stylesage-notebooks/keras_viz-stylesage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"   \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "import tensorflow as tf\n",
    "tf.set_random_seed(2)\n",
    "config = tf.ConfigProto(\n",
    "    intra_op_parallelism_threads=1,\n",
    "    inter_op_parallelism_threads=1 \n",
    ")\n",
    "config.gpu_options.allow_growth = True  # dynamically grow the memory used on the GPU\n",
    "sess = tf.Session(graph= tf.get_default_graph(), config=config) \n",
    "import keras \n",
    "from keras import backend as K\n",
    "K.set_session(sess)  # set this TensorFlow session as the default session for Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAD KERAS PRETRAINED MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /data/xai/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /data/xai/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4074: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /data/xai/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Model: \"model_1\"\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",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 512)          1049088     avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 7)            3591        dense_1[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 22,855,463\n",
      "Trainable params: 16,004,615\n",
      "Non-trainable params: 6,850,848\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model = load_model('finetune.model')\n",
    "model.load_weights('weights.best.hdf5')\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAD AND PREPROCESS IMAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image, ImageDraw\n",
    "from keras_preprocessing.image import img_to_array\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_input_inceptionv3(x):\n",
    "    x /= 255.\n",
    "    x -= 0.5\n",
    "    x *= 2.\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image(path): \n",
    "  img = Image.open(path)\n",
    "  img = np.array(img)\n",
    "  x = img_to_array(img)\n",
    "  x = preprocess_input_inceptionv3(x)\n",
    "\n",
    "  return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = {\n",
    "    0: '1886 - Accessories', \n",
    "    1: '1887 - Swimwear', \n",
    "    2: '1888 - Dresses',\n",
    "    3: '1889 - Upper part', \n",
    "    4: '1890 - Shoes', \n",
    "    5: '1891 - Bottom part', \n",
    "    6: '1892 - Underwear'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "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 os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "x = load_image('images/1888/c29977b941a2168c312319e55907028a3dc1fb45.jpg')\n",
    "y_pred = model.predict(x.reshape(1,299,299,3))\n",
    "class_idx = np.argmax(y_pred)\n",
    "\n",
    "plt.figure()\n",
    "plt.title(f'Predicted: {class_idx} - {class_names[int(class_idx)]}')\n",
    "plt.imshow(x)\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KERAS-VIZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vis.losses import ActivationMaximization\n",
    "from vis.regularizers import TotalVariation, LPNorm\n",
    "from vis.input_modifiers import Jitter\n",
    "from vis.optimizer import Optimizer\n",
    "from vis.callbacks import GifGenerator\n",
    "from vis.visualization import overlay, visualize_activation, visualize_cam_with_losses\n",
    "from vis.utils import utils\n",
    "from keras import activations\n",
    "from keras.layers.wrappers import Wrapper\n",
    "from keras.layers.convolutional import _Conv\n",
    "from keras.layers.pooling import _Pooling1D, _Pooling2D, _Pooling3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utility to search for layer index by name. \n",
    "# Alternatively we can specify this as -1 since it corresponds to the last layer.\n",
    "layer_idx = utils.find_layer_idx(model, 'conv2d_94')\n",
    "\n",
    "# Swap softmax with linear\n",
    "model.layers[layer_idx].activation = keras.activations.linear\n",
    "model = utils.apply_modifications(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utility to search for layer index by name. \n",
    "# Alternatively we can specify this as -1 since it corresponds to the last layer.\n",
    "layer_idx = utils.find_layer_idx(model, 'dense_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_map(grads):\n",
    "    fig, axes = plt.subplots(1,2,figsize=(14,5))\n",
    "    axes[0].imshow(x)\n",
    "    axes[1].imshow(x)\n",
    "    i = axes[1].imshow(grads,cmap=\"jet\",alpha=0.8)\n",
    "    fig.colorbar(i)\n",
    "    plt.suptitle(\"Pr(class={}) = {:5.2f}\".format(\n",
    "                      class_names[class_idx],\n",
    "                      y_pred[0,class_idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SALIENCY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vis.visualization import visualize_saliency\n",
    "\n",
    "grads = visualize_saliency(model, layer_idx, filter_indices=class_idx, seed_input=x.reshape(299,299,3))\n",
    "\n",
    "plot_map(grads)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GRADCAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import zoom\n",
    "\n",
    "def visualize_cam_with_losses(input_tensor, losses, seed_input, penultimate_layer, grad_modifier=None):\n",
    "    penultimate_output = penultimate_layer.output\n",
    "    opt = Optimizer(input_tensor, losses, wrt_tensor=penultimate_output, norm_grads=False)\n",
    "    _, grads, penultimate_output_value = opt.minimize(seed_input, max_iter=1, grad_modifier=grad_modifier, verbose=False)\n",
    "\n",
    "    # For numerical stability. Very small grad values along with small penultimate_output_value can cause\n",
    "    # w * penultimate_output_value to zero out, even for reasonable fp precision of float32.\n",
    "    grads = grads / (np.max(grads) + K.epsilon())\n",
    "\n",
    "    # Average pooling across all feature maps.\n",
    "    # This captures the importance of feature map (channel) idx to the output.\n",
    "    channel_idx = 1 if K.image_data_format() == 'channels_first' else -1\n",
    "    other_axis = np.delete(np.arange(len(grads.shape)), channel_idx)\n",
    "    weights = np.mean(grads, axis=tuple(other_axis))\n",
    "\n",
    "    # Generate heatmap by computing weight * output over feature maps\n",
    "    output_dims = utils.get_img_shape(penultimate_output_value)[2:]\n",
    "    heatmap = np.zeros(shape=output_dims, dtype=K.floatx())\n",
    "    for i, w in enumerate(weights):\n",
    "        if channel_idx == -1:\n",
    "            heatmap += w * penultimate_output_value[0, ..., i]\n",
    "        else:\n",
    "            heatmap += w * penultimate_output_value[0, i, ...]\n",
    "\n",
    "    # ReLU thresholding to exclude pattern mismatch information (negative gradients).\n",
    "    heatmap = np.maximum(heatmap, 0)\n",
    "\n",
    "    # The penultimate feature map size is definitely smaller than input image.\n",
    "    input_dims = 299, 299\n",
    "\n",
    "    # Figure out the zoom factor.\n",
    "    zoom_factor = [i / (j * 1.0) for i, j in iter(zip(input_dims, output_dims))]\n",
    "    heatmap = zoom(heatmap, zoom_factor)\n",
    "    return utils.normalize(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _find_penultimate_layer(model, layer_idx, penultimate_layer_idx):\n",
    "    \"\"\"Searches for the nearest penultimate `Conv` or `Pooling` layer.\n",
    "    Args:\n",
    "        model: The `keras.models.Model` instance.\n",
    "        layer_idx: The layer index within `model.layers`.\n",
    "        penultimate_layer_idx: The pre-layer to `layer_idx`. If set to None, the nearest penultimate\n",
    "            `Conv` or `Pooling` layer is used.\n",
    "    Returns:\n",
    "        The penultimate layer.\n",
    "    \"\"\"\n",
    "    if penultimate_layer_idx is None:\n",
    "        for idx, layer in utils.reverse_enumerate(model.layers[:layer_idx - 1]):\n",
    "            if isinstance(layer, Wrapper):\n",
    "                layer = layer.layer\n",
    "            if isinstance(layer, (_Conv, _Pooling1D, _Pooling2D, _Pooling3D)):\n",
    "                penultimate_layer_idx = idx\n",
    "                break\n",
    "\n",
    "    if penultimate_layer_idx is None:\n",
    "        raise ValueError('Unable to determine penultimate `Conv` or `Pooling` '\n",
    "                         'layer for layer_idx: {}'.format(layer_idx))\n",
    "\n",
    "    # Handle negative indexing otherwise the next check can fail.\n",
    "    if layer_idx < 0:\n",
    "        layer_idx = len(model.layers) + layer_idx\n",
    "    if penultimate_layer_idx > layer_idx:\n",
    "        raise ValueError('`penultimate_layer_idx` needs to be before `layer_idx`')\n",
    "\n",
    "    return model.layers[penultimate_layer_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "penultimate_layer = _find_penultimate_layer(model, layer_idx, None)\n",
    "\n",
    "# `ActivationMaximization` outputs negative gradient values for increase in activations. Multiply with -1\n",
    "# so that positive gradients indicate increase instead.\n",
    "losses = [\n",
    "    (ActivationMaximization(model.layers[layer_idx], class_idx), -1)\n",
    "]\n",
    "\n",
    "grads = visualize_cam_with_losses(model.input, losses, x, penultimate_layer, None)\n",
    "\n",
    "plot_map(grads)"
   ]
  }
 ],
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