)    2048        conv_13[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_35 (LeakyReLU)      (None, 2, 2, 512)    0           norm_13[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling2D) (None, 1, 1, 512)    0           leaky_re_lu_35[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv_14 (Conv2D)                (None, 1, 1, 1024)   4718592     max_pooling2d_10[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "norm_14 (BatchNormalization)    (None, 1, 1, 1024)   4096        conv_14[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_36 (LeakyReLU)      (None, 1, 1, 1024)   0           norm_14[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_15 (Conv2D)                (None, 1, 1, 512)    524288      leaky_re_lu_36[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_15 (BatchNormalization)    (None, 1, 1, 512)    2048        conv_15[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_37 (LeakyReLU)      (None, 1, 1, 512)    0           norm_15[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_16 (Conv2D)                (None, 1, 1, 1024)   4718592     leaky_re_lu_37[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_16 (BatchNormalization)    (None, 1, 1, 1024)   4096        conv_16[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_38 (LeakyReLU)      (None, 1, 1, 1024)   0           norm_16[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_17 (Conv2D)                (None, 1, 1, 512)    524288      leaky_re_lu_38[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_17 (BatchNormalization)    (None, 1, 1, 512)    2048        conv_17[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_39 (LeakyReLU)      (None, 1, 1, 512)    0           norm_17[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_18 (Conv2D)                (None, 1, 1, 1024)   4718592     leaky_re_lu_39[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_18 (BatchNormalization)    (None, 1, 1, 1024)   4096        conv_18[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_40 (LeakyReLU)      (None, 1, 1, 1024)   0           norm_18[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_19 (Conv2D)                (None, 1, 1, 1024)   9437184     leaky_re_lu_40[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "norm_19 (BatchNormalization)    (None, 1, 1, 1024)   4096        conv_19[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_21 (Conv2D)                (None, 2, 2, 64)     16384       leaky_re_lu_34[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_41 (LeakyReLU)      (None, 1, 1, 1024)   0           norm_19[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "norm_21 (BatchNormalization)    (None, 2, 2, 64)     256         conv_21[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_20 (Conv2D)                (None, 1, 1, 1024)   9437184     leaky_re_lu_41[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_43 (LeakyReLU)      (None, 2, 2, 64)     0           norm_21[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "norm_20 (BatchNormalization)    (None, 1, 1, 1024)   4096        conv_20[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_2 (Lambda)               (None, 1, 1, 256)    0           leaky_re_lu_43[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_42 (LeakyReLU)      (None, 1, 1, 1024)   0           norm_20[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 1, 1, 1280)   0           lambda_2[0][0]                   \n",
            "                                                                 leaky_re_lu_42[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv_22 (Conv2D)                (None, 1, 1, 1024)   11796480    concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "norm_22 (BatchNormalization)    (None, 1, 1, 1024)   4096        conv_22[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_44 (LeakyReLU)      (None, 1, 1, 1024)   0           norm_22[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "flatten_2 (Flatten)             (None, 1024)         0           leaky_re_lu_44[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 10)           10250       flatten_2[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 50,541,802\n",
            "Trainable params: 50,521,130\n",
            "Non-trainable params: 20,672\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "apCwOjvZ4Kts",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# determine Loss function and Optimizer\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tLaFy2AO4TLl",
        "colab_type": "code",
        "outputId": "890e5adc-a08e-4ecb-8b27-d42a5a5fe223",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1774
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=epochs,\n",
        "                    verbose=1,\n",
        "                    validation_data=(x_test, y_test))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "50000/50000 [==============================] - 101s 2ms/step - loss: 1.5168 - acc: 0.4565 - val_loss: 3.6033 - val_acc: 0.3302\n",
            "Epoch 2/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 1.0293 - acc: 0.6323 - val_loss: 1.6219 - val_acc: 0.5035\n",
            "Epoch 3/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.8208 - acc: 0.7123 - val_loss: 1.2042 - val_acc: 0.5997\n",
            "Epoch 4/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.6991 - acc: 0.7555 - val_loss: 2.2421 - val_acc: 0.5758\n",
            "Epoch 5/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.6015 - acc: 0.7918 - val_loss: 1.0959 - val_acc: 0.6621\n",
            "Epoch 6/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.5513 - acc: 0.8106 - val_loss: 0.9213 - val_acc: 0.7177\n",
            "Epoch 7/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.4524 - acc: 0.8441 - val_loss: 1.0267 - val_acc: 0.7004\n",
            "Epoch 8/50\n",
            "50000/50000 [==============================] - 92s 2ms/step - loss: 0.4024 - acc: 0.8615 - val_loss: 0.8106 - val_acc: 0.7400\n",
            "Epoch 9/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.3403 - acc: 0.8835 - val_loss: 2.6972 - val_acc: 0.5051\n",
            "Epoch 10/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.3135 - acc: 0.8924 - val_loss: 1.7609 - val_acc: 0.5465\n",
            "Epoch 11/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.3111 - acc: 0.8941 - val_loss: 0.8318 - val_acc: 0.7532\n",
            "Epoch 12/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.2005 - acc: 0.9297 - val_loss: 0.8830 - val_acc: 0.7580\n",
            "Epoch 13/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.1698 - acc: 0.9414 - val_loss: 1.1837 - val_acc: 0.7101\n",
            "Epoch 14/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.1561 - acc: 0.9448 - val_loss: 0.9166 - val_acc: 0.7657\n",
            "Epoch 15/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.1315 - acc: 0.9536 - val_loss: 1.0545 - val_acc: 0.7530\n",
            "Epoch 16/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.1358 - acc: 0.9532 - val_loss: 1.2335 - val_acc: 0.7171\n",
            "Epoch 17/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.1084 - acc: 0.9626 - val_loss: 1.1389 - val_acc: 0.7492\n",
            "Epoch 18/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0983 - acc: 0.9661 - val_loss: 1.2588 - val_acc: 0.7359\n",
            "Epoch 19/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0865 - acc: 0.9695 - val_loss: 1.1609 - val_acc: 0.7578\n",
            "Epoch 20/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0881 - acc: 0.9693 - val_loss: 2.3625 - val_acc: 0.6135\n",
            "Epoch 21/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0978 - acc: 0.9661 - val_loss: 1.2588 - val_acc: 0.7296\n",
            "Epoch 22/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0804 - acc: 0.9733 - val_loss: 1.2158 - val_acc: 0.7500\n",
            "Epoch 23/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0674 - acc: 0.9767 - val_loss: 1.1454 - val_acc: 0.7659\n",
            "Epoch 24/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0632 - acc: 0.9789 - val_loss: 1.2550 - val_acc: 0.7393\n",
            "Epoch 25/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0604 - acc: 0.9790 - val_loss: 1.0467 - val_acc: 0.7803\n",
            "Epoch 26/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0977 - acc: 0.9681 - val_loss: 1.2560 - val_acc: 0.7146\n",
            "Epoch 27/50\n",
            "50000/50000 [==============================] - 92s 2ms/step - loss: 0.1019 - acc: 0.9646 - val_loss: 0.9979 - val_acc: 0.7773\n",
            "Epoch 28/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0539 - acc: 0.9812 - val_loss: 1.1058 - val_acc: 0.7781\n",
            "Epoch 29/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0455 - acc: 0.9836 - val_loss: 1.0551 - val_acc: 0.7909\n",
            "Epoch 30/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0437 - acc: 0.9850 - val_loss: 1.1830 - val_acc: 0.7709\n",
            "Epoch 31/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.2174 - acc: 0.9374 - val_loss: 1.0513 - val_acc: 0.7578\n",
            "Epoch 32/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0455 - acc: 0.9843 - val_loss: 1.0590 - val_acc: 0.7947\n",
            "Epoch 33/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0302 - acc: 0.9899 - val_loss: 1.2071 - val_acc: 0.7797\n",
            "Epoch 34/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0341 - acc: 0.9888 - val_loss: 1.7196 - val_acc: 0.7229\n",
            "Epoch 35/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0402 - acc: 0.9861 - val_loss: 1.1465 - val_acc: 0.7720\n",
            "Epoch 36/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0396 - acc: 0.9865 - val_loss: 1.0922 - val_acc: 0.7910\n",
            "Epoch 37/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0391 - acc: 0.9863 - val_loss: 1.1685 - val_acc: 0.7714\n",
            "Epoch 38/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0368 - acc: 0.9871 - val_loss: 1.1100 - val_acc: 0.7825\n",
            "Epoch 39/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0407 - acc: 0.9857 - val_loss: 1.2445 - val_acc: 0.7733\n",
            "Epoch 40/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0332 - acc: 0.9886 - val_loss: 1.2491 - val_acc: 0.7713\n",
            "Epoch 41/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0398 - acc: 0.9869 - val_loss: 1.4168 - val_acc: 0.7430\n",
            "Epoch 42/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0357 - acc: 0.9878 - val_loss: 1.1808 - val_acc: 0.7694\n",
            "Epoch 43/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0454 - acc: 0.9851 - val_loss: 1.0527 - val_acc: 0.7872\n",
            "Epoch 44/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0329 - acc: 0.9887 - val_loss: 1.1978 - val_acc: 0.7748\n",
            "Epoch 45/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0321 - acc: 0.9892 - val_loss: 1.1876 - val_acc: 0.7795\n",
            "Epoch 46/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0320 - acc: 0.9893 - val_loss: 1.1542 - val_acc: 0.7881\n",
            "Epoch 47/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0337 - acc: 0.9888 - val_loss: 1.3287 - val_acc: 0.7637\n",
            "Epoch 48/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0289 - acc: 0.9901 - val_loss: 1.2128 - val_acc: 0.7771\n",
            "Epoch 49/50\n",
            "50000/50000 [==============================] - 93s 2ms/step - loss: 0.0603 - acc: 0.9812 - val_loss: 0.9708 - val_acc: 0.7997\n",
            "Epoch 50/50\n",
            "50000/50000 [==============================] - 92s 2ms/step - loss: 0.0295 - acc: 0.9901 - val_loss: 1.1284 - val_acc: 0.7851\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f33acf02e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "pTfZIGrf4Uyd",
        "colab_type": "code",
        "outputId": "887700b4-dbb6-483a-e0fd-b8e1c5e42cff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "cell_type": "code",
      "source": [
        "# Test the model\n",
        "score = model.evaluate(x_test, y_test, verbose=1)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])\n",
        "# Save the trained weights in to .h5 format\n",
        "model.save_weights(\"Yolo_Basic_model2.h5\")\n",
        "print(\"Saved the model to disk\")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 9s 938us/step\n",
            "Test loss: 1.1283900047779083\n",
            "Test accuracy: 0.7851\n",
            "Saved the model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Jiyb9TlVGsZs",
        "colab_type": "code",
        "outputId": "fa74dae3-c55a-488b-fdae-fa9ebd29897f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "cell_type": "code",
      "source": [
        "model.save_weights(\"Yolo_Basic_model2.h5\")\n",
        "print(\"Saved the model to disk\")\n",
        "from google.colab import files\n",
        "\n",
        "files.download('Yolo_Basic_model2.h5')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved the model to disk\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Exception happened during processing of request from ('::ffff:127.0.0.1', 55206, 0, 0)\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 317, in _handle_request_noblock\n",
            "    self.process_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 348, in process_request\n",
            "    self.finish_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 361, in finish_request\n",
            "    self.RequestHandlerClass(request, client_address, self)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 721, in __init__\n",
            "    self.handle()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 418, in handle\n",
            "    self.handle_one_request()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 406, in handle_one_request\n",
            "    method()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 639, in do_GET\n",
            "    self.copyfile(f, self.wfile)\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 800, in copyfile\n",
            "    shutil.copyfileobj(source, outputfile)\n",
            "  File \"/usr/lib/python3.6/shutil.py\", line 82, in copyfileobj\n",
            "    fdst.write(buf)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 800, in write\n",
            "    self._sock.sendall(b)\n",
            "ConnectionResetError: [Errno 104] Connection reset by peer\n",
            "----------------------------------------\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "g9A3pesKbUJ8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}