cs395proj1_dev_work.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model and Show its Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\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, 32 864         input_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNorm (None, None, None, 32 96          conv2d_1[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "activation_1 (Activation)        (None, None, None, 32 0           batch_normalization_1[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)                (None, None, None, 32 9216        activation_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNorm (None, None, None, 32 96          conv2d_2[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "activation_2 (Activation)        (None, None, None, 32 0           batch_normalization_2[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)                (None, None, None, 64 18432       activation_2[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNorm (None, None, None, 64 192         conv2d_3[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "activation_3 (Activation)        (None, None, None, 64 0           batch_normalization_3[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)   (None, None, None, 64 0           activation_3[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)                (None, None, None, 80 5120        max_pooling2d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNorm (None, None, None, 80 240         conv2d_4[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "activation_4 (Activation)        (None, None, None, 80 0           batch_normalization_4[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)                (None, None, None, 19 138240      activation_4[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNorm (None, None, None, 19 576         conv2d_5[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "activation_5 (Activation)        (None, None, None, 19 0           batch_normalization_5[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)   (None, None, None, 19 0           activation_5[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)                (None, None, None, 64 12288       max_pooling2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNorm (None, None, None, 64 192         conv2d_9[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "activation_9 (Activation)        (None, None, None, 64 0           batch_normalization_9[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)                (None, None, None, 48 9216        max_pooling2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)               (None, None, None, 96 55296       activation_9[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNorm (None, None, None, 48 144         conv2d_7[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNor (None, None, None, 96 288         conv2d_10[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_7 (Activation)        (None, None, None, 48 0           batch_normalization_7[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "activation_10 (Activation)       (None, None, None, 96 0           batch_normalization_10[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePool (None, None, None, 19 0           max_pooling2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)                (None, None, None, 64 12288       max_pooling2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)                (None, None, None, 64 76800       activation_7[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)               (None, None, None, 96 82944       activation_10[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)               (None, None, None, 32 6144        average_pooling2d_1[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNorm (None, None, None, 64 192         conv2d_6[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNorm (None, None, None, 64 192         conv2d_8[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNor (None, None, None, 96 288         conv2d_11[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNor (None, None, None, 32 96          conv2d_12[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_6 (Activation)        (None, None, None, 64 0           batch_normalization_6[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "activation_8 (Activation)        (None, None, None, 64 0           batch_normalization_8[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "activation_11 (Activation)       (None, None, None, 96 0           batch_normalization_11[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_12 (Activation)       (None, None, None, 32 0           batch_normalization_12[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)             (None, None, None, 25 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, 64 16384       mixed0[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNor (None, None, None, 64 192         conv2d_16[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_16 (Activation)       (None, None, None, 64 0           batch_normalization_16[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)               (None, None, None, 48 12288       mixed0[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)               (None, None, None, 96 55296       activation_16[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNor (None, None, None, 48 144         conv2d_14[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNor (None, None, None, 96 288         conv2d_17[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_14 (Activation)       (None, None, None, 48 0           batch_normalization_14[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_17 (Activation)       (None, None, None, 96 0           batch_normalization_17[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePool (None, None, None, 25 0           mixed0[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)               (None, None, None, 64 16384       mixed0[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)               (None, None, None, 64 76800       activation_14[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)               (None, None, None, 96 82944       activation_17[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)               (None, None, None, 64 16384       average_pooling2d_2[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNor (None, None, None, 64 192         conv2d_13[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNor (None, None, None, 64 192         conv2d_15[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNor (None, None, None, 96 288         conv2d_18[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNor (None, None, None, 64 192         conv2d_19[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_13 (Activation)       (None, None, None, 64 0           batch_normalization_13[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_15 (Activation)       (None, None, None, 64 0           batch_normalization_15[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_18 (Activation)       (None, None, None, 96 0           batch_normalization_18[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_19 (Activation)       (None, None, None, 64 0           batch_normalization_19[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)             (None, None, None, 28 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, 64 18432       mixed1[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNor (None, None, None, 64 192         conv2d_23[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_23 (Activation)       (None, None, None, 64 0           batch_normalization_23[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)               (None, None, None, 48 13824       mixed1[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)               (None, None, None, 96 55296       activation_23[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNor (None, None, None, 48 144         conv2d_21[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNor (None, None, None, 96 288         conv2d_24[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_21 (Activation)       (None, None, None, 48 0           batch_normalization_21[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_24 (Activation)       (None, None, None, 96 0           batch_normalization_24[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePool (None, None, None, 28 0           mixed1[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)               (None, None, None, 64 18432       mixed1[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)               (None, None, None, 64 76800       activation_21[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)               (None, None, None, 96 82944       activation_24[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)               (None, None, None, 64 18432       average_pooling2d_3[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNor (None, None, None, 64 192         conv2d_20[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNor (None, None, None, 64 192         conv2d_22[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNor (None, None, None, 96 288         conv2d_25[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNor (None, None, None, 64 192         conv2d_26[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_20 (Activation)       (None, None, None, 64 0           batch_normalization_20[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_22 (Activation)       (None, None, None, 64 0           batch_normalization_22[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_25 (Activation)       (None, None, None, 96 0           batch_normalization_25[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_26 (Activation)       (None, None, None, 64 0           batch_normalization_26[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)             (None, None, None, 28 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, 64 18432       mixed2[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNor (None, None, None, 64 192         conv2d_28[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_28 (Activation)       (None, None, None, 64 0           batch_normalization_28[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)               (None, None, None, 96 55296       activation_28[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNor (None, None, None, 96 288         conv2d_29[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_29 (Activation)       (None, None, None, 96 0           batch_normalization_29[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)               (None, None, None, 38 995328      mixed2[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)               (None, None, None, 96 82944       activation_29[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNor (None, None, None, 38 1152        conv2d_27[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNor (None, None, None, 96 288         conv2d_30[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_27 (Activation)       (None, None, None, 38 0           batch_normalization_27[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_30 (Activation)       (None, None, None, 96 0           batch_normalization_30[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)   (None, None, None, 28 0           mixed2[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)             (None, None, None, 76 0           activation_27[0][0]              \n",
      "                                                                   activation_30[0][0]              \n",
      "                                                                   max_pooling2d_3[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)               (None, None, None, 12 98304       mixed3[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNor (None, None, None, 12 384         conv2d_35[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_35 (Activation)       (None, None, None, 12 0           batch_normalization_35[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)               (None, None, None, 12 114688      activation_35[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNor (None, None, None, 12 384         conv2d_36[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_36 (Activation)       (None, None, None, 12 0           batch_normalization_36[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)               (None, None, None, 12 98304       mixed3[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)               (None, None, None, 12 114688      activation_36[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNor (None, None, None, 12 384         conv2d_32[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNor (None, None, None, 12 384         conv2d_37[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_32 (Activation)       (None, None, None, 12 0           batch_normalization_32[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_37 (Activation)       (None, None, None, 12 0           batch_normalization_37[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)               (None, None, None, 12 114688      activation_32[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)               (None, None, None, 12 114688      activation_37[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNor (None, None, None, 12 384         conv2d_33[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNor (None, None, None, 12 384         conv2d_38[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_33 (Activation)       (None, None, None, 12 0           batch_normalization_33[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_38 (Activation)       (None, None, None, 12 0           batch_normalization_38[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePool (None, None, None, 76 0           mixed3[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)               (None, None, None, 19 147456      mixed3[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)               (None, None, None, 19 172032      activation_33[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)               (None, None, None, 19 172032      activation_38[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)               (None, None, None, 19 147456      average_pooling2d_4[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNor (None, None, None, 19 576         conv2d_31[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNor (None, None, None, 19 576         conv2d_34[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNor (None, None, None, 19 576         conv2d_39[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNor (None, None, None, 19 576         conv2d_40[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_31 (Activation)       (None, None, None, 19 0           batch_normalization_31[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_34 (Activation)       (None, None, None, 19 0           batch_normalization_34[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_39 (Activation)       (None, None, None, 19 0           batch_normalization_39[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_40 (Activation)       (None, None, None, 19 0           batch_normalization_40[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)             (None, None, None, 76 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, 16 122880      mixed4[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNor (None, None, None, 16 480         conv2d_45[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_45 (Activation)       (None, None, None, 16 0           batch_normalization_45[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)               (None, None, None, 16 179200      activation_45[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNor (None, None, None, 16 480         conv2d_46[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_46 (Activation)       (None, None, None, 16 0           batch_normalization_46[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)               (None, None, None, 16 122880      mixed4[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)               (None, None, None, 16 179200      activation_46[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNor (None, None, None, 16 480         conv2d_42[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNor (None, None, None, 16 480         conv2d_47[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_42 (Activation)       (None, None, None, 16 0           batch_normalization_42[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_47 (Activation)       (None, None, None, 16 0           batch_normalization_47[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)               (None, None, None, 16 179200      activation_42[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)               (None, None, None, 16 179200      activation_47[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNor (None, None, None, 16 480         conv2d_43[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNor (None, None, None, 16 480         conv2d_48[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_43 (Activation)       (None, None, None, 16 0           batch_normalization_43[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_48 (Activation)       (None, None, None, 16 0           batch_normalization_48[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePool (None, None, None, 76 0           mixed4[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)               (None, None, None, 19 147456      mixed4[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)               (None, None, None, 19 215040      activation_43[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)               (None, None, None, 19 215040      activation_48[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)               (None, None, None, 19 147456      average_pooling2d_5[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNor (None, None, None, 19 576         conv2d_41[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNor (None, None, None, 19 576         conv2d_44[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNor (None, None, None, 19 576         conv2d_49[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNor (None, None, None, 19 576         conv2d_50[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_41 (Activation)       (None, None, None, 19 0           batch_normalization_41[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_44 (Activation)       (None, None, None, 19 0           batch_normalization_44[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_49 (Activation)       (None, None, None, 19 0           batch_normalization_49[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_50 (Activation)       (None, None, None, 19 0           batch_normalization_50[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)             (None, None, None, 76 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, 16 122880      mixed5[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNor (None, None, None, 16 480         conv2d_55[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_55 (Activation)       (None, None, None, 16 0           batch_normalization_55[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)               (None, None, None, 16 179200      activation_55[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNor (None, None, None, 16 480         conv2d_56[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_56 (Activation)       (None, None, None, 16 0           batch_normalization_56[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)               (None, None, None, 16 122880      mixed5[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)               (None, None, None, 16 179200      activation_56[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNor (None, None, None, 16 480         conv2d_52[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNor (None, None, None, 16 480         conv2d_57[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_52 (Activation)       (None, None, None, 16 0           batch_normalization_52[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_57 (Activation)       (None, None, None, 16 0           batch_normalization_57[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)               (None, None, None, 16 179200      activation_52[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)               (None, None, None, 16 179200      activation_57[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNor (None, None, None, 16 480         conv2d_53[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNor (None, None, None, 16 480         conv2d_58[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_53 (Activation)       (None, None, None, 16 0           batch_normalization_53[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_58 (Activation)       (None, None, None, 16 0           batch_normalization_58[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePool (None, None, None, 76 0           mixed5[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)               (None, None, None, 19 147456      mixed5[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)               (None, None, None, 19 215040      activation_53[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)               (None, None, None, 19 215040      activation_58[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)               (None, None, None, 19 147456      average_pooling2d_6[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNor (None, None, None, 19 576         conv2d_51[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNor (None, None, None, 19 576         conv2d_54[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNor (None, None, None, 19 576         conv2d_59[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNor (None, None, None, 19 576         conv2d_60[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_51 (Activation)       (None, None, None, 19 0           batch_normalization_51[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_54 (Activation)       (None, None, None, 19 0           batch_normalization_54[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_59 (Activation)       (None, None, None, 19 0           batch_normalization_59[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_60 (Activation)       (None, None, None, 19 0           batch_normalization_60[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)             (None, None, None, 76 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, 19 147456      mixed6[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNor (None, None, None, 19 576         conv2d_65[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_65 (Activation)       (None, None, None, 19 0           batch_normalization_65[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)               (None, None, None, 19 258048      activation_65[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNor (None, None, None, 19 576         conv2d_66[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_66 (Activation)       (None, None, None, 19 0           batch_normalization_66[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)               (None, None, None, 19 147456      mixed6[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)               (None, None, None, 19 258048      activation_66[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNor (None, None, None, 19 576         conv2d_62[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNor (None, None, None, 19 576         conv2d_67[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_62 (Activation)       (None, None, None, 19 0           batch_normalization_62[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_67 (Activation)       (None, None, None, 19 0           batch_normalization_67[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)               (None, None, None, 19 258048      activation_62[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)               (None, None, None, 19 258048      activation_67[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNor (None, None, None, 19 576         conv2d_63[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNor (None, None, None, 19 576         conv2d_68[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_63 (Activation)       (None, None, None, 19 0           batch_normalization_63[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_68 (Activation)       (None, None, None, 19 0           batch_normalization_68[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePool (None, None, None, 76 0           mixed6[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)               (None, None, None, 19 147456      mixed6[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)               (None, None, None, 19 258048      activation_63[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)               (None, None, None, 19 258048      activation_68[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)               (None, None, None, 19 147456      average_pooling2d_7[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNor (None, None, None, 19 576         conv2d_61[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNor (None, None, None, 19 576         conv2d_64[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNor (None, None, None, 19 576         conv2d_69[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNor (None, None, None, 19 576         conv2d_70[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_61 (Activation)       (None, None, None, 19 0           batch_normalization_61[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_64 (Activation)       (None, None, None, 19 0           batch_normalization_64[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_69 (Activation)       (None, None, None, 19 0           batch_normalization_69[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_70 (Activation)       (None, None, None, 19 0           batch_normalization_70[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)             (None, None, None, 76 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, 19 147456      mixed7[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNor (None, None, None, 19 576         conv2d_73[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_73 (Activation)       (None, None, None, 19 0           batch_normalization_73[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)               (None, None, None, 19 258048      activation_73[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNor (None, None, None, 19 576         conv2d_74[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_74 (Activation)       (None, None, None, 19 0           batch_normalization_74[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)               (None, None, None, 19 147456      mixed7[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)               (None, None, None, 19 258048      activation_74[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNor (None, None, None, 19 576         conv2d_71[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNor (None, None, None, 19 576         conv2d_75[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_71 (Activation)       (None, None, None, 19 0           batch_normalization_71[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_75 (Activation)       (None, None, None, 19 0           batch_normalization_75[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)               (None, None, None, 32 552960      activation_71[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)               (None, None, None, 19 331776      activation_75[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNor (None, None, None, 32 960         conv2d_72[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNor (None, None, None, 19 576         conv2d_76[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_72 (Activation)       (None, None, None, 32 0           batch_normalization_72[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_76 (Activation)       (None, None, None, 19 0           batch_normalization_76[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)   (None, None, None, 76 0           mixed7[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)             (None, None, None, 12 0           activation_72[0][0]              \n",
      "                                                                   activation_76[0][0]              \n",
      "                                                                   max_pooling2d_4[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)               (None, None, None, 44 573440      mixed8[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNor (None, None, None, 44 1344        conv2d_81[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_81 (Activation)       (None, None, None, 44 0           batch_normalization_81[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)               (None, None, None, 38 491520      mixed8[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)               (None, None, None, 38 1548288     activation_81[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNor (None, None, None, 38 1152        conv2d_78[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNor (None, None, None, 38 1152        conv2d_82[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_78 (Activation)       (None, None, None, 38 0           batch_normalization_78[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_82 (Activation)       (None, None, None, 38 0           batch_normalization_82[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)               (None, None, None, 38 442368      activation_78[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)               (None, None, None, 38 442368      activation_78[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)               (None, None, None, 38 442368      activation_82[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)               (None, None, None, 38 442368      activation_82[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePool (None, None, None, 12 0           mixed8[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)               (None, None, None, 32 409600      mixed8[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNor (None, None, None, 38 1152        conv2d_79[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNor (None, None, None, 38 1152        conv2d_80[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNor (None, None, None, 38 1152        conv2d_83[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNor (None, None, None, 38 1152        conv2d_84[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)               (None, None, None, 19 245760      average_pooling2d_8[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNor (None, None, None, 32 960         conv2d_77[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_79 (Activation)       (None, None, None, 38 0           batch_normalization_79[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_80 (Activation)       (None, None, None, 38 0           batch_normalization_80[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_83 (Activation)       (None, None, None, 38 0           batch_normalization_83[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_84 (Activation)       (None, None, None, 38 0           batch_normalization_84[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNor (None, None, None, 19 576         conv2d_85[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_77 (Activation)       (None, None, None, 32 0           batch_normalization_77[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)           (None, None, None, 76 0           activation_79[0][0]              \n",
      "                                                                   activation_80[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)      (None, None, None, 76 0           activation_83[0][0]              \n",
      "                                                                   activation_84[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "activation_85 (Activation)       (None, None, None, 19 0           batch_normalization_85[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)             (None, None, None, 20 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, 44 917504      mixed9[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNor (None, None, None, 44 1344        conv2d_90[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_90 (Activation)       (None, None, None, 44 0           batch_normalization_90[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)               (None, None, None, 38 786432      mixed9[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)               (None, None, None, 38 1548288     activation_90[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNor (None, None, None, 38 1152        conv2d_87[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNor (None, None, None, 38 1152        conv2d_91[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_87 (Activation)       (None, None, None, 38 0           batch_normalization_87[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_91 (Activation)       (None, None, None, 38 0           batch_normalization_91[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)               (None, None, None, 38 442368      activation_87[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)               (None, None, None, 38 442368      activation_87[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)               (None, None, None, 38 442368      activation_91[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)               (None, None, None, 38 442368      activation_91[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePool (None, None, None, 20 0           mixed9[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)               (None, None, None, 32 655360      mixed9[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNor (None, None, None, 38 1152        conv2d_88[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNor (None, None, None, 38 1152        conv2d_89[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNor (None, None, None, 38 1152        conv2d_92[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNor (None, None, None, 38 1152        conv2d_93[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)               (None, None, None, 19 393216      average_pooling2d_9[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNor (None, None, None, 32 960         conv2d_86[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_88 (Activation)       (None, None, None, 38 0           batch_normalization_88[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_89 (Activation)       (None, None, None, 38 0           batch_normalization_89[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_92 (Activation)       (None, None, None, 38 0           batch_normalization_92[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_93 (Activation)       (None, None, None, 38 0           batch_normalization_93[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNor (None, None, None, 19 576         conv2d_94[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_86 (Activation)       (None, None, None, 32 0           batch_normalization_86[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)           (None, None, None, 76 0           activation_88[0][0]              \n",
      "                                                                   activation_89[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)      (None, None, None, 76 0           activation_92[0][0]              \n",
      "                                                                   activation_93[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "activation_94 (Activation)       (None, None, None, 19 0           batch_normalization_94[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)            (None, None, None, 20 0           activation_86[0][0]              \n",
      "                                                                   mixed9_1[0][0]                   \n",
      "                                                                   concatenate_2[0][0]              \n",
      "                                                                   activation_94[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glob (None, 2048)          0           mixed10[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 1024)          2098176     global_average_pooling2d_1[0][0] \n",
      "____________________________________________________________________________________________________\n",
      "dense_2 (Dense)                  (None, 14)            14350       dense_1[0][0]                    \n",
      "====================================================================================================\n",
      "Total params: 23,915,310\n",
      "Trainable params: 18,328,462\n",
      "Non-trainable params: 5,586,848\n",
      "____________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import argparse\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import requests\n",
    "from io import BytesIO\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from keras.preprocessing import image\n",
    "from keras.models import load_model\n",
    "from keras.applications.inception_v3 import preprocess_input\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#LOOK AT retrained MODEL on tiny data set ( of just 60 Females from the 2000s and then 25 other F's as validation)\n",
    "model = load_model(\"model/inceptionv3-ft.model\")\n",
    "print(model.summary())\n",
    "\n",
    "#details on inception v3 architecture\n",
    "#https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py\n",
    "\n",
    "#how the model was made.  \n",
    "#  python fine-tune.py --data_dir=\"../data/yearbook\" --input_dir=\"train\" --valid_dir=\"valid\" --train_file=\"yearbook_train_small2000s.txt\" --valid_file=\"yearbook_valid_small2000s.txt\" --model_name=\"inceptionv3\"\n",
    "\n",
    "# if you are using a subset of data, before running the above, \n",
    "# you need to manually make the yearbook_train_small2000s.txt and yearbook_valid_small2000s.txt files \n",
    "# and the folder data/valid_inceptionv3/\n",
    "\n",
    "# if you want to generate the model from all the data, do the following\n",
    "#  python fine-tune.py --data_dir=\"../data/yearbook\" --input_dir=\"train\" --valid_dir=\"valid\" --train_file=\"yearbook_train.txt\" --valid_file=\"yearbook_valid.txt\" --model_name=\"inceptionv3\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show Sample Image to Predict, and Show Histogram of Predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14c1e0dd8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rTZpX/ex1HqHfSIH6O4tQBk1XtsFsNkv0APM3mUywvb1doRnqvK497xEpHNVB\nVBeLZEXyrmQlK3lpci0QDHDZhZqbvIA57CYkVH8AQnagCoMj/mYRH7JoJsnZ6o6YnANyviEykTwN\nDR99j3gZpuW+DjSHdIMcORjOiuRSer1e4lt0Mx6RT7S3BUAFxRDB8D8wP20w4ju8PnwvTeSgF6Ge\nCE06+oj6Rg4J1ZkFnladqawI1PuF75nT9GiaAnMn1MFgkM6DKYoind5IeqDdbuP8/Dy1U65v8i93\nqZ3m383Dq6AWlWujYLQyaGPy1sajo6PkxesQTt930srjXVZy5k+kvBg+93td+IjzWTZ/0Xssv94O\n2O120e12K6s/rVYr3TkMXJg6biKRTFQFo0pGlaa2B9MhX3Z8fJzu12HcbkJw9ck7duTXoiuMUX3r\nu1G91dW5Kx13kIsk11dy+dLfnSsqy7lTHXkvXj7f7/cBXFzNq451LKtODs4j+f4qzwP/uxd2rizL\nyrVTMGoLq2cil65ZcbTxXeF4BeYGfEQc1oWLOqTPFJEdnmsM71g50d/cqc49MbmkTIQCzK/YHQwG\naLfbSekQ1bBD5laRqOhZt7zu1Os8Ou9mMpmk+Ol5qhfi+cqWEsVaf+roByA5mCkJ6hfreXtESMXT\nqtsusaiN9X3KMpOIhyHHxAkAAIbDIQaDQeWoWPeM1jNiWJeuFPR8GP7GYx4cUUYc5fNM1MA359Dv\nJoAvAni3LMsfLp7z4jU3K3QWZkebzWZpmZpL1HV+DHUSdYagbAtnLlcU0Tvuku6fl1VMkdIjYgHm\nCmAwGCTUAswH98bGRoXUdROJUF5XmrSzq/kT5VO9gvlsNptVdsO3Wq1k8qo3qsaVq2NFXrPZrGIe\n8H/dUrZ+jkyZXL1rPDmvZKI3DbPIHPd4PE3WKXChBEiUs651EllfX7+EINUfiMJn7gmuZWEeIpPQ\nzfxl5JtB8v5XAL4s338U84vXvgPALz37vpKVrOQPobzorQL3AfwHAH4cwH/77PGVL14ry4sjM6lR\ndTZtt9soy7Ky94hL1nWcR47AyhF6mTICiC9a4/cccViXrwiK6u9RHL7MSBNDzaHNzU30er3K7DYc\nDiskL/cUEZFofI5SnPeK9j6VZVlZyiZXo8jj9PQ0IRiecXJ0dHRpM2BEgms+I9OKm1yZX69zn6Hr\nRNvPl8Q13rr2ikwKN78ijkO9i4nM6LjI8q6vr6c/olTu34sO/a7jqDRt/ex1kAu3jLyoifQ/AvjL\nAIbybKn0HkYoAAAgAElEQVSL11y88VXBkNiaTqeVrQFAPcmnJktukLiZk8uX2r659/xZ1MjO22g6\nbgtHzmEqHMzsaFQw/X4/1V23200KR+uAnAvrmuIb/RYpQr+lkaYtvUuB+aA4OjrC4eEhgDnpS/OW\n7UiOQNuOZprmk2VWBcPNsbl2iTZeRm0V1THD1E0iy/AtLto3+F6jcXEDJ+uFypNtxM2mvV4vEb/r\n6+soiqLijBcpVS+LpgsgnXG9trZWe6TrH4iCKYrihwE8Ksvy14qi+KNRmLLMX7xWyM2Ot27dCvdS\nqCs77VDaotx/4Wx8HaJhXIwfWDyrLSKDo86m70SKI4qjzs5nmMim7nQ6iQAcDAbY2Nio3NlNQldJ\nVNatriK54oueaX3ws6/UTKfThDjZSXu9XqXT0u3A9ytxItG6Y5wUnXT430lNoq5oWdgHV8SDLepD\nubCRsvL6ipCN/s6+rlfv0BsbmE8YJO7Z7vyv3vAsv06Q3s+8jnzcqeTQ1yJ5EQ7mjwD4j4qi+BqA\nnwXw7xRF8b/i2cVrzzKVvXitLMvPl2X5dlmWb29sbLxANlaykpVcV3mRa0s+B+BzAPAMwfx3ZVn+\n6aIo/hqe4+I1iRdAFdaSb/HlSZ2RcrDNkQfh5iKexOOM7O6cne15ipCWzghqumg+IjtfUUOn00Gv\n10uXz21ubmJjYwP9fv8ST6OzPM0KogA9lV5ND0dnjEvzrX+sX7YdkQdv1qQZc3R0lOA9Z11yMLpD\nmM6VWh/Mu6Jbbc/I1Z9mR93Oc0eHGkbbItfui8Lwd0V8NDF1a4znnSt5bOONjQ0Mh0P0er2EXNR3\nSfOh7aft5qjGeRZHWnUUwiJ5GX4wL3Txmjo26cHHPNA7gnRAfPB2xMEo9OZ3f6/ut0V2dQ5OR+ZP\nXYescxKjYuj1etje3sb29jYAYGtrKxG6GrebhVQuepCUm3McyEow0pfG/UM0b3rvtvIy6sNUliX6\n/X464hRAOhVPd8uTV6Apx6VZHxQ6SDkRqbJkmt72Wq85pZ7jaiKJTMloYuFzPlPeS4le1ku3203v\nkdtSklc5KaA6UWmbR5OG5olt7S4jNDn9hoxl5JuiYMqy/GeYrxahLMvHuOLFa0C1QVioSNy1PGK5\nI7LNn3m6+lk7otudyyokf9cHvIuTfTl0pLubt7a2cOPGDWxtbQGYO2U538LtFU6M+ioPw6pScCHx\n7rO+5lcHu9ZHt9tN39vtNra2tirHbzx+/LiyA5vOlZ1OB5ubm6m83W73EjlMpQZceAQ7V0VRZJCb\naCg+OWk7RBIhgNxgZhjta4pQ+d7x8TGKokgIhqhHnSLZtp4v7/fRAofzMJEiZPzuTLmMXBtPXgoL\nrRDejwlgOP8fzag55OEzkntiRjNQXZ413qihKYq4IqXIz05csqE7nQ7IWW1vb2Nrays5tNGjU9Pk\nDmj+Zx4U0agy8aVwz5cqfhKQarLkFDDLBMyRF88zOTg4AHBx9qyS+I1GAxsbG9jd3QUwJ7GpwGhu\nqdkMICFdVSralrkL8BYpjRyC0eeRqZsLH+UvEpZVlQAVPRWMm6mqvHRi5Jjy3e36nk4aTEvvePe8\nL5LVbuqVrGQlL02uBYJxSBkhGEJ6dwRzDmYRGZUj5NzO9tkn4lc8Ti9LlE/mNYeYNE61e9vtdkIv\nvPFyZ2cHm5ubieQjP+H2OBGG2uM+I7H+9LAiRzFO9kWzsP6ucZVlWdntO5vN0Ol00nuHh4fo9/uV\npe1Go4Hd3V3cuHEjlY95ZVx0XVBfDkdhEbqiORi5xy+SRWEj4pgIwn/LmWaURqNR2fqhx5b67mmP\nQ6kG9avyrQLKjfGZkuj8n7MG6uRaKJgcS62ep61WqwLzI8cpxqXx1qXJ99zu9M8a/zJ+MzogI4VT\nxwOpqJdut9vFcDjE9vZ2GnD9fr+yD0VXhxwuK+cS2dKumHRznCofNd10ILsCdfLSYbV6pgJzc288\nHie+5fz8PDmU0QSMzEvnl3QVy9PSz75CVWcq5XgUDR+Zu9EElVM+Gl7DtVot9Pv9CtFN5aJHZLp/\nVW7CipRcRDO4OZ0bo4vkWiiYHBehnchPU8sda+gD3J9FaXqYHArR91yJaP6XIYLrOrI2LhXMcDjE\n1tYWtra20hIvHdh8yVZRnnJauoFOORH+rrNUxCN4PJGCcYVE4eqO5qksy3TOydbWFsqyTNwKuRJ1\nMuM7zkd4nuiU6YS1D0LnlPjMV5+0TXLtt0wf8/dzyEOfO6G7traW/viMxLbH4wrOn7kyySlOJ3mv\nwsFcCwUDxGv4FK0c9Y0BLpZsI+3P5zn4GKXPzzoIIvHlPH/P8+Lx10FjxqsrRsPhMG0D4KB0VKeD\nzcunCMZXiFQJLZqRFZrTv6TRaFzahR0pJldauiJF72MqFu7BUaXAOtdVI+aZ/YBKgxf1ARcHwk8m\nk4qy0iX4aNDojJ6bfLSfLJq0IgQTTU5c+dM21TBsB+/7kRLU+N1dwN8joesK3PO6InlXspKVXAu5\nNgjGtaY+0wOnojV7/b5IIhuas1mdqVMHZ9WvQOOv8xuIEIISjtwwSHOIe094Op3Goekod6Jp+Zkt\nOrtyNtQyRp6fNKt05iR56HwA42VcWnd6UBjDsFycXbvdLkajUWWntMahiEX/0zHPkQ+AdPOn5l2X\nxV0iUzcynyMz2d/XOBZ952535v3g4CCVd2Njo7Kxk+EjDsbz6vycI003k/nMubvIRyon10bBaKN4\nhxyPx+kOZHY28giROVJn+/p3Khe3vT1Oho06RY5wjngJ/T2nENfW1rC+vo7Nzc3kYMVzdfVkuog4\n9fzpKpJvHtUOQ3NF4XNUH97haGZEXsFUhDnHQv+vpo6WRTf+sQ18ZYj/WUd6lAfD6iTC/qPX3rgi\n03ZSBRNNUrnJp66taQp5m+nE0mg00u5zYE6G3759O/RoV7Mx2vrAvuArr648cxPN85hI10bBUHSg\n6/GY3O7P82CW6bTAZdvXGz5aLow6lqe5iEdheIar4zY46+sWgK2treRIByCdVKerB+xYTvI6ymAn\n1nBaZna6SMlGs6AvT6sy8iVwPnP3Aq8XRxDK9VAJNBqNRGhG8VFcwbj7PXDByzipHJ0n4xOe11G0\nshhxN1EY5VtYj+5E5wRrDvl4HiME7m3h3Ir2Wf19GesgkhUHs5KVrOSlybVBMK59Z7NZOpX+yZMn\nePLkSQXBANUzYgn5fUbI+a0oPIxs0WUgbvRcZ4Rol6zO9r46wxWj7e1t7OzsJEc6AOkQKeVSaE/r\n7MYZytFKtFXAIbLa1r76AFzsRI9WoTQPa2trlVsEPExkFjiSdISiddtoNCru7o4UeI1HZJZqWYui\nqGw5YJtEPjERAmX46DO/a19wXyHvW5H5wpVEmkx+YV6UJ33uY8Gf+eqeI59IvqVNJHaM6XSKp0/n\nZ4W/9957ePz4ceWIxVardQm6RbYxYXVO1C5XE4txRZspPWz0PPrNn7GhuEuY5hAVzNbWVtp3RK9X\nHZxuU9M0UgXDOtABHpk1vlyrJKEOXvd50bAal9aX767WPOmStxKV5MYc0jcajUtXoCjRzv1azqlE\nfj5leXHUJw8U9ytRllEskXj/iPgQF61H9fNyT16tU82nmoTe5vqc9ckJw9vKnRTJtflixDJybRSM\nrgJwxUjPD2GnUmWgHYvPIo4jUkSUCKGoze7puUT+MFEYT0+VQLfbxebmZvLQ3djYSOe66ADwgR0p\nGN9Zq6tKkRLhd++M+swVgyqviA/QFSU+i/gdJ4d9cPOZIyxVOM7lcDBofQGXT7lj31FOLxqQ3qf4\nrpe57vfcM3+f/Y2bSPV3RRl+FnXEy/C9HD/DZz7x5HyYItJ4Gbk2CoaiF3vruaQRvIuW5nym1N8i\nWaSElpmptDFy4R1l6LUhw+EQN2/eTAqGhwnpipEqCidQNW5XOkreutmkYXJb8qMZ0MN4G6hJxd9V\nEUdxO+znTKoKTgeGE+Q6SFm/ilxZPn2Pioi/09FMy5JDt3USTWD+3BcMKFQe6kjKvAJIx4y6B7Ln\n1ZGXKhPPnysTHzd00IvKtEhWJO9KVrKSlybXAsH4rOznX1Dj+kY7tRcXmTI5GBvZ2HUzUGQS+HtO\n3jpH0el00qlkwNwkunnzZjr3hO+02+1LqMNnHIf1vvlPzRp3P3dTS+uXnx15+HtRnUcIx/kVD+vO\nlcDFvhutV87ASqC7icpZX9uI7zoS0D0+juxYrjpebRFqcIQchfd+o4Qw24b55L3UEQejeYr6iptE\n7lwaIWQvp39eJC96L9IWgL8J4FMASgB/DsBv4zludiRE5vGYensfO0PEsWhndzsxR9C6RBDcza3n\nESUWmYd2u512CFPB0KGOKwVM2y+Vj4hB5zFyPI36xug7+t3NHypINXl8IEX8iis+1qOmmRsUbjpE\nNw3oDQVeFs2Pexzr3iP3F9J9XE5m1vWDHDcRTUYap5qu/h6AxA1RkbCM4/E4HbKlZpTXtys0FTef\no3bw8Gr+/0GSvD8F4P8sy/KPF0XRBtAD8GOY3+z4E0VR/CjmNzvWXrzm3Ao9d/WAb9+S7vwLcKFk\ndNZYRskoKuL3RSgoxz/4f/7ptRNK4gIXxy6Qk2G8PgA8PVUe/M6O5rxMbmXJ44nsb82TbxBUJaP5\nBKrOc6oUIns+6tT872WPLoNTofLwGd7rSp35Op0Ozs7OLs36vgKmdeGiE1VElkYKRRWUo3PG6e1Z\nFEXlPGPnxiIesg6h+/eoTnPvL5IXuRdpE8C/DeBHniU6BjAuiuLKNzsCqKyWcKmaheSStK8yLGMK\n5SCf/l7HkDvEjSq+bnAURVG55Kzf76ejF3jOCT10ncB2nxeg2iEjQtc7SISi3BRw9KLl8Pe0TqI6\ncOWqcakSopLQPLjLgCOaKE9RGNaXE8GKxtjPWK8k1JXQVBSbG1RMX5VJbqe0K+/cQoKjhbK8OKxL\nj2hQBeNI09vDESafeVj2A/XD4fOrIBfKi5C8bwL4AMD/XBTFbxRF8TeLouhjyZsdi6L4bFEUXyyK\n4ot7e3svkI2VrGQl11VexERaA/BvAPgLZVn+alEUPwW76L4s8zc7lmX5eQCfB4CPf/zjJWeSTqeD\n8XicZn4+4yyRWy6rm2XqyFmdbXT2jCCxziw+O/OZzxR0/OIZLryVT891IYKJZmUlZp0rcq/aKB+K\nThi/701yk0njc5gfhYvqVcO5i4FuWPVlcCd0dYlW+YhcnVO0/fSaYUV2JJGBi4OtNO5lkQj/O9qN\nTJEor25uex1rXIqo1IzyPLHt1LwkMnYkqv2iLMuKIyPH4fPykS+iYN4B8E5Zlr/67Ps/wFzBPCyK\n4k5Zlg+KmpsdXXQg8ZAb9c70RnAIGMFNX3GIJOJS+DxHknk+os/83mq10Ov1kkcuyd1er5e8MwnP\ndXC6a7mWhWmQ1F2Gl9GB6Ao1UhqRzV2nWJQfcTjt8auC8yMd1HeGA0SVqCuriOvw8qqPjfczNT18\ndS0yf6N+EW2mjOrH+4q3XRS/1ivT0nQ1jI8JlSgtn8R0oSTaZpEz6erkRW52fL8oit8viuI7y7L8\nbczvQvrSs78/gyve7KhaFEDFk1dXkCKb8ll+Kv+ByzfY+e9LlrOSP30eKRRtyEZj7uY9GAwqaKXT\n6Vw680NFPVgjbiNSPJ6HaMD5LBcpGJ/dPC3fbezEuir0aKbXOOvKoqstUZ6cY1LRmZtCBaIDVjmY\nTqeTdqrXEZyaBv9HyiWqvyjOHCrWyVU5PE5G0V6tOpLX04zyFiHUyA3gD0TBPJO/AODvFPMVpN8D\n8Gcx53We+2bHlaxkJd8+8kIKpizLfw7g7eCnK9/sqDMXL+VSXwCKa/5FsG2Rto0g7zKmAPOls6Wa\nJsDFwVF+j7Da+xqvL0XqM84uXhcRu+9L0vyf41JypkA040UIJWf/K2rMmTE607o/i6806WY9X2Wp\nM+mYDjf3sY7b7XYK2+12L12N63Wk8WvZItOorg9pmCjvbvLqZkduMYmQW136RG8RknXOScv9PGaR\nyrXw5NVKPT8/x2g0SncVA6iYC84jRKfCqyzT0NHy66J4tGPzNw5sfif/wsOi+ExPhmf5NA09FU47\nO//nBjiVnQ5WHdxuhnodLLLRI9s8Mq2ifOn/aMnU8xARrPqumhBRmqq8lNNyTkMVDMn2yCTzcjv/\noZJTvJ7PaHKI6koVDI9rUDJax0RUp1pfrtAi89Iphdymz2XkWigY4ILpPzs7w+HhIQ4ODtIxgapg\ndHaKnO2imcwb1jmL6HPuN09P4240Gml2AS46Le17huEMEZGXQFXBUKg4vENGYZy30JmJEs3O3rEU\nQek7Ucd2JOIzcV3dMq7Ix6csy4RktWw5DoR9Ra/y0EHjJC+5jfPz84ovjObRJ55Fs7nXm8ah5fM+\nzd8jpaNbHJh/7VMaT8S/eP1GaUUTmua3Tqnm5FooGK3AyWSStgnQFVqXNbUjLerEubSWCRsNPv3v\nsB+YN4IeaUnvXH3G1aI6k4Rl9Y7qHqp8TzuFDw4+9zJEUDkqj5tKmr4rJM+PE7au5LX9PAzT0VUk\nHQARQtPv6jAXIRzWlaLNTqeTCHiG8Xxq/v2z/r5MX4wUiSOIRqORUD2AdAi6rxRGxK7nLWdKKhp0\npKf9IkJIi2S1m3olK1nJS5NrgWCA6rKk26ecOdfW1hJE5JJpNINSIls5B9Vz2t1na3/ue5+azYuj\nL2ka6Z4i5V60zPo/Qhi6p4jCWczJYX834ph8RsqZSxHSid5zXkWRjM60+l+vL9F3/XtuMyPjdt+g\nHGKKluMpekYPzSa+p6aF96MIQUdokBL1m4j70jrTWzH1Boy6JXBPm+9EdIGWxc13PqvbTlMn10bB\naIfpdDpot9uVlZd2u105j5fvRAx+1OgRH6PicD8HBx26qnnHjuFHALhDl3ZcltkHsfMdGj/LQsWl\nZpG+y2c5CO83A7oPhKev+fA6czLRFYwrJh/wfK4TAju1cg3RoNQ+oPXqR6WqieXKk/VBzoz5dhMp\nUiwualLkTKuccmE43TNVlhe3H0QrPTmF5n2aE3LOxI5I7Yhz+pbjYICLhmu32+mi98PDQwBz79fR\naJQOAdfwKhFKyVX8sr973LkZixJ5h7prO//7DK+Sm6GcZ/HVBMar6el9Qp62liVSAlEd6Hevg2g1\nycuuA9DRUMSb+CoWj2xQUZ6G70dKwevA0afe+exoUesgmri0Lh35RKiC4TwORark7LjgwetWovux\n6vhIllcd56I+oPnyeqRcZdPjioNZyUpW8tLk2iAYCjkMP0k9silzkoOdi77rDJIzLTQNzoCKRCIT\nSSGvmgvRzK71kFuGz808kVkVvRdBbEcUjC8ShuEMHc1yGi6C5Zpflt0Rjf7X+onCKbfhvlH+fu6Z\nm7Pq8LjILIhQcYSEtU4dCajpSjOt2+1e4mtoNvpqV105PY+536PldUf5EeLOybVRMFop5F/0iga/\n1sIlV3nR4F0UVuGz772JlgJ9mdgVjId3m5nQNSL7nF9wH5fInvaO4s5oOXNtmToi6cjPuUG7yITN\nmWRuYmkY3RXtR18qCTqZTCr3mGs9+WKCK1RdFqd54qaOl49hvO6cQI0mLRVORLzGBrjMX3GRQD3B\nc1xkJJEyqlOOrLvIb2oZuTYKxleMtAJpN+og9JWCRYMjCsfv7CA+w2vHitLjYNdT59yGd8/aOvZf\ny6bv8JkiLM2T5zvH3bgijPgT71gRcqzrYDmOahkEoIrRlQ3DzGYzjMfjpGxGo1HynQIu7tWK2rkO\nZelA0vaMSF4tjyrGOs7K6yA3WbHve7/j6Yfcha/kvsev7RPxLa5UI+XovJ4/W1aujYLRZTH3WuUl\nWhFUjOSq5tOyrLh3GE+fnZOzjw9ofvZO6vGznErIRTuU+Y7P7ITZQHUlSxXRIpQRpRWFdRNC/2tH\nzA34aNc002Y9EJ1wC8lkMkkrilQuWgdEvJGplOs/VG7qUqD9MCKvvQ7qJjavAyWBFZ2QGtD0er0e\nbt+en9t269YtbG5uXhrkEdr1tD09z3c0qbwoglmRvCtZyUpemlwbBEOzoiiKSxvR+DkH2V2iWcoh\nbTQL6Xc3yTzuiIOh/e5oRUWXXHNogel72p4n3ZeicZZlmUhC+hT5oV2KjtS0usrsxLgigjH3nJ8V\noWgdME+TySRdwEe0cnp6itPT08SxABeu81qWsiwrW02AywjJOQv1KVKTQIlez6PmO+Jh9L2cCTWb\nzSoHjw8GA+zs7FR8cTY2NvDqq68CAF555ZV0X7nzSYvQfWTe5SSHvJjesnItFIzCtlarlXa0qh3q\nO47dK7FusPrvdekzrELu6H1tJD+uQU0j/eMz5ZQYl5oLhP3aaZV8Y9q+H0sVjCoh7krXQ4t0UyZN\nKj89ziVSeK6Ucs5wDK/5VMVA8pj+HicnJ+nKYLb9aDSqvENRs4b8RafTuXQrhR6UrUQ18+oEudZH\nbvDWDerIA9jN4LIsU94HgwF2d3fx+uuvY3t7G8CccxkOh9jZ2QEw52KokFyZu0nqZK1zKRQdZ16G\nSAl9yykYHRDkMbjxDIhXY/Td6DPlqjOy5meZ+IqiqJC6PtgiL1af8Xw7w/n5Ocbj8aXzYqbTaXhn\nse+q1atFT09PE0dBBbO+vo5+v19RjO12O9W3l9P5B5/N6zos/0ftxFPl9BmdKQ8ODrC/v3/pcrBG\no5FQGetc+wfrWzmYyWSSzhdi/FQ6mnefIFRxeZnrBmLUd1Sx6DNtv/X1dWxvb+OVV17BnTt30rNW\nq5VcNnSbiabh/FWE3D1vdTwgpdm8uE43mswXyYtevPbfAPjPAJQA/gXmJ9r1cMWL1xxB8KAmavH7\n9++ngfzhhx8CAI6PjytxLEIwFEc+uXBukkWkINGLk4IUdtZcQ0Zeq0D1gjMdrFS87Ezn5+eXtu0D\nSBdzAfNZfzabVYhRHkeqS6HswKromX9HHhQOND+I3Tu1Iy1+VnRFxcjvp6enOD8/R6PRuHSWju4X\nUn8QTV/Rymg0SihS86amlpaB9ciVzMj8iBSmTyL6m3/md71nqtPpoN/vY3NzE1tbWwAujk9VZc30\nFf0tQvOaR+/TdatIvrv6qvLcJG9RFPcA/EUAb5dl+SkATQB/EvODv3+pLMvvAPBLsJsGVrKSlfzh\nkRc1kdYArBdFMcEcubwH4HN4jovX3EbtdDqJzHr11VcTDOYMd3x8vJCsBfKOUctKneaOln/rfvd4\nfVZ0U0CXt2mr633VPJjK/TbUl0Pfoa8IgMrsHZ09wzj8RP5c3Xn7RZyA3xigKIP+KyRmyQu1Wq3k\nA0J+hRti9ZmbII4k1Wkzyq+artqe5GGYrxz6zdWLIwg3N9VM02VqojbmOUJH+n8RyliUP81b7vfn\nQTEvcqvAu0VR/CSAbwA4BfALZVn+QlEUS1285uJEkzZ0u91O163ydP61tbXKKsGihncFs8j2zJT5\nUp69wSP/BH0eQWYnS/nfD1uimchBz1WWiH/Q3bjtdrtCRpOjUVK5LKv3DXEXryow5ywcXjOMmxFU\nLu6rQuJV88B3eNpcp9Op7Kpn2dyd31eMVMGpQyTfoxnpO9G9nziJvUw/8bLzmX5XYpZ54EZfJ9sj\nDk/jZD4jv58o7zmlp+ZXVAdepmXkRa6O3QbwGQBvAtgD8L8XRfGnNUxZ5i9eK4riswA+CwC3b9+u\nDEDOZCTkTk5OUJZl5QgHn5G1suvs4zrSK7Klc5WpjRM1OH/joM2RnPyuHccHBsOoIgEu30msM6Ev\nYWo4d6/n6szZ2VlFmRAh5Rz0fLDUiSKYyJHQHQSpRFTB6P4s3dsVpZ9TFORuxuPxpbrzCcLrTduK\n4ryOvptD1KwDThjME5GLK5dI2eixHcwX85m7nNCfkV/T74zflUyOV1okL+Jo9+8C+GpZlh+UZTkB\n8A8B/Ft4dvHas4xlL14ry/LzZVm+XZbl2yRzV7KSlXx7yYtwMN8A8H1FUfQwN5H+GIAvAjjGC1y8\nVpbzQ5739vbw8OFDAHMEQzSgs5nOXJHmVWTwPPDO85V77jOXmzjL8EI6U+qM4ahG88EZyLkTNVtY\nb51OJ71LBKNmxmw2Q6fTqeTJXe4jc9PRg/NKDO9ohe953fmeLZ5pDFQvtPclfJ29Ga8iNq0PreM6\n7ihCqR4PP3sd5PqLckCNRgPD4RA3btwAAGxvb6PX6yXzNBe/5l/jrgujv+d4F0ctHsZNqmXkRTiY\nXy2K4h8A+HUA5wB+A/O7pgd4jovXWKDpdIqTkxMcHx8nE+ns7AzD4RDr6+sYDocAkJzxFhFbUaVG\n7zgpmcufxhcpLbWpaR7lGtHTjsK5gtGB6nnTd91BT/kHNwdYj+7HQ7Mmt3vauQ6+owSm1lWkYFy0\n7iIfF69D53d02VfrILcPJ2ce83PO+TBSPovS0++z2Qy9Xg+3bt3C3bt3Acy9dIfDYcX8cSWh9RQ5\nYWrZtDxFcdlDPkcbsB69vNEEs0he9OK1vwrgr9rjEa548ZrOXKPRKO2QVU6BPgmDwQDA3Ovx+Pi4\nQu7l4n6W17pyVP7nGiqyqd0jV9/3+2s8nqhhGQ/Rg9vL6kTHsK4AVAFp/nQmUmVCZRB1Zo3fnQaZ\nf1d4+hs/62CO7ll21KFl8c2ceiUJ4/FVnkajcYlAVgVJLkfzQmWq7aK+TFoW/65Che5tr3GzjNvb\n27h16xaACwQT9Y0cuuBv7vznSofvqQKOxPv5shN0Tq6FJ68KPRu73W5anuQKwGw2SyYSj9QkytH7\nq10iCE9RqLyI2NIOWjf7qVLwDqq/UxyVsMNMJpMKsnOk4+e86AD1gVSW1e0P0Yw0m80qqyq5uotQ\nTF29Mz0ndJkm03MlxDxrGF7hoXlQb1O9DVI9eXUSY7oatyoXJ821Xt37OhqsRF91ikn/+3nNEfLR\nOvW28X4RmdaUqJ0WTcKRGbmsrHZTr2QlK3lpcm0QDLVou93G1tZWRfsfHByg0WhgMpkkVLOxsYEn\nTzj7vR4AACAASURBVJ6kpeqIOKyzFVUb52Zfj8tRSC4dnRGiGcvzwFPYdN9LxGP4JklfRtZtC+7U\n5qaUckU0D3zJlaiC7/lB20Ql0SyuaERRFL/n2kbL62cAEan4Mqxuhj09Pb1kirFseogZ41FHP6ah\nmwndHIvME0cQEZKoQ41a587FRQimrq6ZvqONnBOd9rEoLs338yCYa6NgFHJ2Oh1sbGykZ3QUG41G\nqZMPh0N0Op1LsNdXbXLKw00JrUAN5/Hoe3zHVwY8Te98bjO7CaHw3TvGaDRKSpVmo3JVrCvfNexc\nUbSHqSgu/ER0AKh5oD42WgdeFjch/QhL5600HMWVAB3tuL9K2ybymXKOSJ0UlevzdvK9Zd6mXl5V\nxl4vWk7td/1+H9vb29jc3EzOo3qPlnNv3n+c24n4Hu/T0USoCi6nOKK+v6xcCwXjWpNenLpiVJbz\nk83oIr6zs4ONjQ0cHR0BuNg64JocyM86FG+IHKpRichO/c3jipzV3P5XvoUcjA5u/s7tEu4o1Wq1\nEkmoBCe5GuVgiqJIfBZX5BQBRatW0Wwa1ZUOLA+rcXq9+w7h8Xhc4VtYrpOTk8o9QXrMw2QyQbfb\nRa/XS2Xhpkldmh+NRhU0xPw2mxdXzvLoEF1J8t3fucnMy8d6YjzD4RB3797FrVu3Uj+ngvG+VYeW\nvY2A2PlzGYmI/gg9XSXOa6FggMuZV58XvaCcezReeeUVPH78OCmYx48fYzweV1CNwsOcQslVVqTt\no8qP4KwfOBUhmOiQJIX0RDDqgxItUesM3G630e/3sba2lnZOn5+fpzpRc1L313S73TS7R+fRumKK\nloFdIaniUc9dDaM+O456eGSFEvmTyQSnp6c4ODhISpZeyHyPE9POzk7qN4pgWGb6AulNoT4ouXtd\nz87RNtN2V4lW1dg3mKcbN27g1VdfxZ07d7CxsVFph4ikddM1SlOV5DLoJKes3ArwiWRF8q5kJSu5\nFnJtEIzb8PwMXEA3vc3u1VdfxdnZWToX5sGDBzg5OanYq26aUHImUBRGZzVHIhEh5zMhvyuBqzM7\nn3FvDL/76fjkZfwMk7OzszSbA0gIb39/H8DcSbHX62FrayvNnp1O59JuXT5TcpjOXHWmoKM4r0e+\n5+3KemC9jMfjS+WdTCY4PDzEo0ePUpkODg5weHhYOfRbD+IaDAbpYHCa0+SOWq1W8qOiyeQEq5Ou\njhii/86vRHwe+yXz2ev1sLm5iRs3blS80yMzNOqH/iwikJmul8XHlecxMovcc3lZuTYKJiJbFSay\nEWlDDwYD3Lp1Czdv3gQwt2kPDg5qoSvTiTrAMoSYDiba5eppynARSahh3JeCXICuANGEUrLWV1AA\n4PDwEA8ezDevn5ycJGj85MkTAHNu6tatW3jrrbewu7sL4II013NtmW/Nmx84VQeNvXM7p+X8itcL\n8+N112q1Uj739/fx8OFDnJ2dVVZ/zs7OKmc6d7tdHB4eJsXLgbS2tpbe44VmFHItPvnQdF2Gf6jr\nU6xPlo/n7/IKEgCXFEdUr6xHJ4JzvItP1jnl72E8vUU8UE6ujYKhqBZl59PjB9TOZCMBF2eVekVE\nisM7P5+reKUyTd2/Q+VSp2CiVSXfo0TlQa7h9PQUx8fHldUSxqvog9wAB9KTJ0+S1yq5KaKT+/fv\nJ2Xc7/crjmjKkfiF8d42PgCjGd5Xb+rOU9aw0+k0lU335BClPnr0CGdnZ4nQ5XuNRiOtxNy5cwf3\n799Hp9NJfMvTp09xdHSUjuAEgM3NzcoVM2wXRSKso+dRMBFP0mq10imNN2/exPr6+iWU7fVSlmVF\n8atoPqN6V9H8LEIwi2TFwaxkJSu5FnJtEEwdqtAzVfgbUYDyCq59I00bwdhoZmZYn5X0cKDIPGLe\ngAtHOM+3Q3+ujnCm3tvbw9OnTyvn3dA3qN/vJ9S2ubmJfr+Pg4MDAPMVladPn1YO+B4MBnj99dfx\niU98Iu3aLYoCp6enCeVwKZhXhQAXs5muThBl1G0nAC74DXf2U/OS4mfi8jdubG02mwmx7O/v44MP\nPsD5+XlKm+c3v/baawCAT3/603j99ddRlmXajV8Ucx8q3lIAzE0r30hJjkRRas5BbRm3BkWvzWYz\nLU0DwN27d5PTqKIVXzXKoQVFJERe0YFTmkdvz6jd3JRlWs8r10bBqDhMVGhN8YOc/HBmFYf0UcW6\nLeqmDaEsO58SzhrG8+38g3YKKgHyLez8PNlMBwAVzO7ubjJ1dnZ2EgfBOLvdboWT2Nrawquvvoqd\nnZ1Kh1ZzqCiKdPMAyVNdSlZF4krXl5ujemZ6vsSr8ahZqTIYDHD//n0AF6Yjy8i62t3dxUc+8hEA\nwL179zAcDnF6eppI3n6/nxw3WQf9fj8tCzN9HrDteVjmyIpItL+0Wi1sbW2lGxp3d3eTS4ErOY8z\nMnlUlGjmuxFZHJHtEaHsS9LkjzS9ZeVaKBi1Q4F6ra3oQL08gaqTFOOJyFqVOoWkMwrtdXZa8h/O\n3ke+BzmCk2VhnDor93q9Stno3zEcDitXWDSbzUTejsdj9Ho9TKfTFOfW1hbu37+fiF3Nl9+prUdI\nRpyDd1B1JnM/GHexd9GVJM2L8m6j0QhFceE7srOzg7feegu9Xi/xVWtra9jc3Ex1UBQF9vf3K0Rw\np9PBcDhEo3FxQ8FgMEiDX8sZDcxIOep/50B0MlK0ef/+/ZTPwWCQHOu07O6z5Qpdn+cGemQN1Ckd\nR0KRqJL5llMwQOxQ5KIV5HCPiECJ0auQUS4cNMwXd3jrYdNRGdghKU6kaUPzOQ/l1hv++v1+hXTj\ncaF6bzE9VOkJevfuXWxvb6eVFGA+U29tbVU6D0lLdQikE5qex0JRpePL1k7kEqr70QS5QaMTi9ap\nIjqm0ev1cP/+fWxsbCQFM51OK+cElWWZ7rBmGjzTWe/aIsHqx676al80wCPXBM2npksFc/v2bdy5\ncycdZN/tdlO7+8BVAr7OXFlWEfG9q0yu3yxZkbwrWclKXposRDBFUfxtAD8M4FE5v/8IRVHcQOZy\ntaIoPgfgzwOYAviLZVn+k6tkKFrudE5Gf+eswSVcPeMjsh89buDyHgy1adVvod/vV8yZyPxy3xVf\nOowIPZK+yu+wzIpquJuYcUwmk7T/CJjPymVZVi4m4wzpNx2qKcmZ1E2BCFYrqlJyXMtHgjfH3UQo\n1R0idQmdxHOj0cDu7i5u3LiREIxu/OR3T5+miF67SgKe6eh5QtEyfI4PjPxPmDZNM2C+LL27u1u7\n74hoyDmXyMR20zyH+DWO6MRCL1Pu1Duvk2VlGRPppwH8DQA/I894udpPFEXxo8++/5WiKD6B+eVr\nnwRwF8A/LYrio2VZ5ultE2W7KdrAfE7T4JVXXgEw93WgnwPDjMfjSysYTogxrpzHKjsk7whWf5wo\nPAeYl0nDONuvKxeMmyaM72HSc2nJC9EcIq+jdydNp9NLXrIMq58jJzOtfxXfH6VhWH5d6dFy1cXt\ngy3iO1gO3QmuEw0VKgevvq+TjRO6eriX8m45XyZKVGd81u/3E+dy48aNCu/jvl7MZ87UqWuXiFtx\nPpASOXx6PD5elslLThYqmLIs/6+iKN6wx59BfLnaZwD8bFmWIwBfLYridwF8L4D/Z9kMaSM7mejn\ngGxubqZlP7rMNxqN5MV6dHSUZmu3RaMG0bjZGVXBbG5uVmYpxstZVDsy/1N5Uen4LAJcOA1q+k4u\nKv+hqEYP6o74E0UPzqtoXbvDoOeFcU2n08olZsyXKlUuZ2snVeXCDuxI0tP1MEzLyWFFVaxvVQqq\nhPS9VquVnBSpYIqiqJCuVFh1DmjOs3E1b2Njo3Kg9/r6ekKWqmAiripCx/w9h7KiOqTQSzridLSe\notXA5yF3Kc9L8uYuV7sH4Fck3DvPni0Uh4D6TDuZ+kvokQ537tzBdDrFcDjEe++9N8/Y++/j6dOn\nOD09vXTYUl1lqdmVuzCeyEBJZXZ+PR+WSkKhuM6uLIsuV+qyucetDe7IK1rVUbShg8vJVSfZfXma\neY+URdR2dR1e03DT0c2F6H33zYniVgVK88/9Z2azWWrf0WiUUJwOKPrGaP14//FBu76+jq2tLdy4\ncSORuoPBIF1kz7Lz3bq4fNJyhcDfvF0iiZSH/4+esY093WXkhUnecl4jV16uKYris0VRfLEoii8+\nffr0RbOxkpWs5BrK8yKYh0VR3CnL8kFRvVztXQCvSrj7z55dkrIsP4/5NSf4xCc+kRSUElgOC12K\n4sJHgsdsKgzlmSLj8fjS7QPRTOEaXk0knrXC737gkcarBCUJVsbdbrcxm80qqIEIRjfsReV1s8nL\noaaVPiPEV+SjyIMzNGf6KF0tG02IaHbjcyc/tX7VbIuQFvPIfGq+vY5J6LoTpqJE/kYnQj7jPi0A\nlf1NzsGoWesmmnq/8r1+v4/bt29ja2srLVOT1K3jNnLmYmRCOXejKJ8mor/nKDFCn/5e5HpxFQTz\nvArm5xBfrvZzAP63oij+OuYk73cA+H+XiVALHTWCr0Ao2Qtc7I4djUbpAJ/t7W0cHR3h5OTk0rUW\nkfml/IqTe+5oB8yVjMJnPQmf311x0UVd8+4ciP/u9aPmQQRr1beCg9Ftae9YVC5ez67oVVnq4NK0\nffCwTVXxukkWKSl/pr48Od6A9a1KR8OoIuPKI3BxHKfydTSPlIPRWwsYhvXKCYKOfxsbG5UjSJXf\nUeV8VYnaT0WVtyv1iFTW9mQ+I3Nd419Wllmm/ruYE7q7RVG8g/k9SD+B4HK1siz/ZVEUfx/AlzC/\njO2/KK+wgvQsvcr/Z/Fe+p2fdVWAdxnTHXw4HKYt8STz6kg05x+ijuQu09po6gAHXNx/7IiD8fpM\n4Cs7jgKUX8iVQd+lRORv5J2qCEYHvpPtroR8xlPyl/G6sogcEJVAZr58RmUetUy+KuczOutRFSjD\nKELlJKRxs91dIerEw1PveK7LrVu30tYMPbbTiXYq6zpEECER51xynKK3C8uc6zesb0crL1XBlGX5\npzI/hZerlWX54wB+fOkcrGQlK/m2lWuzVcBXARwGahiGU+3PmUTdxjc2Ni4d6uMXtEWrFkQlOnMx\nP85jaBzK6jMtd1zSdxb5GkRhfAaKzAo1U3I8li+BRzukHVL7yla02qXpuBkbcQKOlNzc0fdzcUWz\nri6bRwiN39kv/D5o4GKHvraDvgvMV4w2NzcxGAxSv3vllVfSZka9AsXN/2Wc2lyiceESmbf6m9Zr\nHZrhfzefryLXQsFoRVBpRLa1SgRVgYsjE4E58cvNgbq8HFWqcw3OwSixSCHEdTOGeRuNRpc6brTc\n6KKdsG6QeadnB1YTyculcUSHS2k7REog+u+fXcFEdc7fPT3Pi5uJzHtETmpaOa4hpyjoPa11xx3z\n7lfE3wAkZ88bN25ga2sLwHxT5s2bNytpReZQxKWwvnMDX7kVDRO5FfA5n3mcPnk1m810YFluSds/\nL5JroWCAaufxDqIdPWoQfiYRS98DriIdHx+nM2rpeJfbzMe4/NQ5zviKTnivjisYPeMkp9AiX4oI\n0UQKJqdste6i2dA5GO98uiKmRGnEAeTaRScIHxR6JKgjFtaVn7LnSjbiUhyRcqXLOaZcnWmbKL9D\ndLK3t5fe4y0N3J5x+/ZtvPnmm7h9+3biYPr9ftpMyTzoRlKm53nP5dUVEeszVwcU7TOO3FjnPtGR\n03IlFU00y8i1UTAURRPqlu+IhRrcZyS9YoKrPmdnZ3j8+DGAeUP7kqQPIopXqg4AHsDtMwnDAtVZ\nS80nzzcbdNHM4Egr6lgROcw8+YBn/XL/Fh0HWU+arqZP0bL4IPFdwmqetFqtcDsF64JxRxMKlX5u\n2ZR5ig5f0jy4+eaezcB8ZXJzcxNbW1tp4eD09BSbm5tpi8qbb76J119/PU1qnp6eB6OXweUUidcH\nn3u/X/R+hI48bp/QcuOAbVx3oFVOVrupV7KSlbw0uXYIBqjCcIra0frdTSngwh+BJN7rr78Oegs3\nm008efIkoZjxeFzx7WDcQBVWlmWZUAu/+0yqeQAuYL/DWaKMCKFp+ovEZzKNO8fPaLkU0RC9sHyE\nz3pqH2d5dQ2IuDCG03p0gpV1otsyFNUQveghWJqmH8fpdedLy7pkTlEzUZdxNS06WPIkOp4Q+NZb\nbwGYI5jd3V0URZFcIbglQdtY68Xz6uLmkFMBfJ5bOo+QvZqbas6qKBp1xJNDTYvk2ikYt+WBKvmV\nq1SKw7xOp4Pbt2/jox/9KIC52fTuu+/iww8/BDA/59XvU2L6bofqCXplWSaor0qHYYGq6eMd2c0K\nTS+Cqm5va74o2qm1szmsdn8dHrvpxzgwj8pR6cpLtNOY6XsefHJwf5tGo1HhKcqyTFfn6v4zNZO0\njutMD77refD9SlFfKoq5rww31fL8Xx7jefv27eSkR6EDpp4iGJXXJWcm+/OIt/F+4aLmtCtZHXPs\nQ0oNOC/6LcnBaIU5Meokn4bTjpUbfMPhEHfu3AFQnRkBpNPPorw4GlFlwh3QTvzqrMD86UzmDcdw\nEc/kA5fvOSei3yNnQC03RVEAL4JXPoln1Gp8REbRPT7KZVApOG+gdeNKj++5EtI8EQ16+dxpMOIK\nqFy8bjXfnodGo5F4GB67sLOzk45e0Lh1awI/5xYq+H1ZySmXHMkbIbqIK/S2o2KJlNXz5n3Fwaxk\nJSt5aXJtEIzzA8BlJOGci842CuX8PZ7oDsxRxvHxcbrq4/DwEAcHB6Hr+mQySZsaOctzKZIn0OkF\n6u4i7zauxu3/Hf1EtjDrJILHfCdaIvYZiflUFEeuw0/Z902Znn6UT3I3mgetX50NfRuDriKpaczf\n2M6KWHJIwTm0aGmXoiaShun1esmBE5jvb1MHugh5uomh4vzXIh5mGZMp57MSvett5/nLcS0+1paV\na6dg6mBeRHbp+5GZRLOBp75tbm5ie3s7bYjkDYI+QNhBuWx7dnYW3rTo57ioovCNgRTGrZ3QYb47\nU7lZpOE0/WjJ2HkAvyaF3Eu73a4sq7J8yrm4meWf3WT19qHokrXGrYdwR2mof4zWiyooHzjk17w+\n1WzT/qPtwKNK2V94IyTFzVstm+fT+Q7Nv4uG802eVKA5RaJhIgrBze2Ib4nGw/OYSNdGwXhD6Myk\niMBt5zryS4WdgieN0W+h3+9XfGcYDwc8Fczp6SlOT0/TrM9VkWjAR452PrjqbNycw5zb2r5i5Hnx\nOtFOq5es6UY8eqiSpNTjN5m+ruA4amP8OaKZ351PoQLwoy+0rqLB7O2tHJBK1FY+SKNVtmaziV6v\nV7muVzdYKg/oytDzGQ3SaDD7764UovQcnbloPBFqyb3n5dD6WUauhYJxoonijeCNoZ2dA81hsVf6\n2toa+v0+dnZ2AMwvjKd35uHhIQAkRVKWZVp65I5sKhz+Hi1xK2LgfiR3N+eAZjhXHByEGq/vSo5W\njCLRmYmiToP6LutfCV4lo92UY/59EnDF4IqQYXVQ+4AkSoyI8zphXXkduIJxBOMrO2y3yAzz8mu7\naDvxmR7hwTAeXw6he5+KlGOETLR8EeLxPFJySDXXv+pkRfKuZCUreWlyLRCMmzYRTOT/yN70sG6O\n6Pdms4l+v5/8Gnj73s7ODt5//30A87N89/b2Ko5nh4eHKIoiLU8Oh8OEMnIkGxEMz+FlPhV1MU/+\nvkNZLg9HaClCMBGM9q0B7vOi4bkkf3h4WCG6i+LiUjfyEwAumU3ko/ib5433YftVLTrjRkvXDtkd\nTbDecmaAfo8Qk/ed3JK31q9zIpGPi5tts9mscnc54/K2d/TE+vAx42g9GjuKfvwd4OJg8Bxiuwr3\nQrkWCga4DNHd8SzXaPp+ZM+ykyr/oAcBra+v48aNG9jd3U2+Mh988AEePXqEd955J602keSlgrl1\n61byg9HGVHjJDqodlfuXVFG4l2tOaUQE7jKiHZfvTafTitcuOy7zMRqNcHx8jCdPnqRbGk5OTrCx\nsYFbt24BQGWnetS59eAt3efEFTkAFYWt5p4e46mn0DlZy0Hi/SNSMnzOuCLS18N6W2kePKz23Yjz\n0fS9roCL9o34uWiDYpRfzWMuf3X8jyumHL+0rFwbBePKxJFHNANpBUUELxWLzvytVguz2cVp8v1+\nH9vb27h582ZyuDs8PMTDhw/xpS99CV//+tcBAB9++GHy+AUue5ACVfd04EKZKMopyzKt2rgNrZxF\n5FnKsMuQbB63PwOqqx2KtoC5Ejg8PMT+/n5SsqPRKDmf8f3T09PKkQb09C2KIikwXilDPov3Rity\n42ZAH/Bap1TmkfLwmdjRgqMM1n90Gp7Xi9ZZVL8Mq3lQfknLExGzGpd7HEcLAoxL448mrUgBRdyN\nIyitT+ZFx1o02efkeW92/GsA/kMAYwBfAfBny7Lce/bbC93s+CwOAHlNC1w+YoEDI2pEDQ9UL+Bi\nQwwGg2QKbG5uYjgcYm1tLd1r8/jxY+zv76cZl9vxvRM6UvAZgOaJkqU+c6lSqiPk6gbZovrU82jZ\nyabTaYXEbjQa6Pf7lTR12ZoKVJGV+s2osuLlb1rn7q+j5VUzwAeEKuccARm5AWh4V1S+Cqb5d/PH\nBxt/j2Z7nyS9LJEprHnwiTPyR2FckckUrYxFfkP63Sdub+NlkTOwHMn70wB+yJ79IoBPlWX53QD+\nFYDPPUtYb3b8IQD/U1EUTaxkJSv5QynPdbNjWZa/IF9/BcAff/b5uW92jGzaaAZS7c3rP/WdOi/K\nTPkqswlwQfzeu3cvHSy0t7eXlrGB+UxOElT9SSLt7r4SimIAJCSh5hdRgs6wUfkcilOiGV1n3W63\nW1ky5bK7zoq8NZJerAyr6NERAA8697yQr9K8lWVZ8btRc8iJby1H7oQ8ls3zRon6VIQafbaP+Ast\nG993fyRHL8pVReYIzbPoYKooTS+3X/3i5nFEIzji9/ZUJ8W69HPyzeBg/hyAv/fs83Pf7JgzM4Aq\nnM3BT4bLKSq3h6MwygP1ej00m81kEvHCdSUqj46O0n3YwOW7ffSEfO/Yeom9/1fOQOsn508TQdzI\ndNDyra2tVe60Jv/CMORSlBdxjklPoXM/D789UtuUxK2Wh57DbufXdX7G67yCyyJTifl205XxRlxK\nnSJzU4hlXjThUbm4QsuZwjnz0MMx7ly/z73j7y5KL5IXUjBFUfz3mF9P8nee493PAvgsML/21TW7\nkm1KRulyqNuKLl7B+tw7rs/e3JdDBLO5uYnxeJyIyqOjI6ytreHx48cpT6enp5UZiDO3z5w+KDmL\na2fPcQNezmhm8dURV7zswERORIJORrtybrVal06F42oU+SuSt7rtwJ3ViqJIR446D6VlYV1F3II/\nU1Tl/Uefq2i9qBOhKjnnx7zvRAsSLLMrPi+ji5OunlcNF73n5co5B0b1yc85pLaMNRDJczvaFUXx\nI5iTv/9JeZH6lW52LMvy7bIs3+ZGxJWsZCXfXvJcCKYoih8C8JcB/EBZlify03Pf7KjwXW1fIGbU\nfTZX9MEwujNXN6j5exHTToivrvPcQc3vwNwpj/HRMU/31/DGSYeX0+k0zfpEEm5qRZxBtNqhyKBu\ntSS3IsU6V3TiMBy4QCK++VHzydncr0lV9BmtEJHb8SXi3IpRtHeMwnpwNKbvMQ+6YsTPevuiHlSu\n5fO6c4TkfFO0jYX58fauQ6qaluehru1U3Frw3yLkfxWzSOV5b3b8HIAOgF98lvCvlGX5n5cvcLOj\n74uJCuTQThsscn5yBzbGERHBCl/VG9Q7LhVMt9tFr9ercBQ8UJwKhsSm759R0wK4OAGNov4ofE89\ngiNOgd+9XPrMfT4iM6yOUIxscPImrrTdRHGzj4OIeaJCcgVKxafl1boib6KmCNtdPaa9Pfm7Kg6m\npwdqMU+q/JUrcrOO+fR9V8yXh9fyuW8U/7tJpnWj8dTRBTnJ+dl4v3peed6bHf9WTfjnutkx4mCi\nzq6VGDlJ6f9IkXi8GlY7LivZ32NnGI/HODg4wMHBQeU8GH1nMplc2hxIBTebXezUpvMflZde6K6D\n0ncTR2VjWEd6Oa4GuBj8Wn5tl2gwaXlzHImv8LmSdQ5EZ3NVzK4s9X5v52nUgc5RlcZFZ0clp6NV\nLl/p0xU1F33fJzZKtHgRKYpFKNslUlyOUJieo7g6dOLt5mktkmvjyUuJZkmtLF8KrNOu3oD6XjRT\neNyu3em6DswVzNOnTysKxs0F7udRJcM8z2azhGC4L4fvtVotTCaTykypA9IVRA4pMM+L3otmTpan\nDtFEq106qKO25HefzZkHNVnoyKeHO/kybpSumwr8rArDzx/OmSfRvi2P1wcqP3vf1P6jdacomgjN\ntx2wTE6ya51GpqTXkebL2y7aphPJVRDNajf1Slaykpcm1w7BUFS7KlrJQUdFHHXvRWddOJzU9zV+\nNaPoGNbpdNIM62Qbb5b0S8yIKjgrOt/CozgXmYR8zveVa3DeJTKt1DzwZdUI/VGipWyNO2fXMy5P\ni3GST2HectyVmo7enor0Il5PEQyfa106kc4+5jcp6O8uaub786ge/T0V+ij58rGH1QUJugro+HCk\nxLJHCF/TiQj5q8i1UTCRSUSpg2Ru1mhc2vndT2IRBHQTjANcHcO2trZw69atdOcSlYIe+0gfEb3a\nxH0UHAbTPHBITVKS7/qqk+bTSUY3HbwO+HvOD8LrVePx8FEcXpd8FrWVlsPj8RshfcDrpBCZEM47\nuVnj6bVaLayvrycOJnI29PJFBK3zH3XcR5QXXeGKzHefWBmHm+YuGiZaFbyKORTJtVEwKrmKcPJs\nkVZV4o8DVZcgmZZzE7nZWzs9NwLu7OykO3KKYr7KxLuMyb/o8ZQRFxKhDj1DhmE4w7MMEUnHeLTj\ns95cwfh7vgPZ3fbZSX2QqALP8TL+HkWVgS9Js8waj26aBC6c/yiLFAzFN5MqYc1ndDPgsap8X+kw\ntwAAIABJREFUllMmKs715fgOF5Y5UlJed/osql9HiL5Ln+hIlZiiYE8jV9Y6uRYKxtFHjhT0FQuV\nyKMyR3yxYvW7mho64L0DqhJot9vY2tpKz7rdLrrdbopnb2/vkqernr2Si5uKRJUMnyk6cTivg1w7\nEcukSse3HPiqDj97h2T8DMM69t+8PP5d20glQjeez/F4fEnpaZjI+5Z51PxpXTIudVfodDqVM280\nD25mRAPQFXhUXpUc8nGF5hOGI2JF37oEznA+1hYpMK+XnHKMZEXyrmQlK3lpci0QDHD5EGRHItTW\nDgPVxnRNy13CGj6C73zfl+kimKhcSqPRQKvVSvZ5t9tNUBq4OC+FphLjjnxA1IGOs6sTlUQxbkqp\nT4gfWKTQWWcerW/1N/GZNno/Mi8pEZJ0UtvT0nbQ73ymxC/RoO/f0vLyzBpfrvY0FckVxfyArKK4\ncLTr9/sJvdTxV3x/0czudeV+N1EYF0VLjhA1Dm03PvN8R5aChtXPkTWwjFwbBePiMDHyjHQyKoKo\nZNc9nCosh5g5olM7pPpjcOD4wUocVOPxOJ2WR69dz7t2EnI2yh2xvMpTOMzX8L7ipKsMubrU3yia\nL9+sl/N49tURr1fnVfQ9VSaqgPl/PB4nU5EScVBa3slkUjED+Y4qBTWrND1ODhpOvbe1bJFJpOLP\n1EdJv0eKQeNwJc48RxOA50/Hgj5j/UfmXd19WIvk2imYaFakQogqQytLB6zGpY3PhvBGcAWVs0XV\njZy8zdHREQBcOmd2NpsfKTmZTHByMt+yRcXhB3hrXtix6WzH9BxZORLSVQAdJH76H4CKEx8HsysX\nj4v50M+uHKP28/Jp3TsnotsnuPqmS8tEbI48tU6Us2OYiJ/TvJIsHo1Gqbw8IpU764HLh5NrfIuW\nkr0NtC60viMlE73nSFLrRAlbz59LHSemvIsjwmVkxcGsZCUreWlybRBMjhfhd18+8xnIPwN5qB7Z\n/9FKSKTN+YyzKA+dAuaHWbfbbdy+fRvA/JCqJ0+e4OHDh+kcmdFohG63WznblhyIpquHOWletEy+\nWkK04uez6AoU49KT6YjMIhSnZoWaHZoHr8soX8onqamjNxsoWqFZqYeFl2V56bZJRRcMo/nQ55GJ\npOX1Hd1ra2tpQ6su2TsacJSs4n2yjmPJoeZoa0SERiLzJkJtzltGeaw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nAAAB\nW0lEQVSftIK5rPjZnPPrGLiGX0kp/bz+mAdTe5dmeu+ylRfAnwP4AoDXAawA+OPJZufsMGkFcynP\nUco53zv8vwrg2xiYwR+nlF4BgMP/q+UULgRK5b3wbSLn/HHO+SDn/BTAX+LIDbpwZZ+0gnkXwBdT\nSp9PKU1jQHB9Z8J5OlOklOZTSgv8DOAXAfwQg3K/dXjbWwD+fjI5/MxQKu93APxmSmkmpfR5nOBs\nrecFVKyH+HUM6h+4gGWf6HYNOedPUkq/D+CfAbwA4O2c839NMk+fAW4C+Pbh0vcpAH+Vc/6nlNK7\nGBzB+zsAPgTwGxPM4zNFis/W+iaC8uacT3221nlEoey/kFJ6HQO38AMAvwtcvLIDTSRvgwYNzhCT\ndpEaNGhwgdEomAYNGpwZGgXToEGDM0OjYBo0aHBmaBRMgwYNzgyNgmnQoMGZoVEwDRo0ODM0CqZB\ngwZnhv8Hn1W0X7B/3+gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d7692b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Look At Sample Image To Predict \n",
    "f = \"F/000680.png\"\n",
    "img = Image.open(\"data/yearbook/train/\"+f)    #TRUE RESPONSE:  2001\n",
    "%matplotlib inline\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction with probability 0.442535251 and index: 13 of 2013\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAA5FJREFUeJzt1MENwCAQwLDS/Xc+tgCJ2BPklTUzHwDv+28HAHCG4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+\nQIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5A\nhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE\n4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QITh\nA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOED\nRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+\nQIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5A\nhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE\n4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QITh\nA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOED\nRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QMQGL4sE9RSocXsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14c653780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pZUm7CLgUWCxpPrAeOKOvgqT7gQnA3pLmAG+yvQZ4N3A18BLgq+UVERHxPB0DVJmRN9j3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\nIlopASoiIlopASoiIlopASoiIlrp/wNK5guB0nKeeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14c777240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The following script is how I got predictions for a given image\n",
    "#python predict.py --image ../data/yearbook/train/F/000680.png --model inceptionv3-ft.model\n",
    "\n",
    "preds = [7.15491478e-05, 1.36070222e-01, 4.75838835e-09, 2.75789207e-05 , 2.34750900e-04, 1.39142768e-02, 2.95705236e-02, 1.12968966e-01 , 8.78592059e-02, 1.54093141e-02, 8.00036937e-02, 8.13279748e-02 , 6.58842146e-06, 4.42535251e-01]\n",
    "labels = [i + 2000  for i in range(14) ]\n",
    "\n",
    "print(\"Prediction with probability \"+ str(max(preds)) +\" and index: \"+ str(preds.index(max(preds))) + \" of \" +str(labels[preds.index(max(preds))]))\n",
    "plt.axis('off')\n",
    "plt.figure()\n",
    "\n",
    "plt.barh(labels, preds, alpha=0.5)\n",
    "plt.title(\"Predictions for \"+f)\n",
    "plt.xlabel('Probability')\n",
    "plt.xlim(0,1.01)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#savefig(\"pred.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3O2krGCXNDyfhD4dDXF5exs3NTdsui8XiRGcZ4hiKajSMTqTZb5pONknivy6v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\nohHVFS6c0jhKzCKs1leX4Z0x4brq0rkiRF1p5LhNc3rpFuf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qgalSpcrZpBqYKlWqnE2q\ngalSpcrZpBqYKlWqnE2qgalSpcrZpBqYKlWqnE2qgalSpcrZpBqYKlWqnE2qgalSpcrZpBqYKlWq\nnE2qgalSpcrZpBqYKlWqnE2qgalSpcrZpBqYKlWqnE2qgalSpcrZpBqYKlWqnE2qgalSpcrZ5P8D\nyW3fURrnuzkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14c568320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B0/srSLoXmAjsLWkO8Gbba4D3AVcB+wFfK4+IiIgX6Rigyoy8oX6vdPIQdXqGSO8DXtm0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\nRCslQEVERCv9f1q1DErJXFIuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14c550780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2 = \"F/000782.png\"   \n",
    "img2 = Image.open(\"data/yearbook/train/\"+f2)    #TRUE RESPONSE:  2013\n",
    "plt.imshow(img2)\n",
    "preds2 = [4.88650054e-04, 1.41260266e-01, 3.27573105e-07, 2.05188859e-04 , 1.27761823e-03, 2.58977674e-02, 4.79926243e-02, 1.43489718e-01 , 1.08343594e-01, 1.50818210e-02, 1.00244790e-01, 6.34194836e-02 , 5.84914196e-05, 3.52239668e-01]\n",
    "\n",
    "plt.axis('off')\n",
    "plt.figure()\n",
    "\n",
    "plt.barh(labels, preds2, alpha=0.5)\n",
    "plt.title(\"Predictions for \"+f2)\n",
    "plt.xlabel('Probability')\n",
    "plt.xlim(0,1.01)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Scratch ..help move image files into folder structure expected by keras for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Women:  (12149, 3)\n",
      "Men:  (10691, 3)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from shutil import copyfile\n",
    "\n",
    "train = pd.read_csv(\"data/yearbook/yearbook_train.txt\",names=['imagepath','year'],delimiter=\"\\t\")\n",
    "train['gender'] = [ t.split(\"/\")[0] for t in train['imagepath']]\n",
    "train['imagepath'] = [ t.split(\"/\")[1] for t in train['imagepath']]\n",
    "\n",
    "print(\"Women: \",train[train['gender']=='F'].shape)\n",
    "print(\"Men: \",train[train['gender']=='M'].shape)\n",
    "\n",
    "def create_folder_with_classes(input_folder,output_folder,train):\n",
    "    os.system(\"mkdir data/yearbook/\"+output_folder)\n",
    "    os.system(\"mkdir data/yearbook/\"+output_folder+\"/F\")\n",
    "    os.system(\"mkdir data/yearbook/\"+output_folder+\"/M\")\n",
    "    #make women folders and copy over file\n",
    "    womenyears = sorted(train[train['gender']=='F'][\"year\"].unique())\n",
    "    for y in womenyears:\n",
    "        curfolder = \"data/yearbook/\"+output_folder+\"/F/\"+str(y)\n",
    "        os.system(\"mkdir \"+curfolder)\n",
    "        imgs = train[(train[\"year\"] == y) & (train[\"gender\"] == 'F')][\"imagepath\"]\n",
    "        for i in imgs:\n",
    "            copyfile(input_folder+\"/F/\"+i, curfolder+\"/\"+i)\n",
    "        \n",
    "    #make men folders\n",
    "    menyears = sorted(train[train['gender']=='M'][\"year\"].unique())\n",
    "    for y in menyears:\n",
    "        curfolder = \"data/yearbook/\"+output_folder+\"/M/\"+str(y)\n",
    "        os.system(\"mkdir \"+curfolder)\n",
    "        imgs = train[(train[\"year\"] == y) & (train[\"gender\"] == 'M')][\"imagepath\"]\n",
    "        for i in imgs:\n",
    "            copyfile(input_folder+\"/M/\"+i, curfolder+\"/\"+i)\n",
    "\n",
    "#create_folder_with_classes(\"data/yearbook/train\",\"train_inception3\",train)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}