load_humerus_fracture_detection_keras_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #-------------------------------------------------\n",
    "# #Utility function for displaying filters as images\n",
    "# #-------------------------------------------------\n",
    "\n",
    "# def deprocess_image(x):\n",
    "    \n",
    "#     x -= x.mean()\n",
    "#     x /= (x.std() + 1e-5)\n",
    "#     x *= 0.1\n",
    "#     x += 0.5\n",
    "#     x = np.clip(x, 0, 1)\n",
    "#     x *= 255\n",
    "#     x = np.clip(x, 0, 255).astype('uint8')\n",
    "#     return x\n",
    "\n",
    "# #---------------------------------------------------------------------------------------------------\n",
    "# #Utility function for generating patterns for given layer starting from empty input image and then \n",
    "# #applying Stochastic Gradient Ascent for maximizing the response of particular filter in given layer\n",
    "# #---------------------------------------------------------------------------------------------------\n",
    "\n",
    "# def generate_pattern(layer_name, filter_index, size=150):\n",
    "    \n",
    "#     layer_output = model.get_layer(layer_name).output\n",
    "#     loss = K.mean(layer_output[:, :, :, filter_index])\n",
    "#     grads = K.gradients(loss, model.input)[0]\n",
    "#     grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)\n",
    "#     iterate = K.function([model.input], [loss, grads])\n",
    "#     input_img_data = np.random.random((1, size, size, 3)) * 20 + 128.\n",
    "#     step = 1.\n",
    "#     for i in range(80):\n",
    "#         loss_value, grads_value = iterate([input_img_data])\n",
    "#         input_img_data += grads_value * step\n",
    "        \n",
    "#     img = input_img_data[0]\n",
    "#     return deprocess_image(img)\n",
    "\n",
    "\n",
    "def hand_scan(img_path):\n",
    "    \n",
    "    \n",
    "    \n",
    "#     model = model(weights=model.load_weights)\n",
    "    \n",
    "#     img=mpimg.imread(img_path)\n",
    "#     plt.imshow(img)\n",
    "#     from keras.preprocessing import image\n",
    "#     import numpy as np\n",
    "#     img = image.load_img(img_path, target_size=(299, 299))\n",
    "#     x = image.img_to_array(img)\n",
    "#     print(x.shape)\n",
    "#     x = np.expand_dims(x, axis=0)\n",
    "#     print(x.shape)\n",
    "#     from keras.applications.vgg16 import preprocess_input\n",
    "#     import pandas as pd\n",
    "#     x = preprocess_input(x, data_format='channels_last')\n",
    "#     preds = model.predict(x)\n",
    "#     decode_predictions = tf.keras.applications.imagenet_utils.decode_predictions(preds, top=3)\n",
    "#     print(decode_predictions)\n",
    "#     predictions = pd.DataFrame(decode_predictions[0],columns=['col1','category','probability']).iloc[:,1:]\n",
    "#     print(predictions)\n",
    "#     argmax = np.argmax(preds[0])\n",
    "#     output = model.output[:, argmax]\n",
    "#     last_conv_layer = model.get_layer('conv2d_93')\n",
    "#     grads = K.gradients(output, last_conv_layer.output)[0]\n",
    "#     pooled_grads = K.mean(grads, axis=(0, 1, 2))\n",
    "#     iterate = K.function([model.input], [pooled_grads, last_conv_layer.output[0]])\n",
    "#     pooled_grads_value, conv_layer_output_value = iterate([x])\n",
    "#     for i in range(32):\n",
    "#         conv_layer_output_value[:, :, i] *= pooled_grads_value[i]\n",
    "#     heatmap = np.mean(conv_layer_output_value, axis=-1)\n",
    "#     heatmap = np.maximum(heatmap, 0)\n",
    "#     heatmap /= np.max(heatmap)\n",
    "#     import cv2\n",
    "#     img = cv2.imread(img_path)\n",
    "#     heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n",
    "#     heatmap = np.uint8(255 * heatmap)\n",
    "#     heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n",
    "#     hif = .8\n",
    "#     superimposed_img = heatmap * hif + img\n",
    "#     output = '/Users/rvupp/opt/anaconda3/output.jpeg'\n",
    "#     cv2.imwrite(output, superimposed_img)\n",
    "#     img=mpimg.imread(output)\n",
    "#     plt.figure(figsize=(20,20))\n",
    "#     plt.imshow(img)\n",
    "#     plt.axis('off')\n",
    "#     plt.title(predictions.loc[0,'category'].upper())\n",
    "\n",
    "    import tensorflow as tf\n",
    "    from tensorflow.keras import models\n",
    "    tf.compat.v1.disable_eager_execution()\n",
    "    import matplotlib.image as mpimg\n",
    "    from tensorflow.keras import backend as K\n",
    "    import matplotlib.pyplot as plt\n",
    "    from tensorflow.keras.optimizers import Adam\n",
    "    from tensorflow.keras.layers import Input\n",
    "    \n",
    "    %matplotlib inline\n",
    "    K.clear_session()\n",
    "    \n",
    "    # load saved model from disk\n",
    "    model = tf.keras.models.load_model('/Users/rvupp/opt/anaconda3/inceptionv3_fracture_detection_hand_model_keras_4_150layers_trained.h5')\n",
    "    print(model.summary())\n",
    "    model.load_weights('/Users/rvupp/opt/anaconda3/inceptionv3_fracture_detection_hand_model_weights_4_150layers_trained.h5')\n",
    "    \n",
    "    test_image = img_path\n",
    "    \n",
    "    img=mpimg.imread(test_image)\n",
    "    plt.figure(figsize=(5,5))\n",
    "    plt.imshow(img)\n",
    "    from tensorflow.keras.preprocessing import image\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    img = image.load_img(test_image, target_size=(299, 299))\n",
    "    img_tensor = image.img_to_array(img)\n",
    "    img_tensor = np.expand_dims(img_tensor, axis=0)\n",
    "    print(img_tensor.shape)\n",
    "    print(type(img_tensor))\n",
    "    \n",
    "#     img_tensor = np.expand_dims(img_tensor, axis=0)\n",
    "#     \n",
    "    img_tensor = tf.convert_to_tensor(img_tensor)\n",
    "    from tensorflow.keras.applications.inception_v3 import preprocess_input\n",
    "    x = preprocess_input(img_tensor, data_format='channels_last')\n",
    "    print(type(x))\n",
    "    print(x.shape)\n",
    "    preds = model.predict(x, steps=1)\n",
    "    \n",
    "    decode_predictions = tf.keras.applications.imagenet_utils.decode_predictions(preds, top=3)\n",
    "    \n",
    "    print(decode_predictions)\n",
    "    \n",
    "    predictions = pd.DataFrame(decode_predictions[0],columns=['col1','category','probability']).iloc[:,1:]\n",
    "    print(predictions)\n",
    "#     print(img_tensor.shape)\n",
    "\n",
    "#     img = image.load_img(test_image, target_size=(IMG_SIZE[0], IMG_SIZE[1]))\n",
    "#     img_tensor = image.img_to_array(img)\n",
    "#     img_tensor = np.expand_dims(img_tensor, axis=0)\n",
    "    \n",
    "    # Determine proper input shape\n",
    "#     input_shape = (299,299,3)\n",
    "\n",
    "#     if img_tensor is None:\n",
    "#         img_tensor = tf.keras.layers.Input(shape=input_shape)\n",
    "#     else:\n",
    "#         img_tensor = tf.keras.layers.Input(tensor=img_tensor, shape=input_shape)\n",
    "\n",
    "# #     img_tensor /= 255.\n",
    "    \n",
    "#     print(img_tensor.shape)\n",
    "#     print(type(img_tensor))\n",
    "    \n",
    "    \n",
    "    \n",
    "#     # load saved model from disk\n",
    "#     model = tf.keras.models.load_model('/Users/rvupp/opt/anaconda3/inceptionv3_fracture_detection_hand_model_keras.h5')\n",
    "#     print(model.summary())\n",
    "#     # load weights from disk into new model\n",
    "#     model.load_weights('/Users/rvupp/opt/anaconda3/inceptionv3_fracture_detection_hand_model_weights.h5')\n",
    "    \n",
    "    # evaluate loaded model on test data\n",
    "#     learning_rate =  0.0001\n",
    "#     opt = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
    "#     model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])\n",
    "    \n",
    "#     print(str(model.input_shape) + \" Input Shape\")\n",
    "    \n",
    "        #---------------------------------------------------------------------------------------\n",
    "#Getting outputs for intermediate convolution layers by running prediction on test image\n",
    "#---------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "    \n",
    "    layer_outputs = [layer.output for layer in model.layers[:50]]\n",
    "    print(layer_outputs)\n",
    "    \n",
    "    activation_model = models.Model(inputs=model.inputs, outputs=layer_outputs)\n",
    "#     score = model.evaluate(img_tensor, Y, verbose=0)\n",
    "#     print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))\n",
    "    \n",
    "\n",
    "\n",
    "#     activation_model = models.Model(inputs=model.inputs, outputs=layer_outputs)\n",
    "    activations = activation_model.predict(img_tensor, steps = 1)\n",
    "\n",
    "    layer_names = ['conv2d_1', 'activation_1', 'conv2d_4', 'activation_4', 'conv2d_9', 'activation_9']\n",
    "    activ_list = [activations[1], activations[3], activations[11], activations[13], activations[18], activations[20]]\n",
    "\n",
    "    images_per_row = 16\n",
    "\n",
    "    for layer_name, layer_activation in zip(layer_names, activ_list):\n",
    "        n_features = layer_activation.shape[-1]\n",
    "        size = layer_activation.shape[1]\n",
    "        n_cols = n_features // images_per_row\n",
    "        display_grid = np.zeros((size * n_cols, images_per_row * size))\n",
    "    \n",
    "        for col in range(n_cols):\n",
    "            for row in range(images_per_row):\n",
    "                channel_image = layer_activation[0, :, :, col * images_per_row + row]\n",
    "                channel_image -= channel_image.mean()\n",
    "                channel_image /= channel_image.std()\n",
    "                channel_image *= 64\n",
    "                channel_image += 128\n",
    "                channel_image = np.clip(channel_image, 0, 255).astype('uint8')\n",
    "                display_grid[col * size : (col + 1) * size, row * size : (row + 1) * size] = channel_image\n",
    "\n",
    "        scale = 1. / size\n",
    "        plt.figure(figsize=(2*scale * display_grid.shape[1], 2*scale * display_grid.shape[0]))\n",
    "        plt.title(layer_name)\n",
    "        plt.grid(False)\n",
    "        plt.imshow(display_grid, aspect='auto', cmap='plasma')\n",
    "        plt.savefig(layer_name+\"_grid.jpg\", bbox_inches='tight')\n",
    "\n",
    "\n",
    "#-------------------------------------------------\n",
    "#Utility function for displaying filters as images\n",
    "#-------------------------------------------------\n",
    "\n",
    "    def deprocess_image(x):\n",
    "    \n",
    "        x -= x.mean()\n",
    "        x /= (x.std() + 1e-5)\n",
    "        x *= 0.1\n",
    "        x += 0.5\n",
    "        x = np.clip(x, 0, 1)\n",
    "        x *= 255\n",
    "        x = np.clip(x, 0, 255).astype('uint8')\n",
    "        return x\n",
    "\n",
    "#---------------------------------------------------------------------------------------------------\n",
    "#Utility function for generating patterns for given layer starting from empty input image and then \n",
    "#applying Stochastic Gradient Ascent for maximizing the response of particular filter in given layer\n",
    "#---------------------------------------------------------------------------------------------------\n",
    "\n",
    "    def generate_pattern(layer_name, filter_index, size=150):\n",
    "    \n",
    "        layer_output = model.get_layer(layer_name).output\n",
    "        loss = K.mean(layer_output[:, :, :, filter_index])\n",
    "        grads = K.gradients(loss, model.input)[0]\n",
    "        grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)\n",
    "        iterate = K.function([model.input], [loss, grads])\n",
    "        input_img_data = np.random.random((1, size, size, 3)) * 20 + 128.\n",
    "        step = 1.\n",
    "        for i in range(80):\n",
    "            loss_value, grads_value = iterate([input_img_data])\n",
    "            input_img_data += grads_value * step\n",
    "        \n",
    "        img = input_img_data[0]\n",
    "        return deprocess_image(img)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "#Generating convolution layer filters for intermediate layers using above utility functions\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "    layer_name = 'conv2d_4'\n",
    "    size = 299\n",
    "    margin = 5\n",
    "    results = np.zeros((8 * size + 7 * margin, 8 * size + 7 * margin, 3))\n",
    "\n",
    "    for i in range(8):\n",
    "        for j in range(8):\n",
    "            filter_img = generate_pattern(layer_name, i + (j * 8), size=size)\n",
    "            horizontal_start = i * size + i * margin\n",
    "            horizontal_end = horizontal_start + size\n",
    "            vertical_start = j * size + j * margin\n",
    "            vertical_end = vertical_start + size\n",
    "            results[horizontal_start: horizontal_end, vertical_start: vertical_end, :] = filter_img\n",
    "        \n",
    "    plt.figure(figsize=(20, 20))\n",
    "    plt.savefig(results)\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------\n",
    "#Here, I am initializing an InceptionV3 model and making prediction on a test image.\n",
    "#Following which, I am creating a activaton heatmap of the last layer of this model,\n",
    "#which is a mixed layer. This heatmap is then superimposed on the original image.\n",
    "#-----------------------------------------------------------------------------------\n",
    "\n",
    "    model = InceptionV3(weights='imagenet')\n",
    "#     img_path = 'any test image'\n",
    "\n",
    "#     img = image.load_img(img_path, target_size=(IMG_SIZE[0], IMG_SIZE[1]))\n",
    "#     x = image.img_to_array(img)\n",
    "#     x = np.expand_dims(x, axis=0)\n",
    "#     x = preprocess_input(x)\n",
    "    \n",
    "    test_image = img_path\n",
    "    \n",
    "    img=mpimg.imread(test_image)\n",
    "    plt.imshow(img)\n",
    "    from keras.preprocessing import image\n",
    "    import numpy as np\n",
    "    img = image.load_img(img_path, target_size=(299, 299))\n",
    "    img_tensor = image.img_to_array(img)\n",
    "    print(img_tensor.shape)\n",
    "#     img_tensor = np.expand_dims(img_tensor, axis=0)\n",
    "    print(img_tensor.shape)\n",
    "    img_tensor = tf.convert_to_tensor(img_tensor)\n",
    "\n",
    "    preds = model.predict(img_tensor)\n",
    "    print (\"Predicted: \", decode_predictions(preds, top=3)[0])\n",
    "\n",
    "#985 is the class index for class 'Daisy' in Imagenet dataset on which my model is pre-trained\n",
    "    flower_output = model.output[:, 985]\n",
    "    last_conv_layer = model.get_layer('mixed10')\n",
    "\n",
    "    grads = K.gradients(flower_output, last_conv_layer.output)[0]\n",
    "    pooled_grads = K.mean(grads, axis=(0, 1, 2))\n",
    "    iterate = K.function([model.input], [pooled_grads, last_conv_layer.output[0]])\n",
    "    pooled_grads_value, conv_layer_output_value = iterate([x])\n",
    "\n",
    "#2048 is the number of filters/channels in 'mixed10' layer\n",
    "    for i in range(2048):\n",
    "        conv_layer_output_value[:, :, i] *= pooled_grads_value[i]\n",
    "\n",
    "    heatmap = np.mean(conv_layer_output_value, axis=-1)\n",
    "    heatmap = np.maximum(heatmap, 0)\n",
    "    heatmap /= np.max(heatmap)\n",
    "    plt.savefig(heatmap)\n",
    "    plt.imshow(heatmap)\n",
    "\n",
    "#Using cv2 to superimpose the heatmap on original image to clearly illustrate activated portion of image\n",
    "    img = cv2.imread(img_path)\n",
    "    heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n",
    "    heatmap = np.uint8(255 * heatmap)\n",
    "    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n",
    "    superimposed_img = heatmap * 0.4 + img\n",
    "    cv2.imwrite('image_name.jpg', superimposed_img)\n",
    "    plt.imshow(superimposed_img)\n",
    "    \n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/rvupp/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1635: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 149, 149, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 149, 149, 32) 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 149, 149, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 147, 147, 32) 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 147, 147, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 147, 147, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 147, 147, 64) 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 147, 147, 64) 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 147, 147, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 73, 73, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 73, 73, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 73, 73, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 73, 73, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 71, 71, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 71, 71, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 71, 71, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 35, 35, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 35, 35, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 35, 35, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 35, 35, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 35, 35, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 35, 35, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 35, 35, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 35, 35, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 35, 35, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 35, 35, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 35, 35, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 35, 35, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 35, 35, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 35, 35, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 35, 35, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 35, 35, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 35, 35, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 35, 35, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 35, 35, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 35, 35, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 35, 35, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 35, 35, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 35, 35, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 35, 35, 256)  0           activation_5[0][0]               \n",
      "                                                                 activation_7[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 35, 35, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 35, 35, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 35, 35, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 35, 35, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 35, 35, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 35, 35, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 35, 35, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 35, 35, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 35, 35, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 35, 35, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 35, 35, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 35, 35, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 35, 35, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 35, 35, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 35, 35, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 35, 35, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 35, 35, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 35, 35, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 35, 35, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 35, 35, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 35, 35, 288)  0           activation_12[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 35, 35, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 35, 35, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 35, 35, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 35, 35, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 35, 35, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 35, 35, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 35, 35, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 35, 35, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 35, 35, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 35, 35, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 35, 35, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 35, 35, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 35, 35, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 35, 35, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 35, 35, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 35, 35, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 35, 35, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 35, 35, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 35, 35, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 35, 35, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 35, 35, 288)  0           activation_19[0][0]              \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 35, 35, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 35, 35, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 35, 35, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 35, 35, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 35, 35, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 35, 35, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 17, 17, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 17, 17, 96)   82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 17, 17, 384)  1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 17, 17, 96)   288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 17, 17, 384)  0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 17, 17, 96)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 17, 17, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 17, 17, 768)  0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 17, 17, 128)  384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 17, 17, 128)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 17, 17, 128)  114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 17, 17, 128)  384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 17, 17, 128)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 17, 17, 128)  114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 17, 17, 128)  384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 17, 17, 128)  384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 17, 17, 128)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 17, 17, 128)  0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 17, 17, 128)  114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 17, 17, 128)  114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 17, 17, 128)  384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 17, 17, 128)  384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 17, 17, 128)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 17, 17, 128)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 17, 17, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 17, 17, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 17, 17, 192)  172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 17, 17, 192)  172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 17, 17, 192)  576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 17, 17, 192)  576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 17, 17, 192)  576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 17, 17, 192)  576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 17, 17, 192)  0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 17, 17, 192)  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 17, 17, 192)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 17, 17, 192)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 17, 17, 768)  0           activation_30[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 17, 17, 160)  480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 17, 17, 160)  0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 17, 17, 160)  179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 17, 17, 160)  480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 17, 17, 160)  0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 17, 17, 160)  179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 17, 17, 160)  480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 17, 17, 160)  480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 17, 17, 160)  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 17, 17, 160)  0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 17, 17, 160)  179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 17, 17, 160)  179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 17, 17, 160)  480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 17, 17, 160)  480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 17, 17, 160)  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 17, 17, 160)  0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 17, 17, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 17, 17, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 17, 17, 192)  215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 17, 17, 192)  215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 17, 17, 192)  576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 17, 17, 192)  576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 17, 17, 192)  576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 17, 17, 192)  576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 17, 17, 192)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 17, 17, 192)  0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 17, 17, 192)  0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 17, 17, 192)  0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 17, 17, 768)  0           activation_40[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "                                                                 activation_48[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 17, 17, 160)  480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 17, 17, 160)  0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 17, 17, 160)  179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 17, 17, 160)  480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 17, 17, 160)  0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 17, 17, 160)  179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 17, 17, 160)  480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 17, 17, 160)  480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 17, 17, 160)  0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 17, 17, 160)  0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 17, 17, 160)  179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 17, 17, 160)  179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 17, 17, 160)  480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 17, 17, 160)  480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 17, 17, 160)  0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 17, 17, 160)  0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 17, 17, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 17, 17, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 17, 17, 192)  215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 17, 17, 192)  215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 17, 17, 192)  576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 17, 17, 192)  576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 17, 17, 192)  576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 17, 17, 192)  576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 17, 17, 192)  0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 17, 17, 192)  0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 17, 17, 192)  0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 17, 17, 192)  0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 17, 17, 768)  0           activation_50[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "                                                                 activation_58[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 17, 17, 192)  576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 17, 17, 192)  0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 17, 17, 192)  258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 17, 17, 192)  576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 17, 17, 192)  0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 17, 17, 192)  258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 17, 17, 192)  576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 17, 17, 192)  576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 17, 17, 192)  0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 17, 17, 192)  0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 17, 17, 192)  258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 17, 17, 192)  258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 17, 17, 192)  576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 17, 17, 192)  576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 17, 17, 192)  0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 17, 17, 192)  0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 17, 17, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 17, 17, 192)  258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 17, 17, 192)  258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 17, 17, 192)  576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 17, 17, 192)  576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 17, 17, 192)  576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 17, 17, 192)  576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 17, 17, 192)  0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 17, 17, 192)  0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 17, 17, 192)  0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 17, 17, 192)  0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 17, 17, 768)  0           activation_60[0][0]              \n",
      "                                                                 activation_63[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 17, 17, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 17, 17, 192)  576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 17, 17, 192)  0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 17, 17, 192)  258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 17, 17, 192)  576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 17, 17, 192)  0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 17, 17, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 17, 17, 192)  258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 17, 17, 192)  576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 17, 17, 192)  576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 17, 17, 192)  0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 17, 17, 192)  0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 8, 8, 320)    552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 8, 8, 192)    331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 8, 8, 320)    960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 8, 8, 192)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 8, 8, 320)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 8, 8, 192)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 8, 8, 1280)   0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 8, 8, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 8, 8, 448)    1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 8, 8, 448)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 8, 8, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 8, 8, 384)    1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 8, 8, 384)    1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 8, 8, 384)    1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 8, 8, 384)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 8, 8, 384)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 8, 8, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 8, 8, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 8, 8, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 8, 8, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 8, 8, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 8, 8, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 8, 8, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 8, 8, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 8, 8, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 8, 8, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 8, 8, 192)    245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 8, 8, 320)    960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 8, 8, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 8, 8, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 8, 8, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 8, 8, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 8, 8, 192)    576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 8, 8, 320)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 8, 8, 768)    0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 8, 8, 768)    0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 8, 8, 192)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 8, 8, 2048)   0           activation_76[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 8, 8, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 8, 8, 448)    1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 8, 8, 448)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 8, 8, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 8, 8, 384)    1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 8, 8, 384)    1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 8, 8, 384)    1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 8, 8, 384)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 8, 8, 384)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 8, 8, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 8, 8, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 8, 8, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 8, 8, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 8, 8, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 8, 8, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 8, 8, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 8, 8, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 8, 8, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 8, 8, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 8, 8, 192)    393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 8, 8, 320)    960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 8, 8, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 8, 8, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 8, 8, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 8, 8, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 8, 8, 192)    576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 8, 8, 320)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 8, 8, 768)    0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 8, 8, 768)    0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 8, 8, 192)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 8, 8, 2048)   0           activation_85[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 2048)         0           global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1024)         2098176     flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 8)            8200        dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Dense)             (None, 2)            18          dense_3[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 23,909,178\n",
      "Trainable params: 18,624,730\n",
      "Non-trainable params: 5,284,448\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "(1, 299, 299, 3)\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'tensorflow.python.framework.ops.Tensor'>\n",
      "(1, 299, 299, 3)\n",
      "[[('n01440764', 'tench', 0.7689306), ('n01443537', 'goldfish', 0.24438252)]]\n",
      "   category  probability\n",
      "0     tench     0.768931\n",
      "1  goldfish     0.244383\n",
      "[<tf.Tensor 'input_1:0' shape=(None, 299, 299, 3) dtype=float32>, <tf.Tensor 'conv2d/Conv2D:0' shape=(None, 149, 149, 32) dtype=float32>, <tf.Tensor 'batch_normalization/cond/Identity:0' shape=(None, 149, 149, 32) dtype=float32>, <tf.Tensor 'activation/Relu:0' shape=(None, 149, 149, 32) dtype=float32>, <tf.Tensor 'conv2d_1/Conv2D:0' shape=(None, 147, 147, 32) dtype=float32>, <tf.Tensor 'batch_normalization_1/cond/Identity:0' shape=(None, 147, 147, 32) dtype=float32>, <tf.Tensor 'activation_1/Relu:0' shape=(None, 147, 147, 32) dtype=float32>, <tf.Tensor 'conv2d_2/Conv2D:0' shape=(None, 147, 147, 64) dtype=float32>, <tf.Tensor 'batch_normalization_2/cond/Identity:0' shape=(None, 147, 147, 64) dtype=float32>, <tf.Tensor 'activation_2/Relu:0' shape=(None, 147, 147, 64) dtype=float32>, <tf.Tensor 'max_pooling2d/MaxPool:0' shape=(None, 73, 73, 64) dtype=float32>, <tf.Tensor 'conv2d_3/Conv2D:0' shape=(None, 73, 73, 80) dtype=float32>, <tf.Tensor 'batch_normalization_3/cond/Identity:0' shape=(None, 73, 73, 80) dtype=float32>, <tf.Tensor 'activation_3/Relu:0' shape=(None, 73, 73, 80) dtype=float32>, <tf.Tensor 'conv2d_4/Conv2D:0' shape=(None, 71, 71, 192) dtype=float32>, <tf.Tensor 'batch_normalization_4/cond/Identity:0' shape=(None, 71, 71, 192) dtype=float32>, <tf.Tensor 'activation_4/Relu:0' shape=(None, 71, 71, 192) dtype=float32>, <tf.Tensor 'max_pooling2d_1/MaxPool:0' shape=(None, 35, 35, 192) dtype=float32>, <tf.Tensor 'conv2d_8/Conv2D:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'batch_normalization_8/cond/Identity:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'activation_8/Relu:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'conv2d_6/Conv2D:0' shape=(None, 35, 35, 48) dtype=float32>, <tf.Tensor 'conv2d_9/Conv2D:0' shape=(None, 35, 35, 96) dtype=float32>, <tf.Tensor 'batch_normalization_6/cond/Identity:0' shape=(None, 35, 35, 48) dtype=float32>, <tf.Tensor 'batch_normalization_9/cond/Identity:0' shape=(None, 35, 35, 96) dtype=float32>, <tf.Tensor 'activation_6/Relu:0' shape=(None, 35, 35, 48) dtype=float32>, <tf.Tensor 'activation_9/Relu:0' shape=(None, 35, 35, 96) dtype=float32>, <tf.Tensor 'average_pooling2d/AvgPool:0' shape=(None, 35, 35, 192) dtype=float32>, <tf.Tensor 'conv2d_5/Conv2D:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'conv2d_7/Conv2D:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'conv2d_10/Conv2D:0' shape=(None, 35, 35, 96) dtype=float32>, <tf.Tensor 'conv2d_11/Conv2D:0' shape=(None, 35, 35, 32) dtype=float32>, <tf.Tensor 'batch_normalization_5/cond/Identity:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'batch_normalization_7/cond/Identity:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'batch_normalization_10/cond/Identity:0' shape=(None, 35, 35, 96) dtype=float32>, <tf.Tensor 'batch_normalization_11/cond/Identity:0' shape=(None, 35, 35, 32) dtype=float32>, <tf.Tensor 'activation_5/Relu:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'activation_7/Relu:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'activation_10/Relu:0' shape=(None, 35, 35, 96) dtype=float32>, <tf.Tensor 'activation_11/Relu:0' shape=(None, 35, 35, 32) dtype=float32>, <tf.Tensor 'mixed0/concat:0' shape=(None, 35, 35, 256) dtype=float32>, <tf.Tensor 'conv2d_15/Conv2D:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'batch_normalization_15/cond/Identity:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'activation_15/Relu:0' shape=(None, 35, 35, 64) dtype=float32>, <tf.Tensor 'conv2d_13/Conv2D:0' shape=(None, 35, 35, 48) dtype=float32>, <tf.Tensor 'conv2d_16/Conv2D:0' shape=(None, 35, 35, 96) dtype=float32>, <tf.Tensor 'batch_normalization_13/cond/Identity:0' shape=(None, 35, 35, 48) dtype=float32>, <tf.Tensor 'batch_normalization_16/cond/Identity:0' shape=(None, 35, 35, 96) dtype=float32>, <tf.Tensor 'activation_13/Relu:0' shape=(None, 35, 35, 48) dtype=float32>, <tf.Tensor 'activation_16/Relu:0' shape=(None, 35, 35, 96) dtype=float32>]\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": "input_1:0 is both fed and fetched.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-204082bb0eb4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mimage_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/Users/rvupp/opt/anaconda3/BoneFractureDetectionMURA/MURA-v1.1/valid/XR_HAND/patient11247/study1_positive/image4.png'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhand_scan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-1-43a55985b245>\u001b[0m in \u001b[0;36mhand_scan\u001b[0;34m(img_path)\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[0;31m#     activation_model = models.Model(inputs=model.inputs, outputs=layer_outputs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m     \u001b[0mactivations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mactivation_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0mlayer_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'conv2d_1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'activation_1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'conv2d_4'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'activation_4'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'conv2d_9'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'activation_9'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1011\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m   1014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mreset_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, model, x, batch_size, verbose, steps, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m         \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m         callbacks=callbacks)\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m           \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m             \u001b[0mactual_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m           \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0mis_dataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3562\u001b[0m         \u001b[0mfeed_symbols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_feed_symbols\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetches\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m \u001b[0;32mor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3563\u001b[0m         session != self._session):\n\u001b[0;32m-> 3564\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_arrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_symbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbol_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3566\u001b[0m     fetched = self._callable_fn(*array_vals,\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m_make_callable\u001b[0;34m(self, feed_arrays, feed_symbols, symbol_vals, session)\u001b[0m\n\u001b[1;32m   3499\u001b[0m       \u001b[0mcallable_opts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_options\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCopyFrom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3500\u001b[0m     \u001b[0;31m# Create callable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3501\u001b[0;31m     \u001b[0mcallable_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_callable_from_options\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallable_opts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3502\u001b[0m     \u001b[0;31m# Cache parameters corresponding to the generated callable, so that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3503\u001b[0m     \u001b[0;31m# we can detect future mismatches and refresh the callable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_make_callable_from_options\u001b[0;34m(self, callable_options)\u001b[0m\n\u001b[1;32m   1505\u001b[0m     \"\"\"\n\u001b[1;32m   1506\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1507\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mBaseSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_Callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallable_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, session, callable_options)\u001b[0m\n\u001b[1;32m   1460\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1461\u001b[0m         self._handle = tf_session.TF_SessionMakeCallable(\n\u001b[0;32m-> 1462\u001b[0;31m             session._session, options_ptr)\n\u001b[0m\u001b[1;32m   1463\u001b[0m       \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1464\u001b[0m         \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_DeleteBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: input_1:0 is both fed and fetched."
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = '/Users/rvupp/opt/anaconda3/BoneFractureDetectionMURA/MURA-v1.1/valid/XR_HAND/patient11247/study1_positive/image4.png'\n",
    "hand_scan(image_path)"
   ]
  },
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