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],
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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------------------------------------\n",
"Number of Classes: 15\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"all_mins, all_maxs = [], []\n",
"\n",
"for image_batch, _ in train_ds: # goes through the entire dataset\n",
" all_mins.append(np.min(image_batch.numpy()))\n",
" all_maxs.append(np.max(image_batch.numpy()))\n",
"\n",
"print(\"Global min pixel value:\", np.min(all_mins))\n",
"print(\"Global max pixel value:\", np.max(all_maxs))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RSTOaMfh1YSE",
"outputId": "9494a469-4b3f-487a-ac90-d1dbbd7cc409"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Global min pixel value: 0.0\n",
"Global max pixel value: 1.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# After train_ds is defined\n",
"for image_batch, label_batch in train_ds.take(1):\n",
" print(\"Image batch shape:\", image_batch.shape) # full batch shape\n",
" print(\"Label batch shape:\", label_batch.shape) # labels shape\n",
"\n",
" input_shape = image_batch.shape[1:] # shape of a single image\n",
" print(\"Single image shape:\", input_shape)\n",
" break"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bCPj-EuU1ai0",
"outputId": "30a1ec02-cb66-4b07-e84c-78acdcbbf076"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Image batch shape: (16, 225, 225, 3)\n",
"Label batch shape: (16,)\n",
"Single image shape: (225, 225, 3)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Model Loading**"
],
"metadata": {
"id": "g4aZrOoNB7Jg"
}
},
{
"cell_type": "code",
"source": [
"inception = InceptionV3(input_shape=input_shape, weights='imagenet', include_top=False)\n",
"\n",
"# don't train existing weights\n",
"for layer in inception.layers:\n",
" layer.trainable = False\n",
"\n",
"# Number of classes\n",
"print(\"Number of Classes: \", len(le.classes_))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G2WuxEF418S8",
"outputId": "e7f279b0-3246-4dbc-8c70-9ecd6663d852"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
"\u001b[1m87910968/87910968\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 0us/step\n",
"Number of Classes: 15\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"x = GlobalAveragePooling2D()(inception.output)\n",
"x = Dense(512, activation='relu')(x)\n",
"x = Dropout(0.5)(x)\n",
"prediction = Dense(len(le.classes_), activation='softmax')(x) # \"sigmoid\" for two classes\n",
"\n",
"# create a model object\n",
"model = Model(inputs=inception.input, outputs=prediction)\n",
"\n",
"# view the structure of the model\n",
"model.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "cstslvnr2CsZ",
"outputId": "de80d09a-8b91-4e7c-f0e6-621a25bf9a4e"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1mModel: \"functional\"\u001b[0m\n"
],
"text/html": [
"Model: \"functional\"\n",
"\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
"│ input_layer │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m225\u001b[0m, \u001b[38;5;34m225\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n",
"│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m110\u001b[0m, \u001b[38;5;34m110\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m110\u001b[0m, \u001b[38;5;34m110\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m110\u001b[0m, \u001b[38;5;34m110\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m110\u001b[0m, \u001b[38;5;34m110\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m110\u001b[0m, \u001b[38;5;34m110\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m110\u001b[0m, \u001b[38;5;34m110\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ max_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m5,120\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m80\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m240\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m138,240\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ max_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
"│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ average_pooling2… │\n",
"│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed0 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ │ │ │ activation_10[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_11[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ average_pooling2… │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_18 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ activation_14[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_17[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_18[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_22 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_20 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_23 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ average_pooling2… │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_19 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_21 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_24 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_25 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ activation_21[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_24[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_25[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_27 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_27[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_28 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m995,328\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m384\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_28[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_26 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_29 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ max_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_26[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_29[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ max_pooling2d_2[\u001b[38;5;34m…\u001b[0m │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_34 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m98,304\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_34 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_35 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_34[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_35[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_35 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m98,304\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_35[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_36[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_31 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_36 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_32 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_31[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_37 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_36[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_37[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_32 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_37 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_33 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m172,032\u001b[0m │ activation_32[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_38 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m172,032\u001b[0m │ activation_37[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_39 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_39[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_30 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_33 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_38 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_39 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_30[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_33[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_38[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_39[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_44 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_44[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_44 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_45 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_44[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_45[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_45 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_41 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_46 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_45[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_41[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_46[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_41 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_46 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_42 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_41[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_47 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_46[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_42[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_47[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_42 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_47 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_40 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_43 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_42[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_48 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_47[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_49 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_40[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_43[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_48[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_49[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_40 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_43 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_48 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_49 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_40[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_43[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_48[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_49[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_54 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_54[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_54 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_55 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_54[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_55[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_55 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_51 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_56 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_55[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_51[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_56[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_51 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_56 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_52 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_51[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_57 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_56[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_52[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_57[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_52 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_57 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_50 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_53 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_52[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_58 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_57[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_59 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_50[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_53[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_58[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_59[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_50 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_53 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_58 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_59 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_50[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_53[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_58[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_59[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_64 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_64[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_64 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_65 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_64[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_65[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_65 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_61 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_66 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_65[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_61[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_66[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_61 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_66 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_62 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_61[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_67 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_66[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_62[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_67[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_62 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_67 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_60 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_63 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_62[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_68 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_67[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_69 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_60[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_63[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_68[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_69[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_60 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_63 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_68 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_69 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_60[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_63[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_68[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_69[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_72 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_72[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_72 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_73 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_72[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_73[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_73 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_70 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_74 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_73[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_70[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_74[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_70 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_74 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_71 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m552,960\u001b[0m │ activation_70[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_75 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m331,776\u001b[0m │ activation_74[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_71[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_75[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_71 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_75 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ max_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_71[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m1280\u001b[0m) │ │ activation_75[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ max_pooling2d_3[\u001b[38;5;34m…\u001b[0m │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_80 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m573,440\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m1,344\u001b[0m │ conv2d_80[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_80 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_77 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m491,520\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_81 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,548,288\u001b[0m │ activation_80[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_77[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_81[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_77 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_81 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_78 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_77[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_79 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_77[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_82 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_81[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_83 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_81[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_76 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m409,600\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_78[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_79[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_82[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_83[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_84 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m245,760\u001b[0m │ average_pooling2… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_76[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_78 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_79 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_82 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_83 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_84[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_76 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed9_0 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_78[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_79[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ concatenate │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_82[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_83[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_84 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_76[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ mixed9_0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
"│ │ │ │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
"│ │ │ │ activation_84[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_89 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m917,504\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m1,344\u001b[0m │ conv2d_89[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_89 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_86 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_90 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,548,288\u001b[0m │ activation_89[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_86[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_90[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_86 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_90 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_87 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_86[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_88 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_86[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_91 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_90[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_92 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_90[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_85 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m655,360\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_87[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_88[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_91[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_92[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_93 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m393,216\u001b[0m │ average_pooling2… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_85[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_87 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_88 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_91 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_92 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_93[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_85 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed9_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_87[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_88[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ concatenate_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_91[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_92[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_93 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_85[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ mixed9_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
"│ │ │ │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ │ │ │ activation_93[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ global_average_p… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m) │ \u001b[38;5;34m7,695\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
],
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
"│ input_layer │ (None, 225, 225, │ 0 │ - │\n",
"│ (InputLayer) │ 3) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d (Conv2D) │ (None, 112, 112, │ 864 │ input_layer[0][0] │\n",
"│ │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalization │ (None, 112, 112, │ 96 │ conv2d[0][0] │\n",
"│ (BatchNormalizatio… │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation │ (None, 112, 112, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_1 (Conv2D) │ (None, 110, 110, │ 9,216 │ activation[0][0] │\n",
"│ │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 110, 110, │ 96 │ conv2d_1[0][0] │\n",
"│ (BatchNormalizatio… │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_1 │ (None, 110, 110, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_2 (Conv2D) │ (None, 110, 110, │ 18,432 │ activation_1[0][… │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 110, 110, │ 192 │ conv2d_2[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_2 │ (None, 110, 110, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ max_pooling2d │ (None, 54, 54, │ 0 │ activation_2[0][… │\n",
"│ (MaxPooling2D) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_3 (Conv2D) │ (None, 54, 54, │ 5,120 │ max_pooling2d[0]… │\n",
"│ │ 80) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 54, 54, │ 240 │ conv2d_3[0][0] │\n",
"│ (BatchNormalizatio… │ 80) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_3 │ (None, 54, 54, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 80) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_4 (Conv2D) │ (None, 52, 52, │ 138,240 │ activation_3[0][… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 52, 52, │ 576 │ conv2d_4[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_4 │ (None, 52, 52, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ max_pooling2d_1 │ (None, 25, 25, │ 0 │ activation_4[0][… │\n",
"│ (MaxPooling2D) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_8 (Conv2D) │ (None, 25, 25, │ 12,288 │ max_pooling2d_1[… │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_8[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_8 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_6 (Conv2D) │ (None, 25, 25, │ 9,216 │ max_pooling2d_1[… │\n",
"│ │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_9 (Conv2D) │ (None, 25, 25, │ 55,296 │ activation_8[0][… │\n",
"│ │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 144 │ conv2d_6[0][0] │\n",
"│ (BatchNormalizatio… │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 288 │ conv2d_9[0][0] │\n",
"│ (BatchNormalizatio… │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_6 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_9 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d │ (None, 25, 25, │ 0 │ max_pooling2d_1[… │\n",
"│ (AveragePooling2D) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_5 (Conv2D) │ (None, 25, 25, │ 12,288 │ max_pooling2d_1[… │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_7 (Conv2D) │ (None, 25, 25, │ 76,800 │ activation_6[0][… │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_10 (Conv2D) │ (None, 25, 25, │ 82,944 │ activation_9[0][… │\n",
"│ │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_11 (Conv2D) │ (None, 25, 25, │ 6,144 │ average_pooling2… │\n",
"│ │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_5[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_7[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 288 │ conv2d_10[0][0] │\n",
"│ (BatchNormalizatio… │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 96 │ conv2d_11[0][0] │\n",
"│ (BatchNormalizatio… │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_5 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_7 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_10 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_11 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 32) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed0 │ (None, 25, 25, │ 0 │ activation_5[0][… │\n",
"│ (Concatenate) │ 256) │ │ activation_7[0][… │\n",
"│ │ │ │ activation_10[0]… │\n",
"│ │ │ │ activation_11[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_15 (Conv2D) │ (None, 25, 25, │ 16,384 │ mixed0[0][0] │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_15[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_15 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_13 (Conv2D) │ (None, 25, 25, │ 12,288 │ mixed0[0][0] │\n",
"│ │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_16 (Conv2D) │ (None, 25, 25, │ 55,296 │ activation_15[0]… │\n",
"│ │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 144 │ conv2d_13[0][0] │\n",
"│ (BatchNormalizatio… │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 288 │ conv2d_16[0][0] │\n",
"│ (BatchNormalizatio… │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_13 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_16 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_1 │ (None, 25, 25, │ 0 │ mixed0[0][0] │\n",
"│ (AveragePooling2D) │ 256) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_12 (Conv2D) │ (None, 25, 25, │ 16,384 │ mixed0[0][0] │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_14 (Conv2D) │ (None, 25, 25, │ 76,800 │ activation_13[0]… │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_17 (Conv2D) │ (None, 25, 25, │ 82,944 │ activation_16[0]… │\n",
"│ │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_18 (Conv2D) │ (None, 25, 25, │ 16,384 │ average_pooling2… │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_12[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_14[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 288 │ conv2d_17[0][0] │\n",
"│ (BatchNormalizatio… │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_18[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_12 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_14 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_17 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_18 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed1 │ (None, 25, 25, │ 0 │ activation_12[0]… │\n",
"│ (Concatenate) │ 288) │ │ activation_14[0]… │\n",
"│ │ │ │ activation_17[0]… │\n",
"│ │ │ │ activation_18[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_22 (Conv2D) │ (None, 25, 25, │ 18,432 │ mixed1[0][0] │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_22[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_22 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_20 (Conv2D) │ (None, 25, 25, │ 13,824 │ mixed1[0][0] │\n",
"│ │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_23 (Conv2D) │ (None, 25, 25, │ 55,296 │ activation_22[0]… │\n",
"│ │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 144 │ conv2d_20[0][0] │\n",
"│ (BatchNormalizatio… │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 288 │ conv2d_23[0][0] │\n",
"│ (BatchNormalizatio… │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_20 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 48) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_23 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_2 │ (None, 25, 25, │ 0 │ mixed1[0][0] │\n",
"│ (AveragePooling2D) │ 288) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_19 (Conv2D) │ (None, 25, 25, │ 18,432 │ mixed1[0][0] │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_21 (Conv2D) │ (None, 25, 25, │ 76,800 │ activation_20[0]… │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_24 (Conv2D) │ (None, 25, 25, │ 82,944 │ activation_23[0]… │\n",
"│ │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_25 (Conv2D) │ (None, 25, 25, │ 18,432 │ average_pooling2… │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_19[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_21[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 288 │ conv2d_24[0][0] │\n",
"│ (BatchNormalizatio… │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_25[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_19 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_21 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_24 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_25 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed2 │ (None, 25, 25, │ 0 │ activation_19[0]… │\n",
"│ (Concatenate) │ 288) │ │ activation_21[0]… │\n",
"│ │ │ │ activation_24[0]… │\n",
"│ │ │ │ activation_25[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_27 (Conv2D) │ (None, 25, 25, │ 18,432 │ mixed2[0][0] │\n",
"│ │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 192 │ conv2d_27[0][0] │\n",
"│ (BatchNormalizatio… │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_27 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 64) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_28 (Conv2D) │ (None, 25, 25, │ 55,296 │ activation_27[0]… │\n",
"│ │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 25, 25, │ 288 │ conv2d_28[0][0] │\n",
"│ (BatchNormalizatio… │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_28 │ (None, 25, 25, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_26 (Conv2D) │ (None, 12, 12, │ 995,328 │ mixed2[0][0] │\n",
"│ │ 384) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_29 (Conv2D) │ (None, 12, 12, │ 82,944 │ activation_28[0]… │\n",
"│ │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 1,152 │ conv2d_26[0][0] │\n",
"│ (BatchNormalizatio… │ 384) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 288 │ conv2d_29[0][0] │\n",
"│ (BatchNormalizatio… │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_26 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 384) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_29 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 96) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ max_pooling2d_2 │ (None, 12, 12, │ 0 │ mixed2[0][0] │\n",
"│ (MaxPooling2D) │ 288) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed3 │ (None, 12, 12, │ 0 │ activation_26[0]… │\n",
"│ (Concatenate) │ 768) │ │ activation_29[0]… │\n",
"│ │ │ │ max_pooling2d_2[… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_34 (Conv2D) │ (None, 12, 12, │ 98,304 │ mixed3[0][0] │\n",
"│ │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 384 │ conv2d_34[0][0] │\n",
"│ (BatchNormalizatio… │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_34 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_35 (Conv2D) │ (None, 12, 12, │ 114,688 │ activation_34[0]… │\n",
"│ │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 384 │ conv2d_35[0][0] │\n",
"│ (BatchNormalizatio… │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_35 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_31 (Conv2D) │ (None, 12, 12, │ 98,304 │ mixed3[0][0] │\n",
"│ │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_36 (Conv2D) │ (None, 12, 12, │ 114,688 │ activation_35[0]… │\n",
"│ │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 384 │ conv2d_31[0][0] │\n",
"│ (BatchNormalizatio… │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 384 │ conv2d_36[0][0] │\n",
"│ (BatchNormalizatio… │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_31 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_36 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_32 (Conv2D) │ (None, 12, 12, │ 114,688 │ activation_31[0]… │\n",
"│ │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_37 (Conv2D) │ (None, 12, 12, │ 114,688 │ activation_36[0]… │\n",
"│ │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 384 │ conv2d_32[0][0] │\n",
"│ (BatchNormalizatio… │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 384 │ conv2d_37[0][0] │\n",
"│ (BatchNormalizatio… │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_32 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_37 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 128) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_3 │ (None, 12, 12, │ 0 │ mixed3[0][0] │\n",
"│ (AveragePooling2D) │ 768) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_30 (Conv2D) │ (None, 12, 12, │ 147,456 │ mixed3[0][0] │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_33 (Conv2D) │ (None, 12, 12, │ 172,032 │ activation_32[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_38 (Conv2D) │ (None, 12, 12, │ 172,032 │ activation_37[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_39 (Conv2D) │ (None, 12, 12, │ 147,456 │ average_pooling2… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_30[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_33[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_38[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_39[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_30 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_33 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_38 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_39 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed4 │ (None, 12, 12, │ 0 │ activation_30[0]… │\n",
"│ (Concatenate) │ 768) │ │ activation_33[0]… │\n",
"│ │ │ │ activation_38[0]… │\n",
"│ │ │ │ activation_39[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_44 (Conv2D) │ (None, 12, 12, │ 122,880 │ mixed4[0][0] │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_44[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_44 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_45 (Conv2D) │ (None, 12, 12, │ 179,200 │ activation_44[0]… │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_45[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_45 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_41 (Conv2D) │ (None, 12, 12, │ 122,880 │ mixed4[0][0] │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_46 (Conv2D) │ (None, 12, 12, │ 179,200 │ activation_45[0]… │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_41[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_46[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_41 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_46 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_42 (Conv2D) │ (None, 12, 12, │ 179,200 │ activation_41[0]… │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_47 (Conv2D) │ (None, 12, 12, │ 179,200 │ activation_46[0]… │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_42[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_47[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_42 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_47 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_4 │ (None, 12, 12, │ 0 │ mixed4[0][0] │\n",
"│ (AveragePooling2D) │ 768) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_40 (Conv2D) │ (None, 12, 12, │ 147,456 │ mixed4[0][0] │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_43 (Conv2D) │ (None, 12, 12, │ 215,040 │ activation_42[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_48 (Conv2D) │ (None, 12, 12, │ 215,040 │ activation_47[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_49 (Conv2D) │ (None, 12, 12, │ 147,456 │ average_pooling2… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_40[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_43[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_48[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_49[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_40 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_43 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_48 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_49 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed5 │ (None, 12, 12, │ 0 │ activation_40[0]… │\n",
"│ (Concatenate) │ 768) │ │ activation_43[0]… │\n",
"│ │ │ │ activation_48[0]… │\n",
"│ │ │ │ activation_49[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_54 (Conv2D) │ (None, 12, 12, │ 122,880 │ mixed5[0][0] │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_54[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_54 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_55 (Conv2D) │ (None, 12, 12, │ 179,200 │ activation_54[0]… │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_55[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_55 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_51 (Conv2D) │ (None, 12, 12, │ 122,880 │ mixed5[0][0] │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_56 (Conv2D) │ (None, 12, 12, │ 179,200 │ activation_55[0]… │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_51[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_56[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_51 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_56 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_52 (Conv2D) │ (None, 12, 12, │ 179,200 │ activation_51[0]… │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_57 (Conv2D) │ (None, 12, 12, │ 179,200 │ activation_56[0]… │\n",
"│ │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_52[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 480 │ conv2d_57[0][0] │\n",
"│ (BatchNormalizatio… │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_52 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_57 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 160) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_5 │ (None, 12, 12, │ 0 │ mixed5[0][0] │\n",
"│ (AveragePooling2D) │ 768) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_50 (Conv2D) │ (None, 12, 12, │ 147,456 │ mixed5[0][0] │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_53 (Conv2D) │ (None, 12, 12, │ 215,040 │ activation_52[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_58 (Conv2D) │ (None, 12, 12, │ 215,040 │ activation_57[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_59 (Conv2D) │ (None, 12, 12, │ 147,456 │ average_pooling2… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_50[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_53[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_58[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_59[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_50 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_53 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_58 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_59 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed6 │ (None, 12, 12, │ 0 │ activation_50[0]… │\n",
"│ (Concatenate) │ 768) │ │ activation_53[0]… │\n",
"│ │ │ │ activation_58[0]… │\n",
"│ │ │ │ activation_59[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_64 (Conv2D) │ (None, 12, 12, │ 147,456 │ mixed6[0][0] │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_64[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_64 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_65 (Conv2D) │ (None, 12, 12, │ 258,048 │ activation_64[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_65[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_65 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_61 (Conv2D) │ (None, 12, 12, │ 147,456 │ mixed6[0][0] │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_66 (Conv2D) │ (None, 12, 12, │ 258,048 │ activation_65[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_61[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_66[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_61 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_66 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_62 (Conv2D) │ (None, 12, 12, │ 258,048 │ activation_61[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_67 (Conv2D) │ (None, 12, 12, │ 258,048 │ activation_66[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_62[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_67[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_62 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_67 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_6 │ (None, 12, 12, │ 0 │ mixed6[0][0] │\n",
"│ (AveragePooling2D) │ 768) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_60 (Conv2D) │ (None, 12, 12, │ 147,456 │ mixed6[0][0] │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_63 (Conv2D) │ (None, 12, 12, │ 258,048 │ activation_62[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_68 (Conv2D) │ (None, 12, 12, │ 258,048 │ activation_67[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_69 (Conv2D) │ (None, 12, 12, │ 147,456 │ average_pooling2… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_60[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_63[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_68[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_69[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_60 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_63 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_68 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_69 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed7 │ (None, 12, 12, │ 0 │ activation_60[0]… │\n",
"│ (Concatenate) │ 768) │ │ activation_63[0]… │\n",
"│ │ │ │ activation_68[0]… │\n",
"│ │ │ │ activation_69[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_72 (Conv2D) │ (None, 12, 12, │ 147,456 │ mixed7[0][0] │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_72[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_72 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_73 (Conv2D) │ (None, 12, 12, │ 258,048 │ activation_72[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_73[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_73 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_70 (Conv2D) │ (None, 12, 12, │ 147,456 │ mixed7[0][0] │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_74 (Conv2D) │ (None, 12, 12, │ 258,048 │ activation_73[0]… │\n",
"│ │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_70[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 12, 12, │ 576 │ conv2d_74[0][0] │\n",
"│ (BatchNormalizatio… │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_70 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_74 │ (None, 12, 12, │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ 192) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_71 (Conv2D) │ (None, 5, 5, 320) │ 552,960 │ activation_70[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_75 (Conv2D) │ (None, 5, 5, 192) │ 331,776 │ activation_74[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 320) │ 960 │ conv2d_71[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 192) │ 576 │ conv2d_75[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_71 │ (None, 5, 5, 320) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_75 │ (None, 5, 5, 192) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ max_pooling2d_3 │ (None, 5, 5, 768) │ 0 │ mixed7[0][0] │\n",
"│ (MaxPooling2D) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed8 │ (None, 5, 5, │ 0 │ activation_71[0]… │\n",
"│ (Concatenate) │ 1280) │ │ activation_75[0]… │\n",
"│ │ │ │ max_pooling2d_3[… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_80 (Conv2D) │ (None, 5, 5, 448) │ 573,440 │ mixed8[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 448) │ 1,344 │ conv2d_80[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_80 │ (None, 5, 5, 448) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_77 (Conv2D) │ (None, 5, 5, 384) │ 491,520 │ mixed8[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_81 (Conv2D) │ (None, 5, 5, 384) │ 1,548,288 │ activation_80[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_77[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_81[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_77 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_81 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_78 (Conv2D) │ (None, 5, 5, 384) │ 442,368 │ activation_77[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_79 (Conv2D) │ (None, 5, 5, 384) │ 442,368 │ activation_77[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_82 (Conv2D) │ (None, 5, 5, 384) │ 442,368 │ activation_81[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_83 (Conv2D) │ (None, 5, 5, 384) │ 442,368 │ activation_81[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_7 │ (None, 5, 5, │ 0 │ mixed8[0][0] │\n",
"│ (AveragePooling2D) │ 1280) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_76 (Conv2D) │ (None, 5, 5, 320) │ 409,600 │ mixed8[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_78[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_79[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_82[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_83[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_84 (Conv2D) │ (None, 5, 5, 192) │ 245,760 │ average_pooling2… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 320) │ 960 │ conv2d_76[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_78 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_79 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_82 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_83 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 192) │ 576 │ conv2d_84[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_76 │ (None, 5, 5, 320) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed9_0 │ (None, 5, 5, 768) │ 0 │ activation_78[0]… │\n",
"│ (Concatenate) │ │ │ activation_79[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ concatenate │ (None, 5, 5, 768) │ 0 │ activation_82[0]… │\n",
"│ (Concatenate) │ │ │ activation_83[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_84 │ (None, 5, 5, 192) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed9 │ (None, 5, 5, │ 0 │ activation_76[0]… │\n",
"│ (Concatenate) │ 2048) │ │ mixed9_0[0][0], │\n",
"│ │ │ │ concatenate[0][0… │\n",
"│ │ │ │ activation_84[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_89 (Conv2D) │ (None, 5, 5, 448) │ 917,504 │ mixed9[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 448) │ 1,344 │ conv2d_89[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_89 │ (None, 5, 5, 448) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_86 (Conv2D) │ (None, 5, 5, 384) │ 786,432 │ mixed9[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_90 (Conv2D) │ (None, 5, 5, 384) │ 1,548,288 │ activation_89[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_86[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_90[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_86 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_90 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_87 (Conv2D) │ (None, 5, 5, 384) │ 442,368 │ activation_86[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_88 (Conv2D) │ (None, 5, 5, 384) │ 442,368 │ activation_86[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_91 (Conv2D) │ (None, 5, 5, 384) │ 442,368 │ activation_90[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_92 (Conv2D) │ (None, 5, 5, 384) │ 442,368 │ activation_90[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ average_pooling2d_8 │ (None, 5, 5, │ 0 │ mixed9[0][0] │\n",
"│ (AveragePooling2D) │ 2048) │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_85 (Conv2D) │ (None, 5, 5, 320) │ 655,360 │ mixed9[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_87[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_88[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_91[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 384) │ 1,152 │ conv2d_92[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ conv2d_93 (Conv2D) │ (None, 5, 5, 192) │ 393,216 │ average_pooling2… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 320) │ 960 │ conv2d_85[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_87 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_88 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_91 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_92 │ (None, 5, 5, 384) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ batch_normalizatio… │ (None, 5, 5, 192) │ 576 │ conv2d_93[0][0] │\n",
"│ (BatchNormalizatio… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_85 │ (None, 5, 5, 320) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed9_1 │ (None, 5, 5, 768) │ 0 │ activation_87[0]… │\n",
"│ (Concatenate) │ │ │ activation_88[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ concatenate_1 │ (None, 5, 5, 768) │ 0 │ activation_91[0]… │\n",
"│ (Concatenate) │ │ │ activation_92[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ activation_93 │ (None, 5, 5, 192) │ 0 │ batch_normalizat… │\n",
"│ (Activation) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ mixed10 │ (None, 5, 5, │ 0 │ activation_85[0]… │\n",
"│ (Concatenate) │ 2048) │ │ mixed9_1[0][0], │\n",
"│ │ │ │ concatenate_1[0]… │\n",
"│ │ │ │ activation_93[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ global_average_poo… │ (None, 2048) │ 0 │ mixed10[0][0] │\n",
"│ (GlobalAveragePool… │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ dense (Dense) │ (None, 512) │ 1,049,088 │ global_average_p… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ dropout (Dropout) │ (None, 512) │ 0 │ dense[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ dense_1 (Dense) │ (None, 15) │ 7,695 │ dropout[0][0] │\n",
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
"\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m22,859,567\u001b[0m (87.20 MB)\n"
],
"text/html": [
" Total params: 22,859,567 (87.20 MB)\n",
"\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,056,783\u001b[0m (4.03 MB)\n"
],
"text/html": [
" Trainable params: 1,056,783 (4.03 MB)\n",
"\n"
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},
"metadata": {}
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{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m21,802,784\u001b[0m (83.17 MB)\n"
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"text/html": [
" Non-trainable params: 21,802,784 (83.17 MB)\n",
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"metadata": {}
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},
{
"cell_type": "code",
"source": [
"# tell the model what cost and optimization method to use\n",
"model.compile(\n",
" loss='sparse_categorical_crossentropy', # Two classes\n",
" optimizer='adam',\n",
" metrics=['accuracy']\n",
")"
],
"metadata": {
"id": "7_f5SNBI2Fxj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# **Model Feature Extraction**"
],
"metadata": {
"id": "_DmYccdpcUsK"
}
},
{
"cell_type": "code",
"source": [
"callbacks = [\n",
" EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True, verbose = 1),\n",
" ModelCheckpoint(\"best_model.h5\", save_best_only=True, monitor='val_loss', verbose = 1),\n",
" ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, min_lr=1e-5, verbose=1)\n",
"]\n",
"\n",
"history = model.fit(train_ds, validation_data=val_ds, epochs=5, callbacks=callbacks, verbose = 1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xAxGcSZA2ITy",
"outputId": "34466ca4-3a59-4fd4-bf98-1b0631b9e797"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/5\n",
"\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.4911 - loss: 1.9572\n",
"Epoch 1: val_loss improved from inf to 0.76183, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 93ms/step - accuracy: 0.4913 - loss: 1.9563 - val_accuracy: 0.7654 - val_loss: 0.7618 - learning_rate: 0.0010\n",
"Epoch 2/5\n",
"\u001b[1m612/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.6986 - loss: 0.9668\n",
"Epoch 2: val_loss improved from 0.76183 to 0.73211, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 43ms/step - accuracy: 0.6986 - loss: 0.9668 - val_accuracy: 0.7534 - val_loss: 0.7321 - learning_rate: 0.0010\n",
"Epoch 3/5\n",
"\u001b[1m612/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.7119 - loss: 0.9227\n",
"Epoch 3: val_loss improved from 0.73211 to 0.64872, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 43ms/step - accuracy: 0.7119 - loss: 0.9226 - val_accuracy: 0.8066 - val_loss: 0.6487 - learning_rate: 0.0010\n",
"Epoch 4/5\n",
"\u001b[1m613/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.7365 - loss: 0.8481\n",
"Epoch 4: val_loss improved from 0.64872 to 0.63308, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 44ms/step - accuracy: 0.7365 - loss: 0.8481 - val_accuracy: 0.7892 - val_loss: 0.6331 - learning_rate: 0.0010\n",
"Epoch 5/5\n",
"\u001b[1m612/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.7582 - loss: 0.7761\n",
"Epoch 5: val_loss improved from 0.63308 to 0.62677, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 44ms/step - accuracy: 0.7581 - loss: 0.7762 - val_accuracy: 0.8020 - val_loss: 0.6268 - learning_rate: 0.0010\n",
"Restoring model weights from the end of the best epoch: 5.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Model Fine-Tuning**"
],
"metadata": {
"id": "-VLoPXkRB_oC"
}
},
{
"cell_type": "code",
"source": [
"#Fine Tuning\n",
"for layer in inception.layers[-30:]: # Unfreeze last 30 layers (tune as needed)\n",
" layer.trainable = True\n",
"\n",
"# tell the model what cost and optimization method to use\n",
"model.compile(\n",
" loss='sparse_categorical_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy']\n",
")\n",
"\n",
"callbacks = [\n",
" EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True, verbose = 1),\n",
" ModelCheckpoint(\"best_model.h5\", save_best_only=True, monitor='val_loss', verbose = 1),\n",
" ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=7, min_lr=1e-5, verbose=1)\n",
"]\n",
"\n",
"history = model.fit(train_ds, validation_data=val_ds, epochs=10, callbacks=callbacks, verbose = 1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6IY6a3HacZa6",
"outputId": "e02fe166-f68e-4ea8-8bca-fa67f9124587"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n",
"\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.6689 - loss: 1.1124\n",
"Epoch 1: val_loss improved from inf to 0.52113, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 100ms/step - accuracy: 0.6690 - loss: 1.1121 - val_accuracy: 0.8478 - val_loss: 0.5211 - learning_rate: 0.0010\n",
"Epoch 2/10\n",
"\u001b[1m613/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.8253 - loss: 0.5925\n",
"Epoch 2: val_loss improved from 0.52113 to 0.45573, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 44ms/step - accuracy: 0.8253 - loss: 0.5926 - val_accuracy: 0.8579 - val_loss: 0.4557 - learning_rate: 0.0010\n",
"Epoch 3/10\n",
"\u001b[1m612/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.8560 - loss: 0.4791\n",
"Epoch 3: val_loss improved from 0.45573 to 0.40941, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 44ms/step - accuracy: 0.8560 - loss: 0.4791 - val_accuracy: 0.8836 - val_loss: 0.4094 - learning_rate: 0.0010\n",
"Epoch 4/10\n",
"\u001b[1m612/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.8896 - loss: 0.3689\n",
"Epoch 4: val_loss did not improve from 0.40941\n",
"\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 43ms/step - accuracy: 0.8896 - loss: 0.3690 - val_accuracy: 0.8836 - val_loss: 0.4392 - learning_rate: 0.0010\n",
"Epoch 5/10\n",
"\u001b[1m612/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.9138 - loss: 0.3028\n",
"Epoch 5: val_loss did not improve from 0.40941\n",
"\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 44ms/step - accuracy: 0.9137 - loss: 0.3028 - val_accuracy: 0.8735 - val_loss: 0.4747 - learning_rate: 0.0010\n",
"Epoch 6/10\n",
"\u001b[1m613/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.9212 - loss: 0.2677\n",
"Epoch 6: val_loss improved from 0.40941 to 0.38850, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 45ms/step - accuracy: 0.9212 - loss: 0.2677 - val_accuracy: 0.8964 - val_loss: 0.3885 - learning_rate: 0.0010\n",
"Epoch 7/10\n",
"\u001b[1m612/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.9314 - loss: 0.2328\n",
"Epoch 7: val_loss did not improve from 0.38850\n",
"\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 44ms/step - accuracy: 0.9314 - loss: 0.2329 - val_accuracy: 0.8937 - val_loss: 0.4152 - learning_rate: 0.0010\n",
"Epoch 8/10\n",
"\u001b[1m613/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.9454 - loss: 0.1768\n",
"Epoch 8: val_loss did not improve from 0.38850\n",
"\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 44ms/step - accuracy: 0.9454 - loss: 0.1769 - val_accuracy: 0.8928 - val_loss: 0.4352 - learning_rate: 0.0010\n",
"Epoch 9/10\n",
"\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.9462 - loss: 0.1672\n",
"Epoch 9: val_loss did not improve from 0.38850\n",
"\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 44ms/step - accuracy: 0.9462 - loss: 0.1672 - val_accuracy: 0.8973 - val_loss: 0.4687 - learning_rate: 0.0010\n",
"Epoch 10/10\n",
"\u001b[1m613/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.9561 - loss: 0.1548\n",
"Epoch 10: val_loss improved from 0.38850 to 0.36028, saving model to best_model.h5\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m614/614\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 45ms/step - accuracy: 0.9560 - loss: 0.1548 - val_accuracy: 0.9129 - val_loss: 0.3603 - learning_rate: 0.0010\n",
"Restoring model weights from the end of the best epoch: 10.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Model Evalaution**"
],
"metadata": {
"id": "dtRetERUCCV3"
}
},
{
"cell_type": "code",
"source": [
"# Evaluate on test dataset\n",
"test_loss, test_accuracy = model.evaluate(test_ds, verbose=1)\n",
"print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
"\n",
"# Predict probabilities\n",
"y_pred_probs = model.predict(test_ds)\n",
"y_pred = np.argmax(y_pred_probs, axis=1)\n",
"\n",
"# True labels (same order as test_ds batching)\n",
"y_true = np.concatenate([y for x, y in test_ds], axis=0)\n",
"\n",
"# Metrics\n",
"precision = precision_score(y_true, y_pred, average='macro')\n",
"recall = recall_score(y_true, y_pred, average='macro')\n",
"f1 = f1_score(y_true, y_pred, average='macro')\n",
"\n",
"print(f\"Precision: {precision:.4f}, Recall: {recall:.4f}, F1-score: {f1:.4f}\")\n",
"\n",
"# Optional: detailed report per class\n",
"print(\"\\nClassification Report:\")\n",
"print(classification_report(y_true, y_pred, target_names=le.classes_))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Dr4yeNgJ-Mup",
"outputId": "30dcd301-0ff8-4b95-dd2e-9bcca092d958"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m76/76\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 264ms/step - accuracy: 0.8905 - loss: 0.4826\n",
"Test Accuracy: 0.8985\n",
"\u001b[1m76/76\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 115ms/step\n",
"Precision: 0.9006, Recall: 0.8987, F1-score: 0.8987\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
"american_football 0.96 0.94 0.95 80\n",
" baseball 0.92 0.89 0.91 81\n",
" basketball 0.96 0.91 0.94 81\n",
" billiard_ball 0.89 0.88 0.88 81\n",
" bowling_ball 0.87 0.93 0.90 81\n",
" cricket_ball 0.99 0.89 0.94 81\n",
" football 0.93 0.88 0.90 81\n",
" golf_ball 0.81 0.80 0.81 81\n",
" hockey_ball 0.84 0.78 0.81 81\n",
" hockey_puck 0.97 0.94 0.96 80\n",
" rugby_ball 0.87 0.88 0.87 81\n",
" shuttlecock 0.94 0.99 0.96 80\n",
"table_tennis_ball 0.80 0.85 0.83 81\n",
" tennis_ball 0.83 0.96 0.89 81\n",
" volleyball 0.92 0.98 0.95 81\n",
"\n",
" accuracy 0.90 1212\n",
" macro avg 0.90 0.90 0.90 1212\n",
" weighted avg 0.90 0.90 0.90 1212\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Evaluate model\n",
"test_loss, test_accuracy = model.evaluate(test_ds, verbose=1)\n",
"print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
"\n",
"# Predictions\n",
"y_probs = model.predict(test_ds) # shape: (num_samples, num_classes)\n",
"y_pred = np.argmax(y_probs, axis=1)\n",
"\n",
"# True labels (extract from test_ds)\n",
"y_true = np.concatenate([y for _, y in test_ds], axis=0)\n",
"\n",
"# Classification report\n",
"print(\"\\nClassification Report:\")\n",
"print(classification_report(y_true, y_pred, target_names=le.classes_))\n",
"\n",
"# Confusion matrix\n",
"cm = confusion_matrix(y_true, y_pred)\n",
"plt.figure(figsize=(10,8))\n",
"sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
" xticklabels=le.classes_, yticklabels=le.classes_)\n",
"plt.xlabel(\"Predicted\")\n",
"plt.ylabel(\"True\")\n",
"plt.title(\"Confusion Matrix\")\n",
"plt.show()\n",
"\n",
"# ROC curve (multi-class, one-vs-rest)\n",
"y_true_bin = label_binarize(y_true, classes=np.arange(len(le.classes_))) # binarized true labels\n",
"\n",
"plt.figure(figsize=(10,8))\n",
"for i in range(len(le.classes_)):\n",
" fpr, tpr, _ = roc_curve(y_true_bin[:, i], y_probs[:, i])\n",
" plt.plot(fpr, tpr, label=f\"{le.classes_[i]}\")\n",
"plt.plot([0,1],[0,1],'k--', label='Random')\n",
"plt.xlabel(\"False Positive Rate\")\n",
"plt.ylabel(\"True Positive Rate\")\n",
"plt.title(\"ROC Curves (One-vs-Rest)\")\n",
"plt.legend()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "SLEDlfpD-UdX",
"outputId": "5f99c318-c74e-4df7-854b-732af23d2a3f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m76/76\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 0.8905 - loss: 0.4826\n",
"Test Accuracy: 0.8985\n",
"\u001b[1m76/76\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
"american_football 0.96 0.94 0.95 80\n",
" baseball 0.92 0.89 0.91 81\n",
" basketball 0.96 0.91 0.94 81\n",
" billiard_ball 0.89 0.88 0.88 81\n",
" bowling_ball 0.87 0.93 0.90 81\n",
" cricket_ball 0.99 0.89 0.94 81\n",
" football 0.93 0.88 0.90 81\n",
" golf_ball 0.81 0.80 0.81 81\n",
" hockey_ball 0.84 0.78 0.81 81\n",
" hockey_puck 0.97 0.94 0.96 80\n",
" rugby_ball 0.87 0.88 0.87 81\n",
" shuttlecock 0.94 0.99 0.96 80\n",
"table_tennis_ball 0.80 0.85 0.83 81\n",
" tennis_ball 0.83 0.96 0.89 81\n",
" volleyball 0.92 0.98 0.95 81\n",
"\n",
" accuracy 0.90 1212\n",
" macro avg 0.90 0.90 0.90 1212\n",
" weighted avg 0.90 0.90 0.90 1212\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Predictions\n",
"y_probs = model.predict(test_ds)\n",
"y_pred = np.argmax(y_probs, axis=1)\n",
"\n",
"# True labels\n",
"y_true = np.concatenate([y for _, y in test_ds], axis=0)\n",
"\n",
"# Metrics per class\n",
"precision, recall, f1, support = precision_recall_fscore_support(\n",
" y_true, y_pred, average=None, labels=np.arange(len(le.classes_))\n",
")\n",
"\n",
"df_metrics = pd.DataFrame({\n",
" 'Class': le.classes_, # use actual class names instead of 0,1,2,3\n",
" 'Precision': precision,\n",
" 'Recall': recall,\n",
" 'F1-score': f1,\n",
" 'Support': support\n",
"})\n",
"\n",
"# Sort by F1-score ascending\n",
"df_metrics_sorted = df_metrics.sort_values(by='F1-score')\n",
"print(df_metrics_sorted)\n",
"\n",
"# Macro averages\n",
"precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support(\n",
" y_true, y_pred, average='macro'\n",
")\n",
"print(f\"\\nMacro Avg -> Precision: {precision_macro:.4f}, Recall: {recall_macro:.4f}, F1-score: {f1_macro:.4f}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "R3yXggJo-fcA",
"outputId": "4f0710ce-24a2-4041-eb31-f1928c54b613"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m76/76\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step\n",
" Class Precision Recall F1-score Support\n",
"7 golf_ball 0.812500 0.802469 0.807453 81\n",
"8 hockey_ball 0.840000 0.777778 0.807692 81\n",
"12 table_tennis_ball 0.802326 0.851852 0.826347 81\n",
"10 rugby_ball 0.865854 0.876543 0.871166 81\n",
"3 billiard_ball 0.887500 0.876543 0.881988 81\n",
"13 tennis_ball 0.829787 0.962963 0.891429 81\n",
"4 bowling_ball 0.872093 0.925926 0.898204 81\n",
"6 football 0.934211 0.876543 0.904459 81\n",
"1 baseball 0.923077 0.888889 0.905660 81\n",
"5 cricket_ball 0.986301 0.888889 0.935065 81\n",
"2 basketball 0.961039 0.913580 0.936709 81\n",
"14 volleyball 0.918605 0.975309 0.946108 81\n",
"0 american_football 0.961538 0.937500 0.949367 80\n",
"9 hockey_puck 0.974026 0.937500 0.955414 80\n",
"11 shuttlecock 0.940476 0.987500 0.963415 80\n",
"\n",
"Macro Avg -> Precision: 0.9006, Recall: 0.8987, F1-score: 0.8987\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Confusion matrix (no annotations, just intensity heatmap)\n",
"cm = confusion_matrix(y_true, y_pred)\n",
"\n",
"plt.figure(figsize=(15,12))\n",
"sns.heatmap(cm, annot=False, fmt='d', cmap='Blues',\n",
" xticklabels=le.classes_, yticklabels=le.classes_)\n",
"plt.xlabel(\"Predicted Class\")\n",
"plt.ylabel(\"True Class\")\n",
"plt.title(\"Confusion Matrix Heatmap\")\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "2JvGJkUz-gDw",
"outputId": "eb757aeb-f79e-4310-9721-e588e78f14f0"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Binarize true labels\n",
"y_test_bin = label_binarize(y_true, classes=np.arange(len(le.classes_)))\n",
"\n",
"# Predict class probabilities\n",
"y_probs = model.predict(test_ds)\n",
"\n",
"# Compute macro-average ROC\n",
"all_fpr = np.linspace(0, 1, 100)\n",
"mean_tpr = 0\n",
"\n",
"for i in range(len(le.classes_)):\n",
" fpr, tpr, _ = roc_curve(y_test_bin[:, i], y_probs[:, i])\n",
" mean_tpr += np.interp(all_fpr, fpr, tpr)\n",
"\n",
"mean_tpr /= len(le.classes_)\n",
"roc_auc = auc(all_fpr, mean_tpr)\n",
"\n",
"# Plot\n",
"plt.figure(figsize=(10,6))\n",
"plt.plot(all_fpr, mean_tpr, color='b',\n",
" label=f'Macro-average ROC (AUC = {roc_auc:.4f})')\n",
"plt.plot([0,1],[0,1],'k--', label='Random')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Macro-average ROC Curve')\n",
"plt.legend()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 581
},
"id": "o1z2ey52-iyv",
"outputId": "ad88b6e9-f93b-485c-d5e3-59fa5ad0faf5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m76/76\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 24ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Saving the Model**"
],
"metadata": {
"id": "iRXx_9clCHVq"
}
},
{
"cell_type": "code",
"source": [
"model.save(\"Sports_Balls_Classification.h5\")\n",
"files.download(\"Sports_Balls_Classification.h5\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"id": "Vd_l-Sxq-lVp",
"outputId": "71080e1c-5289-4e9e-9913-93a58a292158"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"application/javascript": [
"download(\"download_3d7d831b-30f9-4604-a91a-7b0c5a9faba2\", \"Sports_Balls_Classification.h5\", 142156800)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Extract Classes Vector for Deploying**\n",
"\n",
"> *I prefer doing this with multiple classes datasets*\n",
"\n"
],
"metadata": {
"id": "Y_SVYAzPCNHp"
}
},
{
"cell_type": "code",
"source": [
"###FOR LATER DEPLOYING\n",
"\n",
"# Fit LabelEncoder to the class labels\n",
"le = LabelEncoder()\n",
"le.fit(df['class'])\n",
"\n",
"# Get class names\n",
"class_names = le.classes_\n",
"\n",
"# Format with double quotes\n",
"formatted = ', '.join(f'\"{name}\"' for name in class_names)\n",
"print(f'CLASS_NAMES = [{formatted}]')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7gATisKOdLpB",
"outputId": "54f6e877-c858-4807-cde8-ceb81d12e961"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CLASS_NAMES = [\"american_football\", \"baseball\", \"basketball\", \"billiard_ball\", \"bowling_ball\", \"cricket_ball\", \"football\", \"golf_ball\", \"hockey_ball\", \"hockey_puck\", \"rugby_ball\", \"shuttlecock\", \"table_tennis_ball\", \"tennis_ball\", \"volleyball\"]\n"
]
}
]
}
]
}