ImplementingDeepDream.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.applications import inception_v3\n",
    "from keras import backend as K\n",
    "K.set_learning_phase(0)\n",
    "\n",
    "model = inception_v3.InceptionV3(weights = 'imagenet' , include_top = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"inception_v3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, None, 3 9216        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, None, 3 96          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, None, 3 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, None, 6 18432       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, None, 6 192         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None, 6 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, None, 8 5120        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, None, 8 240         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, None, 8 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, None, 1 138240      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, None, 1 576         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, None, 1 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 1 0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, None, None, 6 192         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, None, 6 0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, None, None, 9 55296       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, None, 4 144         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, None, 4 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, None, None, 1 0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, None, 6 76800       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, None, None, 9 82944       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, None, None, 3 6144        average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, None, 6 192         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, None, 6 192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, None, None, 9 288         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, None, None, 3 96          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, None, 6 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, None, None, 9 0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, None, None, 3 0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, None, None, 2 0           activation_6[0][0]               \n",
      "                                                                 activation_8[0][0]               \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, None, None, 6 192         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, None, None, 6 0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, None, None, 4 12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, None, None, 9 55296       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, None, None, 4 144         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, None, None, 9 288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, None, None, 4 0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, None, None, 9 0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, None, None, 2 0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, None, None, 6 76800       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, None, None, 9 82944       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, None, None, 6 16384       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, None, None, 6 192         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, None, None, 6 192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, None, None, 9 288         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, None, None, 6 192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, None, None, 6 0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, None, None, 6 0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, None, None, 9 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, None, None, 6 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, None, None, 2 0           activation_13[0][0]              \n",
      "                                                                 activation_15[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, None, None, 6 192         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, None, None, 6 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, None, None, 4 13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, None, None, 9 55296       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, None, None, 4 144         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, None, None, 9 288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, None, None, 4 0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, None, None, 9 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, None, None, 2 0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, None, None, 6 76800       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, None, None, 9 82944       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, None, None, 6 18432       average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, None, None, 6 192         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, None, None, 6 192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, None, None, 9 288         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, None, None, 6 192         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, None, None, 6 0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, None, None, 6 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, None, None, 9 0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, None, None, 6 0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, None, None, 2 0           activation_20[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, None, None, 6 18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, None, None, 6 192         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, None, None, 6 0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, None, None, 9 55296       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, None, None, 9 288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, None, None, 9 0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, None, None, 3 995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, None, None, 9 82944       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, None, None, 3 1152        conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, None, None, 9 288         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, None, None, 3 0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, None, None, 9 0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, None, None, 7 0           activation_27[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, None, None, 1 384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, None, None, 1 0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, None, None, 1 114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, None, None, 1 384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, None, None, 1 0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, None, None, 1 114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, None, None, 1 384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, None, None, 1 384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, None, None, 1 0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, None, None, 1 0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, None, None, 1 114688      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, None, None, 1 114688      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, None, None, 1 384         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, None, None, 1 384         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, None, None, 1 0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, None, None, 1 0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, None, None, 7 0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, None, None, 1 147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, None, None, 1 172032      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, None, None, 1 172032      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, None, None, 1 576         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, None, None, 1 576         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, None, None, 1 576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, None, None, 1 576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, None, None, 1 0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, None, None, 1 0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, None, None, 1 0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, None, None, 1 0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, None, None, 7 0           activation_31[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "                                                                 activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, None, None, 1 480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, None, None, 1 0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, None, None, 1 179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, None, None, 1 480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, None, None, 1 179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, None, None, 1 480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, None, None, 1 480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, None, None, 1 0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, None, None, 1 0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, None, None, 1 179200      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, None, None, 1 179200      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, None, None, 1 480         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, None, None, 1 480         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, None, None, 1 0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, None, None, 7 0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, None, None, 1 147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, None, None, 1 215040      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, None, None, 1 215040      activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, None, None, 1 576         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, None, None, 1 576         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, None, None, 1 576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, None, None, 1 576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, None, None, 1 0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, None, None, 1 0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, None, None, 7 0           activation_41[0][0]              \n",
      "                                                                 activation_44[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "                                                                 activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, None, None, 1 480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, None, None, 1 0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, None, None, 1 179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, None, None, 1 480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, None, None, 1 179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, None, None, 1 480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, None, None, 1 480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, None, None, 1 0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, None, None, 1 179200      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, None, None, 1 179200      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, None, None, 1 480         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, None, None, 1 480         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, None, None, 7 0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, None, None, 1 147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, None, None, 1 215040      activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, None, None, 1 215040      activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, None, None, 1 576         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, None, None, 1 576         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, None, None, 1 576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, None, None, 1 576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, None, None, 1 0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, None, None, 1 0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, None, None, 7 0           activation_51[0][0]              \n",
      "                                                                 activation_54[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "                                                                 activation_60[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, None, None, 1 576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, None, None, 1 0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, None, None, 1 258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, None, None, 1 576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, None, None, 1 258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, None, None, 1 576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, None, None, 1 576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, None, None, 1 0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, None, None, 1 258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, None, None, 1 258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, None, None, 1 576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, None, None, 1 576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, None, None, 1 0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, None, None, 1 0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, None, None, 7 0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, None, None, 1 258048      activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, None, None, 1 258048      activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, None, None, 1 576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, None, None, 1 576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, None, None, 1 576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, None, None, 1 576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, None, None, 1 0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, None, None, 1 0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, None, None, 1 0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, None, None, 7 0           activation_61[0][0]              \n",
      "                                                                 activation_64[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "                                                                 activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, None, None, 1 576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, None, None, 1 0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, None, None, 1 258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, None, None, 1 576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, None, None, 1 0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, None, None, 1 258048      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, None, None, 1 576         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, None, None, 1 576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, None, None, 1 0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, None, None, 1 0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, None, None, 3 552960      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, None, None, 1 331776      activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, None, None, 3 960         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, None, None, 1 576         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, None, None, 3 0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, None, None, 1 0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, None, None, 1 0           activation_72[0][0]              \n",
      "                                                                 activation_76[0][0]              \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, None, None, 4 573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, None, None, 4 1344        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, None, None, 4 0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, None, None, 3 491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, None, None, 3 1548288     activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, None, None, 3 1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, None, None, 3 1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, None, None, 3 0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, None, None, 3 0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, None, None, 1 0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, None, None, 3 409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, None, None, 3 1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, None, None, 3 1152        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, None, None, 3 1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, None, None, 3 1152        conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, None, None, 1 245760      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, None, None, 3 960         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, None, None, 3 0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, None, None, 3 0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, None, None, 3 0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, None, None, 3 0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, None, None, 1 576         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, None, None, 3 0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_79[0][0]              \n",
      "                                                                 activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 7 0           activation_83[0][0]              \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, None, None, 1 0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, None, None, 2 0           activation_77[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_85[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, None, None, 4 917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, None, None, 4 1344        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, None, None, 4 0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, None, None, 3 786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, None, None, 3 1548288     activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, None, None, 3 1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, None, None, 3 1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, None, None, 3 0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, None, None, 3 0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, None, None, 2 0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, None, None, 3 655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, None, None, 3 1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, None, None, 3 1152        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, None, None, 3 1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, None, None, 3 1152        conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, None, None, 1 393216      average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, None, None, 3 960         conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, None, None, 3 0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, None, None, 3 0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, None, None, 3 0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, None, None, 3 0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, None, None, 1 576         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, None, None, 3 0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_88[0][0]              \n",
      "                                                                 activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, None, None, 7 0           activation_92[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, None, None, 1 0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, None, None, 2 0           activation_86[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_2[0][0]              \n",
      "                                                                 activation_94[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 21,802,784\n",
      "Trainable params: 21,768,352\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_contributions = {\n",
    "    'mixed2':0.2,\n",
    "    'mixed3':3.,\n",
    "    'mixed4':2.,\n",
    "    'mixed5':1.5\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_dictionary = {layer.name : layer for layer in model.layers}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = K.variable(0.0)\n",
    "for lname in layer_contributions:\n",
    "    coeff = layer_contributions[lname]\n",
    "    activation = layer_dictionary[lname].output\n",
    "    \n",
    "    scaling = K.prod(K.cast(K.shape(activation),'float32'))\n",
    "    loss = loss + coeff*K.sum(K.square(activation[:,2:-2,2:-2,:]))/scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepdream = model.input\n",
    "\n",
    "grads = K.gradients(loss,deepdream)[0]\n",
    "\n",
    "grads /= K.maximum( K.mean( K.abs(grads) ), 1e-7 ) \n",
    "\n",
    "outputs = [ loss , grads ]\n",
    "fetch_loss_and_grads = K.function( [deepdream] , outputs )\n",
    "\n",
    "def eval_loss_and_grads(x):\n",
    "    outs = fetch_loss_and_grads([x])\n",
    "    loss_value = outs[0]\n",
    "    grad_values = outs[1]\n",
    "    return loss_value,grad_values\n",
    "\n",
    "def gradient_ascent(x , iters , step , max_loss = None):\n",
    "    for i in range(iters):\n",
    "        loss_value,grad_values = eval_loss_and_grads(x)\n",
    "        if max_loss is not None and loss_value > max_loss:\n",
    "            break\n",
    "        print(f\" Loss value at {i} is {loss_value} \")\n",
    "        x += step*grad_values\n",
    "    return x    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import scipy.misc\n",
    "from keras.preprocessing import image\n",
    "import imageio\n",
    "\n",
    "def resize_img(img,size):\n",
    "    img = np.copy(img)\n",
    "    factors = (\n",
    "                1,\n",
    "                float(size[0])/img.shape[1],\n",
    "                float(size[1])/img.shape[2],\n",
    "                1\n",
    "                )\n",
    "    return scipy.ndimage.zoom(img,factors,order = 1)\n",
    "\n",
    "def save_img(img,fname):\n",
    "    pil_img = deprocess_image(np.copy(img))\n",
    "#     scipy.misc.imsave(fname,pil_img)\n",
    "    imageio.imwrite(fname, pil_img)\n",
    "    \n",
    "def preprocess_image(image_path):\n",
    "    img = image.load_img(image_path)\n",
    "    img = image.img_to_array(img)\n",
    "    img = np.expand_dims(img,axis = 0)\n",
    "    img = inception_v3.preprocess_input(img)\n",
    "    return img\n",
    "\n",
    "def deprocess_image(x):\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        x = x.reshape((3,x.shape[2],x.shape[3]))\n",
    "        x = x.transpose((1,2,0))\n",
    "    else:\n",
    "        x = x.reshape((x.shape[1],x.shape[2],3))\n",
    "    x /= 2\n",
    "    x += 0.5\n",
    "    x *= 255\n",
    "    x = np.clip(x,0,255).astype('uint8')\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints',\n",
       " '047.jpg',\n",
       " '048.jpg',\n",
       " '1262.jpg',\n",
       " '1355.jpg',\n",
       " '740_1375_compressed_Chicago_Crimes_2001_to_2004.csv.zip',\n",
       " '740_1375_compressed_Chicago_Crimes_2005_to_2007.csv.zip',\n",
       " '740_1375_compressed_Chicago_Crimes_2008_to_2011.csv.zip',\n",
       " '894d5a756219106010319ac9e3555d75.jpg',\n",
       " 'aclImdb',\n",
       " 'aclImdb.zip',\n",
       " 'Avocado Price Prediction.ipynb',\n",
       " 'avocado.csv',\n",
       " 'CaliforniPricePrediction.ipynb',\n",
       " 'Car Price Prediction.ipynb',\n",
       " 'Car_Purchasing_Data.csv',\n",
       " 'CharacterLevelLSTMTextGeneration.ipynb',\n",
       " 'CharacterLevelLSTMTextGeneration2.ipynb',\n",
       " 'ChicagoCrimeRatePredictions.ipynb',\n",
       " 'Chicago_Crimes_2001_to_2004.csv',\n",
       " 'Chicago_Crimes_2005_to_2007.csv',\n",
       " 'Chicago_Crimes_2008_to_2011.csv',\n",
       " 'cifar-10-python.tar.gz',\n",
       " 'CIFAR10.ipynb',\n",
       " 'cifar10_model_1.h5',\n",
       " 'Codalab',\n",
       " 'datasets',\n",
       " 'EmailForecasting.ipynb',\n",
       " 'emails.csv',\n",
       " 'glove.6B.zip',\n",
       " 'gloveDir',\n",
       " 'Hackerearth',\n",
       " 'ImplementingDeepDream.ipynb',\n",
       " 'jena_climate_2009_2016.csv',\n",
       " 'jena_climate_2009_2016.csv.zip',\n",
       " 'Kaggle',\n",
       " 'MNIST Dataset classification.ipynb',\n",
       " 'Movie_Id_Titles.csv',\n",
       " 'NLPBasicsWithKeras.ipynb',\n",
       " 'pre_trained_glove_model.h5',\n",
       " 'TemperatureForecasting.ipynb',\n",
       " 'traffic-signs-data',\n",
       " 'TrafficSignClassification.ipynb',\n",
       " 'u.data',\n",
       " 'User Based Collaborative Filtering.ipynb',\n",
       " 'yelp.csv',\n",
       " 'YelpReviews.ipynb']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "currdir = os.getcwd()\n",
    "\n",
    "os.listdir(currdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_image_path = os.path.join(currdir,'1355.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing image shape  (244, 183)\n",
      " Loss value at 0 is 1.8607988357543945 \n",
      " Loss value at 1 is 2.364619731903076 \n",
      " Loss value at 2 is 3.161116600036621 \n",
      " Loss value at 3 is 3.767132520675659 \n",
      " Loss value at 4 is 4.366612434387207 \n",
      " Loss value at 5 is 4.904202461242676 \n",
      " Loss value at 6 is 5.37113618850708 \n",
      " Loss value at 7 is 5.883158206939697 \n",
      " Loss value at 8 is 6.358800888061523 \n",
      " Loss value at 9 is 6.784440040588379 \n",
      " Loss value at 10 is 7.179477214813232 \n",
      " Loss value at 11 is 7.569149017333984 \n",
      " Loss value at 12 is 7.906272888183594 \n",
      " Loss value at 13 is 8.300115585327148 \n",
      " Loss value at 14 is 8.68493366241455 \n",
      " Loss value at 15 is 9.06418228149414 \n",
      " Loss value at 16 is 9.510379791259766 \n",
      " Loss value at 17 is 9.804475784301758 \n",
      "Processing image shape  (342, 257)\n",
      " Loss value at 0 is 2.9567482471466064 \n",
      " Loss value at 1 is 4.417728900909424 \n",
      " Loss value at 2 is 5.620589256286621 \n",
      " Loss value at 3 is 6.525821685791016 \n",
      " Loss value at 4 is 7.340308666229248 \n",
      " Loss value at 5 is 8.105186462402344 \n",
      " Loss value at 6 is 8.803088188171387 \n",
      " Loss value at 7 is 9.426265716552734 \n",
      "Processing image shape  (480, 360)\n",
      " Loss value at 0 is 3.127253293991089 \n",
      " Loss value at 1 is 4.384251594543457 \n",
      " Loss value at 2 is 5.515622615814209 \n",
      " Loss value at 3 is 6.524349212646484 \n",
      " Loss value at 4 is 7.411185264587402 \n",
      " Loss value at 5 is 8.257196426391602 \n",
      " Loss value at 6 is 9.057848930358887 \n",
      " Loss value at 7 is 9.780635833740234 \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "step = 0.01\n",
    "num_octave = 3\n",
    "octave_scale = 1.4\n",
    "iters = 20\n",
    "max_loss = 10.\n",
    "\n",
    "img = preprocess_image(base_image_path)\n",
    "original_shape = img.shape[1:3]\n",
    "successive_shapes = [original_shape]\n",
    "for i in range(1,num_octave):\n",
    "    shape = tuple([int(dim/(octave_scale)**i) for dim in original_shape])\n",
    "    successive_shapes.append(shape)\n",
    "\n",
    "successive_shapes = successive_shapes[::-1]\n",
    "\n",
    "original_img = np.copy(img)\n",
    "shrunk_original_img = resize_img(img,successive_shapes[0])\n",
    "\n",
    "for shape in successive_shapes:\n",
    "    print('Processing image shape ',shape)\n",
    "    img = resize_img(img,shape)\n",
    "    img = gradient_ascent(img,iters,step,max_loss)\n",
    "    upscaled_shrunk_original_img = resize_img(shrunk_original_img,shape)\n",
    "    same_size_original = resize_img(original_img,shape)\n",
    "    lost_detail = same_size_original - upscaled_shrunk_original_img\n",
    "    \n",
    "    img += lost_detail\n",
    "    shrunk_original_img = resize_img(original_img,shape)\n",
    "    save_img(img,fname = 'dream_at_scale_'+str(shape) + '.png')\n",
    "    \n",
    "save_img(img,fname = 'final_dream.png')\n",
    "# import numpy as np\n",
    "\n",
    "# # Playing with these hyperparameters will also allow you to achieve new effects\n",
    "\n",
    "# step = 0.01  # Gradient ascent step size\n",
    "# num_octave = 3  # Number of scales at which to run gradient ascent\n",
    "# octave_scale = 1.4  # Size ratio between scales\n",
    "# iterations = 20  # Number of ascent steps per scale\n",
    "\n",
    "# # If our loss gets larger than 10,\n",
    "# # we will interrupt the gradient ascent process, to avoid ugly artifacts\n",
    "# max_loss = 10.\n",
    "\n",
    "# # Fill this to the path to the image you want to use\n",
    "\n",
    "# # Load the image into a Numpy array\n",
    "# img = preprocess_image(base_image_path)\n",
    "\n",
    "# # We prepare a list of shape tuples\n",
    "# # defining the different scales at which we will run gradient ascent\n",
    "# original_shape = img.shape[1:3]\n",
    "# successive_shapes = [original_shape]\n",
    "# for i in range(1, num_octave):\n",
    "#     shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])\n",
    "#     successive_shapes.append(shape)\n",
    "\n",
    "# # Reverse list of shapes, so that they are in increasing order\n",
    "# successive_shapes = successive_shapes[::-1]\n",
    "\n",
    "# # Resize the Numpy array of the image to our smallest scale\n",
    "# original_img = np.copy(img)\n",
    "# shrunk_original_img = resize_img(img, successive_shapes[0])\n",
    "\n",
    "# for shape in successive_shapes:\n",
    "#     print('Processing image shape', shape)\n",
    "#     img = resize_img(img, shape)\n",
    "#     img = gradient_ascent(img,\n",
    "#                           iterations,\n",
    "#                           step,\n",
    "#                           max_loss)\n",
    "#     upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)\n",
    "#     same_size_original = resize_img(original_img, shape)\n",
    "#     lost_detail = same_size_original - upscaled_shrunk_original_img\n",
    "\n",
    "#     img += lost_detail\n",
    "#     shrunk_original_img = resize_img(original_img, shape)\n",
    "#     save_img(img, fname='dream_at_scale_' + str(shape) + '.png')\n",
    "\n",
    "# save_img(img, fname='final_dream.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f67a91c4a8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.imshow(deprocess_image(np.copy(img)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}