feature_maps_n_deconv.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "feature_maps_n_deconv.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WtnupgkCvnue"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.models import Model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.applications.ResNet50(\n",
        "    include_top=True,\n",
        "    weights=\"imagenet\",\n",
        "    input_tensor=None,\n",
        "    input_shape=None,\n",
        "    pooling=None,\n",
        "    classes=1000,\n",
        "    classifier_activation=\"softmax\",\n",
        ")\n"
      ],
      "metadata": {
        "id": "7p0Wt_ZpwDMZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YeZ4G9L-2QD5",
        "outputId": "2e111042-254d-4423-8005-0bf3f53fdf47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_3[0][0]']                \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
            "                                                                  'conv2_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
            "                                                                  'conv2_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
            "                                                                  'conv2_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
            "                                                                  'conv3_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
            "                                                                  'conv3_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
            "                                                                  'conv3_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
            "                                                                  'conv3_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
            "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
            "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
            "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
            "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
            "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
            "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
            "                                                                  'conv5_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
            "                                                                  'conv5_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
            "                                                                  'conv5_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " avg_pool (GlobalAveragePooling  (None, 2048)        0           ['conv5_block3_out[0][0]']       \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " predictions (Dense)            (None, 1000)         2049000     ['avg_pool[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 25,636,712\n",
            "Trainable params: 25,583,592\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_blocks = [1,5,10,15,20,25,40]\n",
        "outputs = [model.layers[i].output for i in conv_blocks]\n",
        "resnet_model = Model(inputs = model.inputs,outputs=outputs)"
      ],
      "metadata": {
        "id": "qOfPOxWI2Vy6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_map_preds = resnet_model.predict(image)\n",
        "plots = 1\n",
        "for fmap in feature_map_preds:\n",
        "    # plot all 64 maps in an 8x8 squares\n",
        "    ix = 1\n",
        "    for _ in range(plots):\n",
        "        for _ in range(plots):\n",
        "            # specify subplot and turn of axis\n",
        "            ax = plt.subplot(plots, plots, ix)\n",
        "            ax.set_xticks([])\n",
        "            ax.set_yticks([])\n",
        "            # plot filter channel in grayscale\n",
        "            plt.imshow(fmap[0, :, :, ix-1], cmap='gray')\n",
        "            ix += 1\n",
        "    # show the figure\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LShp9lwI1qDB",
        "outputId": "e04f8b3c-0e6d-4734-8297-a7e1084e2de8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:6 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7efbe1a95830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zW7x4scy98Yd63WZ69HP27FlZ660GequF3lhKXX04NDQka70jOdva2mSu1ia91b/Dhw/L3Fs99EZm6u967tw5Wav+5moFlW9WIAiaFQiCZgWCoFmBIGhWIAiaFQiCZgWCyDRnzeVycuY4NjYm69V6nXeFn7cKVigUZK7W0FatWiVrveM6vWsTe3p6ZK5em/d3eeONN2T+8MMPy1wdqWmm/27eVZe9vb0yP3XqlMzV38VbYVPXKprpFTczs+7ubpmXl6dbx1vtU+t7anWPb1YgCJoVCIJmBYKgWYEgaFYgCJoVCIJmBYIo867l+5//cVnZiJldvHVPB/i/11QsFpf9WZCpWQH8e/jPYCAImhUIgmYFgqBZgSBoViAImhUIgmYFgqBZgSBoViCI/wDGJPau5P1tMgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = {0: 'tench, Tinca tinca',\n",
        " 1: 'goldfish, Carassius auratus',\n",
        " 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',\n",
        " 3: 'tiger shark, Galeocerdo cuvieri',\n",
        " 4: 'hammerhead, hammerhead shark',\n",
        " 5: 'electric ray, crampfish, numbfish, torpedo',\n",
        " 6: 'stingray',\n",
        " 7: 'cock',\n",
        " 8: 'hen',\n",
        " 9: 'ostrich, Struthio camelus',\n",
        " 10: 'brambling, Fringilla montifringilla',\n",
        " 11: 'goldfinch, Carduelis carduelis',\n",
        " 12: 'house finch, linnet, Carpodacus mexicanus',\n",
        " 13: 'junco, snowbird',\n",
        " 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',\n",
        " 15: 'robin, American robin, Turdus migratorius',\n",
        " 16: 'bulbul',\n",
        " 17: 'jay',\n",
        " 18: 'magpie',\n",
        " 19: 'chickadee',\n",
        " 20: 'water ouzel, dipper',\n",
        " 21: 'kite',\n",
        " 22: 'bald eagle, American eagle, Haliaeetus leucocephalus',\n",
        " 23: 'vulture',\n",
        " 24: 'great grey owl, great gray owl, Strix nebulosa',\n",
        " 25: 'European fire salamander, Salamandra salamandra',\n",
        " 26: 'common newt, Triturus vulgaris',\n",
        " 27: 'eft',\n",
        " 28: 'spotted salamander, Ambystoma maculatum',\n",
        " 29: 'axolotl, mud puppy, Ambystoma mexicanum',\n",
        " 30: 'bullfrog, Rana catesbeiana',\n",
        " 31: 'tree frog, tree-frog',\n",
        " 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',\n",
        " 33: 'loggerhead, loggerhead turtle, Caretta caretta',\n",
        " 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',\n",
        " 35: 'mud turtle',\n",
        " 36: 'terrapin',\n",
        " 37: 'box turtle, box tortoise',\n",
        " 38: 'banded gecko',\n",
        " 39: 'common iguana, iguana, Iguana iguana',\n",
        " 40: 'American chameleon, anole, Anolis carolinensis',\n",
        " 41: 'whiptail, whiptail lizard',\n",
        " 42: 'agama',\n",
        " 43: 'frilled lizard, Chlamydosaurus kingi',\n",
        " 44: 'alligator lizard',\n",
        " 45: 'Gila monster, Heloderma suspectum',\n",
        " 46: 'green lizard, Lacerta viridis',\n",
        " 47: 'African chameleon, Chamaeleo chamaeleon',\n",
        " 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',\n",
        " 49: 'African crocodile, Nile crocodile, Crocodylus niloticus',\n",
        " 50: 'American alligator, Alligator mississipiensis',\n",
        " 51: 'triceratops',\n",
        " 52: 'thunder snake, worm snake, Carphophis amoenus',\n",
        " 53: 'ringneck snake, ring-necked snake, ring snake',\n",
        " 54: 'hognose snake, puff adder, sand viper',\n",
        " 55: 'green snake, grass snake',\n",
        " 56: 'king snake, kingsnake',\n",
        " 57: 'garter snake, grass snake',\n",
        " 58: 'water snake',\n",
        " 59: 'vine snake',\n",
        " 60: 'night snake, Hypsiglena torquata',\n",
        " 61: 'boa constrictor, Constrictor constrictor',\n",
        " 62: 'rock python, rock snake, Python sebae',\n",
        " 63: 'Indian cobra, Naja naja',\n",
        " 64: 'green mamba',\n",
        " 65: 'sea snake',\n",
        " 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',\n",
        " 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',\n",
        " 68: 'sidewinder, horned rattlesnake, Crotalus cerastes',\n",
        " 69: 'trilobite',\n",
        " 70: 'harvestman, daddy longlegs, Phalangium opilio',\n",
        " 71: 'scorpion',\n",
        " 72: 'black and gold garden spider, Argiope aurantia',\n",
        " 73: 'barn spider, Araneus cavaticus',\n",
        " 74: 'garden spider, Aranea diademata',\n",
        " 75: 'black widow, Latrodectus mactans',\n",
        " 76: 'tarantula',\n",
        " 77: 'wolf spider, hunting spider',\n",
        " 78: 'tick',\n",
        " 79: 'centipede',\n",
        " 80: 'black grouse',\n",
        " 81: 'ptarmigan',\n",
        " 82: 'ruffed grouse, partridge, Bonasa umbellus',\n",
        " 83: 'prairie chicken, prairie grouse, prairie fowl',\n",
        " 84: 'peacock',\n",
        " 85: 'quail',\n",
        " 86: 'partridge',\n",
        " 87: 'African grey, African gray, Psittacus erithacus',\n",
        " 88: 'macaw',\n",
        " 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',\n",
        " 90: 'lorikeet',\n",
        " 91: 'coucal',\n",
        " 92: 'bee eater',\n",
        " 93: 'hornbill',\n",
        " 94: 'hummingbird',\n",
        " 95: 'jacamar',\n",
        " 96: 'toucan',\n",
        " 97: 'drake',\n",
        " 98: 'red-breasted merganser, Mergus serrator',\n",
        " 99: 'goose',\n",
        " 100: 'black swan, Cygnus atratus',\n",
        " 101: 'tusker',\n",
        " 102: 'echidna, spiny anteater, anteater',\n",
        " 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',\n",
        " 104: 'wallaby, brush kangaroo',\n",
        " 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',\n",
        " 106: 'wombat',\n",
        " 107: 'jellyfish',\n",
        " 108: 'sea anemone, anemone',\n",
        " 109: 'brain coral',\n",
        " 110: 'flatworm, platyhelminth',\n",
        " 111: 'nematode, nematode worm, roundworm',\n",
        " 112: 'conch',\n",
        " 113: 'snail',\n",
        " 114: 'slug',\n",
        " 115: 'sea slug, nudibranch',\n",
        " 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',\n",
        " 117: 'chambered nautilus, pearly nautilus, nautilus',\n",
        " 118: 'Dungeness crab, Cancer magister',\n",
        " 119: 'rock crab, Cancer irroratus',\n",
        " 120: 'fiddler crab',\n",
        " 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',\n",
        " 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',\n",
        " 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',\n",
        " 124: 'crayfish, crawfish, crawdad, crawdaddy',\n",
        " 125: 'hermit crab',\n",
        " 126: 'isopod',\n",
        " 127: 'white stork, Ciconia ciconia',\n",
        " 128: 'black stork, Ciconia nigra',\n",
        " 129: 'spoonbill',\n",
        " 130: 'flamingo',\n",
        " 131: 'little blue heron, Egretta caerulea',\n",
        " 132: 'American egret, great white heron, Egretta albus',\n",
        " 133: 'bittern',\n",
        " 134: 'crane',\n",
        " 135: 'limpkin, Aramus pictus',\n",
        " 136: 'European gallinule, Porphyrio porphyrio',\n",
        " 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana',\n",
        " 138: 'bustard',\n",
        " 139: 'ruddy turnstone, Arenaria interpres',\n",
        " 140: 'red-backed sandpiper, dunlin, Erolia alpina',\n",
        " 141: 'redshank, Tringa totanus',\n",
        " 142: 'dowitcher',\n",
        " 143: 'oystercatcher, oyster catcher',\n",
        " 144: 'pelican',\n",
        " 145: 'king penguin, Aptenodytes patagonica',\n",
        " 146: 'albatross, mollymawk',\n",
        " 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',\n",
        " 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',\n",
        " 149: 'dugong, Dugong dugon',\n",
        " 150: 'sea lion',\n",
        " 151: 'Chihuahua',\n",
        " 152: 'Japanese spaniel',\n",
        " 153: 'Maltese dog, Maltese terrier, Maltese',\n",
        " 154: 'Pekinese, Pekingese, Peke',\n",
        " 155: 'Shih-Tzu',\n",
        " 156: 'Blenheim spaniel',\n",
        " 157: 'papillon',\n",
        " 158: 'toy terrier',\n",
        " 159: 'Rhodesian ridgeback',\n",
        " 160: 'Afghan hound, Afghan',\n",
        " 161: 'basset, basset hound',\n",
        " 162: 'beagle',\n",
        " 163: 'bloodhound, sleuthhound',\n",
        " 164: 'bluetick',\n",
        " 165: 'black-and-tan coonhound',\n",
        " 166: 'Walker hound, Walker foxhound',\n",
        " 167: 'English foxhound',\n",
        " 168: 'redbone',\n",
        " 169: 'borzoi, Russian wolfhound',\n",
        " 170: 'Irish wolfhound',\n",
        " 171: 'Italian greyhound',\n",
        " 172: 'whippet',\n",
        " 173: 'Ibizan hound, Ibizan Podenco',\n",
        " 174: 'Norwegian elkhound, elkhound',\n",
        " 175: 'otterhound, otter hound',\n",
        " 176: 'Saluki, gazelle hound',\n",
        " 177: 'Scottish deerhound, deerhound',\n",
        " 178: 'Weimaraner',\n",
        " 179: 'Staffordshire bullterrier, Staffordshire bull terrier',\n",
        " 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',\n",
        " 181: 'Bedlington terrier',\n",
        " 182: 'Border terrier',\n",
        " 183: 'Kerry blue terrier',\n",
        " 184: 'Irish terrier',\n",
        " 185: 'Norfolk terrier',\n",
        " 186: 'Norwich terrier',\n",
        " 187: 'Yorkshire terrier',\n",
        " 188: 'wire-haired fox terrier',\n",
        " 189: 'Lakeland terrier',\n",
        " 190: 'Sealyham terrier, Sealyham',\n",
        " 191: 'Airedale, Airedale terrier',\n",
        " 192: 'cairn, cairn terrier',\n",
        " 193: 'Australian terrier',\n",
        " 194: 'Dandie Dinmont, Dandie Dinmont terrier',\n",
        " 195: 'Boston bull, Boston terrier',\n",
        " 196: 'miniature schnauzer',\n",
        " 197: 'giant schnauzer',\n",
        " 198: 'standard schnauzer',\n",
        " 199: 'Scotch terrier, Scottish terrier, Scottie',\n",
        " 200: 'Tibetan terrier, chrysanthemum dog',\n",
        " 201: 'silky terrier, Sydney silky',\n",
        " 202: 'soft-coated wheaten terrier',\n",
        " 203: 'West Highland white terrier',\n",
        " 204: 'Lhasa, Lhasa apso',\n",
        " 205: 'flat-coated retriever',\n",
        " 206: 'curly-coated retriever',\n",
        " 207: 'golden retriever',\n",
        " 208: 'Labrador retriever',\n",
        " 209: 'Chesapeake Bay retriever',\n",
        " 210: 'German short-haired pointer',\n",
        " 211: 'vizsla, Hungarian pointer',\n",
        " 212: 'English setter',\n",
        " 213: 'Irish setter, red setter',\n",
        " 214: 'Gordon setter',\n",
        " 215: 'Brittany spaniel',\n",
        " 216: 'clumber, clumber spaniel',\n",
        " 217: 'English springer, English springer spaniel',\n",
        " 218: 'Welsh springer spaniel',\n",
        " 219: 'cocker spaniel, English cocker spaniel, cocker',\n",
        " 220: 'Sussex spaniel',\n",
        " 221: 'Irish water spaniel',\n",
        " 222: 'kuvasz',\n",
        " 223: 'schipperke',\n",
        " 224: 'groenendael',\n",
        " 225: 'malinois',\n",
        " 226: 'briard',\n",
        " 227: 'kelpie',\n",
        " 228: 'komondor',\n",
        " 229: 'Old English sheepdog, bobtail',\n",
        " 230: 'Shetland sheepdog, Shetland sheep dog, Shetland',\n",
        " 231: 'collie',\n",
        " 232: 'Border collie',\n",
        " 233: 'Bouvier des Flandres, Bouviers des Flandres',\n",
        " 234: 'Rottweiler',\n",
        " 235: 'German shepherd, German shepherd dog, German police dog, alsatian',\n",
        " 236: 'Doberman, Doberman pinscher',\n",
        " 237: 'miniature pinscher',\n",
        " 238: 'Greater Swiss Mountain dog',\n",
        " 239: 'Bernese mountain dog',\n",
        " 240: 'Appenzeller',\n",
        " 241: 'EntleBucher',\n",
        " 242: 'boxer',\n",
        " 243: 'bull mastiff',\n",
        " 244: 'Tibetan mastiff',\n",
        " 245: 'French bulldog',\n",
        " 246: 'Great Dane',\n",
        " 247: 'Saint Bernard, St Bernard',\n",
        " 248: 'Eskimo dog, husky',\n",
        " 249: 'malamute, malemute, Alaskan malamute',\n",
        " 250: 'Siberian husky',\n",
        " 251: 'dalmatian, coach dog, carriage dog',\n",
        " 252: 'affenpinscher, monkey pinscher, monkey dog',\n",
        " 253: 'basenji',\n",
        " 254: 'pug, pug-dog',\n",
        " 255: 'Leonberg',\n",
        " 256: 'Newfoundland, Newfoundland dog',\n",
        " 257: 'Great Pyrenees',\n",
        " 258: 'Samoyed, Samoyede',\n",
        " 259: 'Pomeranian',\n",
        " 260: 'chow, chow chow',\n",
        " 261: 'keeshond',\n",
        " 262: 'Brabancon griffon',\n",
        " 263: 'Pembroke, Pembroke Welsh corgi',\n",
        " 264: 'Cardigan, Cardigan Welsh corgi',\n",
        " 265: 'toy poodle',\n",
        " 266: 'miniature poodle',\n",
        " 267: 'standard poodle',\n",
        " 268: 'Mexican hairless',\n",
        " 269: 'timber wolf, grey wolf, gray wolf, Canis lupus',\n",
        " 270: 'white wolf, Arctic wolf, Canis lupus tundrarum',\n",
        " 271: 'red wolf, maned wolf, Canis rufus, Canis niger',\n",
        " 272: 'coyote, prairie wolf, brush wolf, Canis latrans',\n",
        " 273: 'dingo, warrigal, warragal, Canis dingo',\n",
        " 274: 'dhole, Cuon alpinus',\n",
        " 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',\n",
        " 276: 'hyena, hyaena',\n",
        " 277: 'red fox, Vulpes vulpes',\n",
        " 278: 'kit fox, Vulpes macrotis',\n",
        " 279: 'Arctic fox, white fox, Alopex lagopus',\n",
        " 280: 'grey fox, gray fox, Urocyon cinereoargenteus',\n",
        " 281: 'tabby, tabby cat',\n",
        " 282: 'tiger cat',\n",
        " 283: 'Persian cat',\n",
        " 284: 'Siamese cat, Siamese',\n",
        " 285: 'Egyptian cat',\n",
        " 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',\n",
        " 287: 'lynx, catamount',\n",
        " 288: 'leopard, Panthera pardus',\n",
        " 289: 'snow leopard, ounce, Panthera uncia',\n",
        " 290: 'jaguar, panther, Panthera onca, Felis onca',\n",
        " 291: 'lion, king of beasts, Panthera leo',\n",
        " 292: 'tiger, Panthera tigris',\n",
        " 293: 'cheetah, chetah, Acinonyx jubatus',\n",
        " 294: 'brown bear, bruin, Ursus arctos',\n",
        " 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus',\n",
        " 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',\n",
        " 297: 'sloth bear, Melursus ursinus, Ursus ursinus',\n",
        " 298: 'mongoose',\n",
        " 299: 'meerkat, mierkat',\n",
        " 300: 'tiger beetle',\n",
        " 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',\n",
        " 302: 'ground beetle, carabid beetle',\n",
        " 303: 'long-horned beetle, longicorn, longicorn beetle',\n",
        " 304: 'leaf beetle, chrysomelid',\n",
        " 305: 'dung beetle',\n",
        " 306: 'rhinoceros beetle',\n",
        " 307: 'weevil',\n",
        " 308: 'fly',\n",
        " 309: 'bee',\n",
        " 310: 'ant, emmet, pismire',\n",
        " 311: 'grasshopper, hopper',\n",
        " 312: 'cricket',\n",
        " 313: 'walking stick, walkingstick, stick insect',\n",
        " 314: 'cockroach, roach',\n",
        " 315: 'mantis, mantid',\n",
        " 316: 'cicada, cicala',\n",
        " 317: 'leafhopper',\n",
        " 318: 'lacewing, lacewing fly',\n",
        " 319: \"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\",\n",
        " 320: 'damselfly',\n",
        " 321: 'admiral',\n",
        " 322: 'ringlet, ringlet butterfly',\n",
        " 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',\n",
        " 324: 'cabbage butterfly',\n",
        " 325: 'sulphur butterfly, sulfur butterfly',\n",
        " 326: 'lycaenid, lycaenid butterfly',\n",
        " 327: 'starfish, sea star',\n",
        " 328: 'sea urchin',\n",
        " 329: 'sea cucumber, holothurian',\n",
        " 330: 'wood rabbit, cottontail, cottontail rabbit',\n",
        " 331: 'hare',\n",
        " 332: 'Angora, Angora rabbit',\n",
        " 333: 'hamster',\n",
        " 334: 'porcupine, hedgehog',\n",
        " 335: 'fox squirrel, eastern fox squirrel, Sciurus niger',\n",
        " 336: 'marmot',\n",
        " 337: 'beaver',\n",
        " 338: 'guinea pig, Cavia cobaya',\n",
        " 339: 'sorrel',\n",
        " 340: 'zebra',\n",
        " 341: 'hog, pig, grunter, squealer, Sus scrofa',\n",
        " 342: 'wild boar, boar, Sus scrofa',\n",
        " 343: 'warthog',\n",
        " 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',\n",
        " 345: 'ox',\n",
        " 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',\n",
        " 347: 'bison',\n",
        " 348: 'ram, tup',\n",
        " 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',\n",
        " 350: 'ibex, Capra ibex',\n",
        " 351: 'hartebeest',\n",
        " 352: 'impala, Aepyceros melampus',\n",
        " 353: 'gazelle',\n",
        " 354: 'Arabian camel, dromedary, Camelus dromedarius',\n",
        " 355: 'llama',\n",
        " 356: 'weasel',\n",
        " 357: 'mink',\n",
        " 358: 'polecat, fitch, foulmart, foumart, Mustela putorius',\n",
        " 359: 'black-footed ferret, ferret, Mustela nigripes',\n",
        " 360: 'otter',\n",
        " 361: 'skunk, polecat, wood pussy',\n",
        " 362: 'badger',\n",
        " 363: 'armadillo',\n",
        " 364: 'three-toed sloth, ai, Bradypus tridactylus',\n",
        " 365: 'orangutan, orang, orangutang, Pongo pygmaeus',\n",
        " 366: 'gorilla, Gorilla gorilla',\n",
        " 367: 'chimpanzee, chimp, Pan troglodytes',\n",
        " 368: 'gibbon, Hylobates lar',\n",
        " 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',\n",
        " 370: 'guenon, guenon monkey',\n",
        " 371: 'patas, hussar monkey, Erythrocebus patas',\n",
        " 372: 'baboon',\n",
        " 373: 'macaque',\n",
        " 374: 'langur',\n",
        " 375: 'colobus, colobus monkey',\n",
        " 376: 'proboscis monkey, Nasalis larvatus',\n",
        " 377: 'marmoset',\n",
        " 378: 'capuchin, ringtail, Cebus capucinus',\n",
        " 379: 'howler monkey, howler',\n",
        " 380: 'titi, titi monkey',\n",
        " 381: 'spider monkey, Ateles geoffroyi',\n",
        " 382: 'squirrel monkey, Saimiri sciureus',\n",
        " 383: 'Madagascar cat, ring-tailed lemur, Lemur catta',\n",
        " 384: 'indri, indris, Indri indri, Indri brevicaudatus',\n",
        " 385: 'Indian elephant, Elephas maximus',\n",
        " 386: 'African elephant, Loxodonta africana',\n",
        " 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',\n",
        " 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n",
        " 389: 'barracouta, snoek',\n",
        " 390: 'eel',\n",
        " 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',\n",
        " 392: 'rock beauty, Holocanthus tricolor',\n",
        " 393: 'anemone fish',\n",
        " 394: 'sturgeon',\n",
        " 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus',\n",
        " 396: 'lionfish',\n",
        " 397: 'puffer, pufferfish, blowfish, globefish',\n",
        " 398: 'abacus',\n",
        " 399: 'abaya',\n",
        " 400: \"academic gown, academic robe, judge's robe\",\n",
        " 401: 'accordion, piano accordion, squeeze box',\n",
        " 402: 'acoustic guitar',\n",
        " 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier',\n",
        " 404: 'airliner',\n",
        " 405: 'airship, dirigible',\n",
        " 406: 'altar',\n",
        " 407: 'ambulance',\n",
        " 408: 'amphibian, amphibious vehicle',\n",
        " 409: 'analog clock',\n",
        " 410: 'apiary, bee house',\n",
        " 411: 'apron',\n",
        " 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',\n",
        " 413: 'assault rifle, assault gun',\n",
        " 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack',\n",
        " 415: 'bakery, bakeshop, bakehouse',\n",
        " 416: 'balance beam, beam',\n",
        " 417: 'balloon',\n",
        " 418: 'ballpoint, ballpoint pen, ballpen, Biro',\n",
        " 419: 'Band Aid',\n",
        " 420: 'banjo',\n",
        " 421: 'bannister, banister, balustrade, balusters, handrail',\n",
        " 422: 'barbell',\n",
        " 423: 'barber chair',\n",
        " 424: 'barbershop',\n",
        " 425: 'barn',\n",
        " 426: 'barometer',\n",
        " 427: 'barrel, cask',\n",
        " 428: 'barrow, garden cart, lawn cart, wheelbarrow',\n",
        " 429: 'baseball',\n",
        " 430: 'basketball',\n",
        " 431: 'bassinet',\n",
        " 432: 'bassoon',\n",
        " 433: 'bathing cap, swimming cap',\n",
        " 434: 'bath towel',\n",
        " 435: 'bathtub, bathing tub, bath, tub',\n",
        " 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',\n",
        " 437: 'beacon, lighthouse, beacon light, pharos',\n",
        " 438: 'beaker',\n",
        " 439: 'bearskin, busby, shako',\n",
        " 440: 'beer bottle',\n",
        " 441: 'beer glass',\n",
        " 442: 'bell cote, bell cot',\n",
        " 443: 'bib',\n",
        " 444: 'bicycle-built-for-two, tandem bicycle, tandem',\n",
        " 445: 'bikini, two-piece',\n",
        " 446: 'binder, ring-binder',\n",
        " 447: 'binoculars, field glasses, opera glasses',\n",
        " 448: 'birdhouse',\n",
        " 449: 'boathouse',\n",
        " 450: 'bobsled, bobsleigh, bob',\n",
        " 451: 'bolo tie, bolo, bola tie, bola',\n",
        " 452: 'bonnet, poke bonnet',\n",
        " 453: 'bookcase',\n",
        " 454: 'bookshop, bookstore, bookstall',\n",
        " 455: 'bottlecap',\n",
        " 456: 'bow',\n",
        " 457: 'bow tie, bow-tie, bowtie',\n",
        " 458: 'brass, memorial tablet, plaque',\n",
        " 459: 'brassiere, bra, bandeau',\n",
        " 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',\n",
        " 461: 'breastplate, aegis, egis',\n",
        " 462: 'broom',\n",
        " 463: 'bucket, pail',\n",
        " 464: 'buckle',\n",
        " 465: 'bulletproof vest',\n",
        " 466: 'bullet train, bullet',\n",
        " 467: 'butcher shop, meat market',\n",
        " 468: 'cab, hack, taxi, taxicab',\n",
        " 469: 'caldron, cauldron',\n",
        " 470: 'candle, taper, wax light',\n",
        " 471: 'cannon',\n",
        " 472: 'canoe',\n",
        " 473: 'can opener, tin opener',\n",
        " 474: 'cardigan',\n",
        " 475: 'car mirror',\n",
        " 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig',\n",
        " 477: \"carpenter's kit, tool kit\",\n",
        " 478: 'carton',\n",
        " 479: 'car wheel',\n",
        " 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',\n",
        " 481: 'cassette',\n",
        " 482: 'cassette player',\n",
        " 483: 'castle',\n",
        " 484: 'catamaran',\n",
        " 485: 'CD player',\n",
        " 486: 'cello, violoncello',\n",
        " 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',\n",
        " 488: 'chain',\n",
        " 489: 'chainlink fence',\n",
        " 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',\n",
        " 491: 'chain saw, chainsaw',\n",
        " 492: 'chest',\n",
        " 493: 'chiffonier, commode',\n",
        " 494: 'chime, bell, gong',\n",
        " 495: 'china cabinet, china closet',\n",
        " 496: 'Christmas stocking',\n",
        " 497: 'church, church building',\n",
        " 498: 'cinema, movie theater, movie theatre, movie house, picture palace',\n",
        " 499: 'cleaver, meat cleaver, chopper',\n",
        " 500: 'cliff dwelling',\n",
        " 501: 'cloak',\n",
        " 502: 'clog, geta, patten, sabot',\n",
        " 503: 'cocktail shaker',\n",
        " 504: 'coffee mug',\n",
        " 505: 'coffeepot',\n",
        " 506: 'coil, spiral, volute, whorl, helix',\n",
        " 507: 'combination lock',\n",
        " 508: 'computer keyboard, keypad',\n",
        " 509: 'confectionery, confectionary, candy store',\n",
        " 510: 'container ship, containership, container vessel',\n",
        " 511: 'convertible',\n",
        " 512: 'corkscrew, bottle screw',\n",
        " 513: 'cornet, horn, trumpet, trump',\n",
        " 514: 'cowboy boot',\n",
        " 515: 'cowboy hat, ten-gallon hat',\n",
        " 516: 'cradle',\n",
        " 517: 'crane',\n",
        " 518: 'crash helmet',\n",
        " 519: 'crate',\n",
        " 520: 'crib, cot',\n",
        " 521: 'Crock Pot',\n",
        " 522: 'croquet ball',\n",
        " 523: 'crutch',\n",
        " 524: 'cuirass',\n",
        " 525: 'dam, dike, dyke',\n",
        " 526: 'desk',\n",
        " 527: 'desktop computer',\n",
        " 528: 'dial telephone, dial phone',\n",
        " 529: 'diaper, nappy, napkin',\n",
        " 530: 'digital clock',\n",
        " 531: 'digital watch',\n",
        " 532: 'dining table, board',\n",
        " 533: 'dishrag, dishcloth',\n",
        " 534: 'dishwasher, dish washer, dishwashing machine',\n",
        " 535: 'disk brake, disc brake',\n",
        " 536: 'dock, dockage, docking facility',\n",
        " 537: 'dogsled, dog sled, dog sleigh',\n",
        " 538: 'dome',\n",
        " 539: 'doormat, welcome mat',\n",
        " 540: 'drilling platform, offshore rig',\n",
        " 541: 'drum, membranophone, tympan',\n",
        " 542: 'drumstick',\n",
        " 543: 'dumbbell',\n",
        " 544: 'Dutch oven',\n",
        " 545: 'electric fan, blower',\n",
        " 546: 'electric guitar',\n",
        " 547: 'electric locomotive',\n",
        " 548: 'entertainment center',\n",
        " 549: 'envelope',\n",
        " 550: 'espresso maker',\n",
        " 551: 'face powder',\n",
        " 552: 'feather boa, boa',\n",
        " 553: 'file, file cabinet, filing cabinet',\n",
        " 554: 'fireboat',\n",
        " 555: 'fire engine, fire truck',\n",
        " 556: 'fire screen, fireguard',\n",
        " 557: 'flagpole, flagstaff',\n",
        " 558: 'flute, transverse flute',\n",
        " 559: 'folding chair',\n",
        " 560: 'football helmet',\n",
        " 561: 'forklift',\n",
        " 562: 'fountain',\n",
        " 563: 'fountain pen',\n",
        " 564: 'four-poster',\n",
        " 565: 'freight car',\n",
        " 566: 'French horn, horn',\n",
        " 567: 'frying pan, frypan, skillet',\n",
        " 568: 'fur coat',\n",
        " 569: 'garbage truck, dustcart',\n",
        " 570: 'gasmask, respirator, gas helmet',\n",
        " 571: 'gas pump, gasoline pump, petrol pump, island dispenser',\n",
        " 572: 'goblet',\n",
        " 573: 'go-kart',\n",
        " 574: 'golf ball',\n",
        " 575: 'golfcart, golf cart',\n",
        " 576: 'gondola',\n",
        " 577: 'gong, tam-tam',\n",
        " 578: 'gown',\n",
        " 579: 'grand piano, grand',\n",
        " 580: 'greenhouse, nursery, glasshouse',\n",
        " 581: 'grille, radiator grille',\n",
        " 582: 'grocery store, grocery, food market, market',\n",
        " 583: 'guillotine',\n",
        " 584: 'hair slide',\n",
        " 585: 'hair spray',\n",
        " 586: 'half track',\n",
        " 587: 'hammer',\n",
        " 588: 'hamper',\n",
        " 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',\n",
        " 590: 'hand-held computer, hand-held microcomputer',\n",
        " 591: 'handkerchief, hankie, hanky, hankey',\n",
        " 592: 'hard disc, hard disk, fixed disk',\n",
        " 593: 'harmonica, mouth organ, harp, mouth harp',\n",
        " 594: 'harp',\n",
        " 595: 'harvester, reaper',\n",
        " 596: 'hatchet',\n",
        " 597: 'holster',\n",
        " 598: 'home theater, home theatre',\n",
        " 599: 'honeycomb',\n",
        " 600: 'hook, claw',\n",
        " 601: 'hoopskirt, crinoline',\n",
        " 602: 'horizontal bar, high bar',\n",
        " 603: 'horse cart, horse-cart',\n",
        " 604: 'hourglass',\n",
        " 605: 'iPod',\n",
        " 606: 'iron, smoothing iron',\n",
        " 607: \"jack-o'-lantern\",\n",
        " 608: 'jean, blue jean, denim',\n",
        " 609: 'jeep, landrover',\n",
        " 610: 'jersey, T-shirt, tee shirt',\n",
        " 611: 'jigsaw puzzle',\n",
        " 612: 'jinrikisha, ricksha, rickshaw',\n",
        " 613: 'joystick',\n",
        " 614: 'kimono',\n",
        " 615: 'knee pad',\n",
        " 616: 'knot',\n",
        " 617: 'lab coat, laboratory coat',\n",
        " 618: 'ladle',\n",
        " 619: 'lampshade, lamp shade',\n",
        " 620: 'laptop, laptop computer',\n",
        " 621: 'lawn mower, mower',\n",
        " 622: 'lens cap, lens cover',\n",
        " 623: 'letter opener, paper knife, paperknife',\n",
        " 624: 'library',\n",
        " 625: 'lifeboat',\n",
        " 626: 'lighter, light, igniter, ignitor',\n",
        " 627: 'limousine, limo',\n",
        " 628: 'liner, ocean liner',\n",
        " 629: 'lipstick, lip rouge',\n",
        " 630: 'Loafer',\n",
        " 631: 'lotion',\n",
        " 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',\n",
        " 633: \"loupe, jeweler's loupe\",\n",
        " 634: 'lumbermill, sawmill',\n",
        " 635: 'magnetic compass',\n",
        " 636: 'mailbag, postbag',\n",
        " 637: 'mailbox, letter box',\n",
        " 638: 'maillot',\n",
        " 639: 'maillot, tank suit',\n",
        " 640: 'manhole cover',\n",
        " 641: 'maraca',\n",
        " 642: 'marimba, xylophone',\n",
        " 643: 'mask',\n",
        " 644: 'matchstick',\n",
        " 645: 'maypole',\n",
        " 646: 'maze, labyrinth',\n",
        " 647: 'measuring cup',\n",
        " 648: 'medicine chest, medicine cabinet',\n",
        " 649: 'megalith, megalithic structure',\n",
        " 650: 'microphone, mike',\n",
        " 651: 'microwave, microwave oven',\n",
        " 652: 'military uniform',\n",
        " 653: 'milk can',\n",
        " 654: 'minibus',\n",
        " 655: 'miniskirt, mini',\n",
        " 656: 'minivan',\n",
        " 657: 'missile',\n",
        " 658: 'mitten',\n",
        " 659: 'mixing bowl',\n",
        " 660: 'mobile home, manufactured home',\n",
        " 661: 'Model T',\n",
        " 662: 'modem',\n",
        " 663: 'monastery',\n",
        " 664: 'monitor',\n",
        " 665: 'moped',\n",
        " 666: 'mortar',\n",
        " 667: 'mortarboard',\n",
        " 668: 'mosque',\n",
        " 669: 'mosquito net',\n",
        " 670: 'motor scooter, scooter',\n",
        " 671: 'mountain bike, all-terrain bike, off-roader',\n",
        " 672: 'mountain tent',\n",
        " 673: 'mouse, computer mouse',\n",
        " 674: 'mousetrap',\n",
        " 675: 'moving van',\n",
        " 676: 'muzzle',\n",
        " 677: 'nail',\n",
        " 678: 'neck brace',\n",
        " 679: 'necklace',\n",
        " 680: 'nipple',\n",
        " 681: 'notebook, notebook computer',\n",
        " 682: 'obelisk',\n",
        " 683: 'oboe, hautboy, hautbois',\n",
        " 684: 'ocarina, sweet potato',\n",
        " 685: 'odometer, hodometer, mileometer, milometer',\n",
        " 686: 'oil filter',\n",
        " 687: 'organ, pipe organ',\n",
        " 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',\n",
        " 689: 'overskirt',\n",
        " 690: 'oxcart',\n",
        " 691: 'oxygen mask',\n",
        " 692: 'packet',\n",
        " 693: 'paddle, boat paddle',\n",
        " 694: 'paddlewheel, paddle wheel',\n",
        " 695: 'padlock',\n",
        " 696: 'paintbrush',\n",
        " 697: \"pajama, pyjama, pj's, jammies\",\n",
        " 698: 'palace',\n",
        " 699: 'panpipe, pandean pipe, syrinx',\n",
        " 700: 'paper towel',\n",
        " 701: 'parachute, chute',\n",
        " 702: 'parallel bars, bars',\n",
        " 703: 'park bench',\n",
        " 704: 'parking meter',\n",
        " 705: 'passenger car, coach, carriage',\n",
        " 706: 'patio, terrace',\n",
        " 707: 'pay-phone, pay-station',\n",
        " 708: 'pedestal, plinth, footstall',\n",
        " 709: 'pencil box, pencil case',\n",
        " 710: 'pencil sharpener',\n",
        " 711: 'perfume, essence',\n",
        " 712: 'Petri dish',\n",
        " 713: 'photocopier',\n",
        " 714: 'pick, plectrum, plectron',\n",
        " 715: 'pickelhaube',\n",
        " 716: 'picket fence, paling',\n",
        " 717: 'pickup, pickup truck',\n",
        " 718: 'pier',\n",
        " 719: 'piggy bank, penny bank',\n",
        " 720: 'pill bottle',\n",
        " 721: 'pillow',\n",
        " 722: 'ping-pong ball',\n",
        " 723: 'pinwheel',\n",
        " 724: 'pirate, pirate ship',\n",
        " 725: 'pitcher, ewer',\n",
        " 726: \"plane, carpenter's plane, woodworking plane\",\n",
        " 727: 'planetarium',\n",
        " 728: 'plastic bag',\n",
        " 729: 'plate rack',\n",
        " 730: 'plow, plough',\n",
        " 731: \"plunger, plumber's helper\",\n",
        " 732: 'Polaroid camera, Polaroid Land camera',\n",
        " 733: 'pole',\n",
        " 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',\n",
        " 735: 'poncho',\n",
        " 736: 'pool table, billiard table, snooker table',\n",
        " 737: 'pop bottle, soda bottle',\n",
        " 738: 'pot, flowerpot',\n",
        " 739: \"potter's wheel\",\n",
        " 740: 'power drill',\n",
        " 741: 'prayer rug, prayer mat',\n",
        " 742: 'printer',\n",
        " 743: 'prison, prison house',\n",
        " 744: 'projectile, missile',\n",
        " 745: 'projector',\n",
        " 746: 'puck, hockey puck',\n",
        " 747: 'punching bag, punch bag, punching ball, punchball',\n",
        " 748: 'purse',\n",
        " 749: 'quill, quill pen',\n",
        " 750: 'quilt, comforter, comfort, puff',\n",
        " 751: 'racer, race car, racing car',\n",
        " 752: 'racket, racquet',\n",
        " 753: 'radiator',\n",
        " 754: 'radio, wireless',\n",
        " 755: 'radio telescope, radio reflector',\n",
        " 756: 'rain barrel',\n",
        " 757: 'recreational vehicle, RV, R.V.',\n",
        " 758: 'reel',\n",
        " 759: 'reflex camera',\n",
        " 760: 'refrigerator, icebox',\n",
        " 761: 'remote control, remote',\n",
        " 762: 'restaurant, eating house, eating place, eatery',\n",
        " 763: 'revolver, six-gun, six-shooter',\n",
        " 764: 'rifle',\n",
        " 765: 'rocking chair, rocker',\n",
        " 766: 'rotisserie',\n",
        " 767: 'rubber eraser, rubber, pencil eraser',\n",
        " 768: 'rugby ball',\n",
        " 769: 'rule, ruler',\n",
        " 770: 'running shoe',\n",
        " 771: 'safe',\n",
        " 772: 'safety pin',\n",
        " 773: 'saltshaker, salt shaker',\n",
        " 774: 'sandal',\n",
        " 775: 'sarong',\n",
        " 776: 'sax, saxophone',\n",
        " 777: 'scabbard',\n",
        " 778: 'scale, weighing machine',\n",
        " 779: 'school bus',\n",
        " 780: 'schooner',\n",
        " 781: 'scoreboard',\n",
        " 782: 'screen, CRT screen',\n",
        " 783: 'screw',\n",
        " 784: 'screwdriver',\n",
        " 785: 'seat belt, seatbelt',\n",
        " 786: 'sewing machine',\n",
        " 787: 'shield, buckler',\n",
        " 788: 'shoe shop, shoe-shop, shoe store',\n",
        " 789: 'shoji',\n",
        " 790: 'shopping basket',\n",
        " 791: 'shopping cart',\n",
        " 792: 'shovel',\n",
        " 793: 'shower cap',\n",
        " 794: 'shower curtain',\n",
        " 795: 'ski',\n",
        " 796: 'ski mask',\n",
        " 797: 'sleeping bag',\n",
        " 798: 'slide rule, slipstick',\n",
        " 799: 'sliding door',\n",
        " 800: 'slot, one-armed bandit',\n",
        " 801: 'snorkel',\n",
        " 802: 'snowmobile',\n",
        " 803: 'snowplow, snowplough',\n",
        " 804: 'soap dispenser',\n",
        " 805: 'soccer ball',\n",
        " 806: 'sock',\n",
        " 807: 'solar dish, solar collector, solar furnace',\n",
        " 808: 'sombrero',\n",
        " 809: 'soup bowl',\n",
        " 810: 'space bar',\n",
        " 811: 'space heater',\n",
        " 812: 'space shuttle',\n",
        " 813: 'spatula',\n",
        " 814: 'speedboat',\n",
        " 815: \"spider web, spider's web\",\n",
        " 816: 'spindle',\n",
        " 817: 'sports car, sport car',\n",
        " 818: 'spotlight, spot',\n",
        " 819: 'stage',\n",
        " 820: 'steam locomotive',\n",
        " 821: 'steel arch bridge',\n",
        " 822: 'steel drum',\n",
        " 823: 'stethoscope',\n",
        " 824: 'stole',\n",
        " 825: 'stone wall',\n",
        " 826: 'stopwatch, stop watch',\n",
        " 827: 'stove',\n",
        " 828: 'strainer',\n",
        " 829: 'streetcar, tram, tramcar, trolley, trolley car',\n",
        " 830: 'stretcher',\n",
        " 831: 'studio couch, day bed',\n",
        " 832: 'stupa, tope',\n",
        " 833: 'submarine, pigboat, sub, U-boat',\n",
        " 834: 'suit, suit of clothes',\n",
        " 835: 'sundial',\n",
        " 836: 'sunglass',\n",
        " 837: 'sunglasses, dark glasses, shades',\n",
        " 838: 'sunscreen, sunblock, sun blocker',\n",
        " 839: 'suspension bridge',\n",
        " 840: 'swab, swob, mop',\n",
        " 841: 'sweatshirt',\n",
        " 842: 'swimming trunks, bathing trunks',\n",
        " 843: 'swing',\n",
        " 844: 'switch, electric switch, electrical switch',\n",
        " 845: 'syringe',\n",
        " 846: 'table lamp',\n",
        " 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle',\n",
        " 848: 'tape player',\n",
        " 849: 'teapot',\n",
        " 850: 'teddy, teddy bear',\n",
        " 851: 'television, television system',\n",
        " 852: 'tennis ball',\n",
        " 853: 'thatch, thatched roof',\n",
        " 854: 'theater curtain, theatre curtain',\n",
        " 855: 'thimble',\n",
        " 856: 'thresher, thrasher, threshing machine',\n",
        " 857: 'throne',\n",
        " 858: 'tile roof',\n",
        " 859: 'toaster',\n",
        " 860: 'tobacco shop, tobacconist shop, tobacconist',\n",
        " 861: 'toilet seat',\n",
        " 862: 'torch',\n",
        " 863: 'totem pole',\n",
        " 864: 'tow truck, tow car, wrecker',\n",
        " 865: 'toyshop',\n",
        " 866: 'tractor',\n",
        " 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',\n",
        " 868: 'tray',\n",
        " 869: 'trench coat',\n",
        " 870: 'tricycle, trike, velocipede',\n",
        " 871: 'trimaran',\n",
        " 872: 'tripod',\n",
        " 873: 'triumphal arch',\n",
        " 874: 'trolleybus, trolley coach, trackless trolley',\n",
        " 875: 'trombone',\n",
        " 876: 'tub, vat',\n",
        " 877: 'turnstile',\n",
        " 878: 'typewriter keyboard',\n",
        " 879: 'umbrella',\n",
        " 880: 'unicycle, monocycle',\n",
        " 881: 'upright, upright piano',\n",
        " 882: 'vacuum, vacuum cleaner',\n",
        " 883: 'vase',\n",
        " 884: 'vault',\n",
        " 885: 'velvet',\n",
        " 886: 'vending machine',\n",
        " 887: 'vestment',\n",
        " 888: 'viaduct',\n",
        " 889: 'violin, fiddle',\n",
        " 890: 'volleyball',\n",
        " 891: 'waffle iron',\n",
        " 892: 'wall clock',\n",
        " 893: 'wallet, billfold, notecase, pocketbook',\n",
        " 894: 'wardrobe, closet, press',\n",
        " 895: 'warplane, military plane',\n",
        " 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',\n",
        " 897: 'washer, automatic washer, washing machine',\n",
        " 898: 'water bottle',\n",
        " 899: 'water jug',\n",
        " 900: 'water tower',\n",
        " 901: 'whiskey jug',\n",
        " 902: 'whistle',\n",
        " 903: 'wig',\n",
        " 904: 'window screen',\n",
        " 905: 'window shade',\n",
        " 906: 'Windsor tie',\n",
        " 907: 'wine bottle',\n",
        " 908: 'wing',\n",
        " 909: 'wok',\n",
        " 910: 'wooden spoon',\n",
        " 911: 'wool, woolen, woollen',\n",
        " 912: 'worm fence, snake fence, snake-rail fence, Virginia fence',\n",
        " 913: 'wreck',\n",
        " 914: 'yawl',\n",
        " 915: 'yurt',\n",
        " 916: 'web site, website, internet site, site',\n",
        " 917: 'comic book',\n",
        " 918: 'crossword puzzle, crossword',\n",
        " 919: 'street sign',\n",
        " 920: 'traffic light, traffic signal, stoplight',\n",
        " 921: 'book jacket, dust cover, dust jacket, dust wrapper',\n",
        " 922: 'menu',\n",
        " 923: 'plate',\n",
        " 924: 'guacamole',\n",
        " 925: 'consomme',\n",
        " 926: 'hot pot, hotpot',\n",
        " 927: 'trifle',\n",
        " 928: 'ice cream, icecream',\n",
        " 929: 'ice lolly, lolly, lollipop, popsicle',\n",
        " 930: 'French loaf',\n",
        " 931: 'bagel, beigel',\n",
        " 932: 'pretzel',\n",
        " 933: 'cheeseburger',\n",
        " 934: 'hotdog, hot dog, red hot',\n",
        " 935: 'mashed potato',\n",
        " 936: 'head cabbage',\n",
        " 937: 'broccoli',\n",
        " 938: 'cauliflower',\n",
        " 939: 'zucchini, courgette',\n",
        " 940: 'spaghetti squash',\n",
        " 941: 'acorn squash',\n",
        " 942: 'butternut squash',\n",
        " 943: 'cucumber, cuke',\n",
        " 944: 'artichoke, globe artichoke',\n",
        " 945: 'bell pepper',\n",
        " 946: 'cardoon',\n",
        " 947: 'mushroom',\n",
        " 948: 'Granny Smith',\n",
        " 949: 'strawberry',\n",
        " 950: 'orange',\n",
        " 951: 'lemon',\n",
        " 952: 'fig',\n",
        " 953: 'pineapple, ananas',\n",
        " 954: 'banana',\n",
        " 955: 'jackfruit, jak, jack',\n",
        " 956: 'custard apple',\n",
        " 957: 'pomegranate',\n",
        " 958: 'hay',\n",
        " 959: 'carbonara',\n",
        " 960: 'chocolate sauce, chocolate syrup',\n",
        " 961: 'dough',\n",
        " 962: 'meat loaf, meatloaf',\n",
        " 963: 'pizza, pizza pie',\n",
        " 964: 'potpie',\n",
        " 965: 'burrito',\n",
        " 966: 'red wine',\n",
        " 967: 'espresso',\n",
        " 968: 'cup',\n",
        " 969: 'eggnog',\n",
        " 970: 'alp',\n",
        " 971: 'bubble',\n",
        " 972: 'cliff, drop, drop-off',\n",
        " 973: 'coral reef',\n",
        " 974: 'geyser',\n",
        " 975: 'lakeside, lakeshore',\n",
        " 976: 'promontory, headland, head, foreland',\n",
        " 977: 'sandbar, sand bar',\n",
        " 978: 'seashore, coast, seacoast, sea-coast',\n",
        " 979: 'valley, vale',\n",
        " 980: 'volcano',\n",
        " 981: 'ballplayer, baseball player',\n",
        " 982: 'groom, bridegroom',\n",
        " 983: 'scuba diver',\n",
        " 984: 'rapeseed',\n",
        " 985: 'daisy',\n",
        " 986: \"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\",\n",
        " 987: 'corn',\n",
        " 988: 'acorn',\n",
        " 989: 'hip, rose hip, rosehip',\n",
        " 990: 'buckeye, horse chestnut, conker',\n",
        " 991: 'coral fungus',\n",
        " 992: 'agaric',\n",
        " 993: 'gyromitra',\n",
        " 994: 'stinkhorn, carrion fungus',\n",
        " 995: 'earthstar',\n",
        " 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',\n",
        " 997: 'bolete',\n",
        " 998: 'ear, spike, capitulum',\n",
        " 999: 'toilet tissue, toilet paper, bathroom tissue'}"
      ],
      "metadata": {
        "id": "G_3JepVlxl8e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image = load_img(\"/content/image.jpg\",target_size=(224,224))\n",
        "image = img_to_array(image)\n",
        "batch = np.expand_dims(image,axis=0)\n",
        "image = tf.keras.applications.resnet50.preprocess_input(batch)\n"
      ],
      "metadata": {
        "id": "kneep6mowoTw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(model.predict(image)[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfUzNxlkzEtU",
        "outputId": "b93cfbc7-1309-485e-f479-099cc2a34e61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dict = {}\n",
        "preds = model.predict(image)[0]\n",
        "for i in range(len(preds)):\n",
        "  num = preds[i]\n",
        "  dict[str(num)] = i\n",
        "array = preds\n",
        "array.sort()"
      ],
      "metadata": {
        "id": "Fjfp83ftyBQW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUaJlSAwy1jN",
        "outputId": "44889a2e-e0cf-4513-8082-9fadd4efc583"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'4.3777133e-09': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes[dict[str(array[999])]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "cwNr15L3xIpj",
        "outputId": "7e52c8fb-1c3f-4bb4-92ba-74a8bccac3b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'bookcase'"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, Conv2DTranspose\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from PIL import Image\n",
        "\n",
        "# Model configuration\n",
        "batch_size = 1\n",
        "no_epochs = 25\n",
        "\n",
        "image = load_img('/content/image.jpg', target_size=(224, 224))\n",
        "image = img_to_array(image)\n",
        "image = image.reshape(1, 224, 224, 3).astype('float32')\n",
        "input_shape=image.shape\n",
        "# Create the model\n",
        "model = Sequential()\n",
        "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(224, 224,3)))\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))\n",
        "model.add(Conv2D(16, kernel_size=(3, 3), activation='relu'))\n",
        "model.add(Conv2DTranspose(16, kernel_size=(3,3), activation='relu'))\n",
        "model.add(Conv2DTranspose(32, kernel_size=(3,3), activation='relu'))\n",
        "model.add(Conv2DTranspose(64, kernel_size=(3,3), activation='relu'))\n",
        "model.add(Conv2D(3, kernel_size=(3, 3), activation='sigmoid', padding='same'))\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "model.fit(image, image,epochs=no_epochs,batch_size=batch_size)\n",
        "reconstructions = model.predict(image)\n",
        "predicted_img = Image.fromarray(reconstructions.reshape(224,224,3), 'RGB')\n",
        "predicted_img.save('Reconstructed.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbiqqpxqxS8C",
        "outputId": "05d25aa4-4063-4939-be8c-98463fd0445d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "1/1 [==============================] - 2s 2s/step - loss: -831.4002\n",
            "Epoch 2/25\n",
            "1/1 [==============================] - 1s 722ms/step - loss: -3265.8062\n",
            "Epoch 3/25\n",
            "1/1 [==============================] - 1s 731ms/step - loss: -7578.2603\n",
            "Epoch 4/25\n",
            "1/1 [==============================] - 1s 710ms/step - loss: -14860.2539\n",
            "Epoch 5/25\n",
            "1/1 [==============================] - 1s 716ms/step - loss: -27312.0723\n",
            "Epoch 6/25\n",
            "1/1 [==============================] - 1s 721ms/step - loss: -47521.2031\n",
            "Epoch 7/25\n",
            "1/1 [==============================] - 1s 702ms/step - loss: -79720.6719\n",
            "Epoch 8/25\n",
            "1/1 [==============================] - 1s 723ms/step - loss: -130032.3672\n",
            "Epoch 9/25\n",
            "1/1 [==============================] - 1s 723ms/step - loss: -207682.3750\n",
            "Epoch 10/25\n",
            "1/1 [==============================] - 1s 710ms/step - loss: -328310.0312\n",
            "Epoch 11/25\n",
            "1/1 [==============================] - 1s 711ms/step - loss: -512814.0312\n",
            "Epoch 12/25\n",
            "1/1 [==============================] - 1s 717ms/step - loss: -793053.3750\n",
            "Epoch 13/25\n",
            "1/1 [==============================] - 1s 723ms/step - loss: -1224090.2500\n",
            "Epoch 14/25\n",
            "1/1 [==============================] - 1s 700ms/step - loss: -1870315.3750\n",
            "Epoch 15/25\n",
            "1/1 [==============================] - 1s 742ms/step - loss: -2832642.0000\n",
            "Epoch 16/25\n",
            "1/1 [==============================] - 1s 727ms/step - loss: -4250433.5000\n",
            "Epoch 17/25\n",
            "1/1 [==============================] - 1s 715ms/step - loss: -6309572.0000\n",
            "Epoch 18/25\n",
            "1/1 [==============================] - 1s 709ms/step - loss: -9266006.0000\n",
            "Epoch 19/25\n",
            "1/1 [==============================] - 1s 721ms/step - loss: -13475733.0000\n",
            "Epoch 20/25\n",
            "1/1 [==============================] - 1s 716ms/step - loss: -19483056.0000\n",
            "Epoch 21/25\n",
            "1/1 [==============================] - 1s 704ms/step - loss: -27944432.0000\n",
            "Epoch 22/25\n",
            "1/1 [==============================] - 1s 719ms/step - loss: -39849640.0000\n",
            "Epoch 23/25\n",
            "1/1 [==============================] - 1s 723ms/step - loss: -56481792.0000\n",
            "Epoch 24/25\n",
            "1/1 [==============================] - 1s 714ms/step - loss: -79504040.0000\n",
            "Epoch 25/25\n",
            "1/1 [==============================] - 1s 719ms/step - loss: -111228072.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(Reconstructed.png)"
      ],
      "metadata": {
        "id": "ZsM3I19_3VZp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}