text: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  # pylint: disable=invalid-name
  """Inception V3 model for Keras.
  
  Reference:
    - [Rethinking the Inception Architecture for Computer Vision](
        http://arxiv.org/abs/1512.00567) (CVPR 2016)
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.applications import imagenet_utils
  from tensorflow.python.keras.engine import training
  from tensorflow.python.keras.layers import VersionAwareLayers
  from tensorflow.python.keras.utils import data_utils
  from tensorflow.python.keras.utils import layer_utils
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.util.tf_export import keras_export
  
  
  WEIGHTS_PATH = (
      'https://storage.googleapis.com/tensorflow/keras-applications/'
      'inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5')
  WEIGHTS_PATH_NO_TOP = (
      'https://storage.googleapis.com/tensorflow/keras-applications/'
      'inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
  
  layers = VersionAwareLayers()
  
  
  @keras_export('keras.applications.inception_v3.InceptionV3',
                'keras.applications.InceptionV3')
  def InceptionV3(
      include_top=True,
      weights='imagenet',
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation='softmax'):
    """Instantiates the Inception v3 architecture.
  
    Reference:
    - [Rethinking the Inception Architecture for Computer Vision](
        http://arxiv.org/abs/1512.00567) (CVPR 2016)
  
    This function returns a Keras image classification model,
    optionally loaded with weights pre-trained on ImageNet.
  
    For image classification use cases, see
    [this page for detailed examples](
      https://keras.io/api/applications/#usage-examples-for-image-classification-models).
  
    For transfer learning use cases, make sure to read the
    [guide to transfer learning & fine-tuning](
      https://keras.io/guides/transfer_learning/).
  
    Note: each Keras Application expects a specific kind of input preprocessing.
    For `InceptionV3`, call `tf.keras.applications.inception_v3.preprocess_input`
    on your inputs before passing them to the model.
    `inception_v3.preprocess_input` will scale input pixels between -1 and 1.
  
    Args:
      include_top: Boolean, whether to include the fully-connected
        layer at the top, as the last layer of the network. Default to `True`.
      weights: One of `None` (random initialization),
        `imagenet` (pre-training on ImageNet),
        or the path to the weights file to be loaded. Default to `imagenet`.
      input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
        to use as image input for the model. `input_tensor` is useful for sharing
        inputs between multiple different networks. Default to None.
      input_shape: Optional shape tuple, only to be specified
        if `include_top` is False (otherwise the input shape
        has to be `(299, 299, 3)` (with `channels_last` data format)
        or `(3, 299, 299)` (with `channels_first` data format).
        It should have exactly 3 inputs channels,
        and width and height should be no smaller than 75.
        E.g. `(150, 150, 3)` would be one valid value.
        `input_shape` will be ignored if the `input_tensor` is provided.
      pooling: Optional pooling mode for feature extraction
        when `include_top` is `False`.
        - `None` (default) means that the output of the model will be
            the 4D tensor output of the last convolutional block.
        - `avg` means that global average pooling
            will be applied to the output of the
            last convolutional block, and thus
            the output of the model will be a 2D tensor.
        - `max` means that global max pooling will be applied.
      classes: optional number of classes to classify images
        into, only to be specified if `include_top` is True, and
        if no `weights` argument is specified. Default to 1000.
      classifier_activation: A `str` or callable. The activation function to use
        on the "top" layer. Ignored unless `include_top=True`. Set
        `classifier_activation=None` to return the logits of the "top" layer.
        When loading pretrained weights, `classifier_activation` can only
        be `None` or `"softmax"`.
  
    Returns:
      A `keras.Model` instance.
    """
    if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
      raise ValueError('The `weights` argument should be either '
                       '`None` (random initialization), `imagenet` '
                       '(pre-training on ImageNet), '
                       'or the path to the weights file to be loaded.')
  
    if weights == 'imagenet' and include_top and classes != 1000:
      raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
                       ' as true, `classes` should be 1000')
  
    # Determine proper input shape
    input_shape = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=299,
        min_size=75,
        data_format=backend.image_data_format(),
        require_flatten=include_top,
        weights=weights)
  
    if input_tensor is None:
      img_input = layers.Input(shape=input_shape)
    else:
      if not backend.is_keras_tensor(input_tensor):
        img_input = layers.Input(tensor=input_tensor, shape=input_shape)
      else:
        img_input = input_tensor
  
    if backend.image_data_format() == 'channels_first':
      channel_axis = 1
    else:
      channel_axis = 3
  
    x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid')
    x = conv2d_bn(x, 32, 3, 3, padding='valid')
    x = conv2d_bn(x, 64, 3, 3)
    x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
  
    x = conv2d_bn(x, 80, 1, 1, padding='valid')
    x = conv2d_bn(x, 192, 3, 3, padding='valid')
    x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
  
    # mixed 0: 35 x 35 x 256
    branch1x1 = conv2d_bn(x, 64, 1, 1)
  
    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
    branch_pool = layers.AveragePooling2D(
        (3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
    x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],
                           axis=channel_axis,
                           name='mixed0')
  
    # mixed 1: 35 x 35 x 288
    branch1x1 = conv2d_bn(x, 64, 1, 1)
  
    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
    branch_pool = layers.AveragePooling2D(
        (3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
    x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],
                           axis=channel_axis,
                           name='mixed1')
  
    # mixed 2: 35 x 35 x 288
    branch1x1 = conv2d_bn(x, 64, 1, 1)
  
    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
    branch_pool = layers.AveragePooling2D(
        (3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
    x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],
                           axis=channel_axis,
                           name='mixed2')
  
    # mixed 3: 17 x 17 x 768
    branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')
  
    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(
        branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid')
  
    branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
    x = layers.concatenate([branch3x3, branch3x3dbl, branch_pool],
                           axis=channel_axis,
                           name='mixed3')
  
    # mixed 4: 17 x 17 x 768
    branch1x1 = conv2d_bn(x, 192, 1, 1)
  
    branch7x7 = conv2d_bn(x, 128, 1, 1)
    branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
    branch7x7dbl = conv2d_bn(x, 128, 1, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
    branch_pool = layers.AveragePooling2D(
        (3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],
                           axis=channel_axis,
                           name='mixed4')
  
    # mixed 5, 6: 17 x 17 x 768
    for i in range(2):
      branch1x1 = conv2d_bn(x, 192, 1, 1)
  
      branch7x7 = conv2d_bn(x, 160, 1, 1)
      branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
      branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
      branch7x7dbl = conv2d_bn(x, 160, 1, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
      branch_pool = layers.AveragePooling2D((3, 3),
                                            strides=(1, 1),
                                            padding='same')(
                                                x)
      branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
      x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],
                             axis=channel_axis,
                             name='mixed' + str(5 + i))
  
    # mixed 7: 17 x 17 x 768
    branch1x1 = conv2d_bn(x, 192, 1, 1)
  
    branch7x7 = conv2d_bn(x, 192, 1, 1)
    branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
    branch7x7dbl = conv2d_bn(x, 192, 1, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
    branch_pool = layers.AveragePooling2D(
        (3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],
                           axis=channel_axis,
                           name='mixed7')
  
    # mixed 8: 8 x 8 x 1280
    branch3x3 = conv2d_bn(x, 192, 1, 1)
    branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding='valid')
  
    branch7x7x3 = conv2d_bn(x, 192, 1, 1)
    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
    branch7x7x3 = conv2d_bn(
        branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')
  
    branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
    x = layers.concatenate([branch3x3, branch7x7x3, branch_pool],
                           axis=channel_axis,
                           name='mixed8')
  
    # mixed 9: 8 x 8 x 2048
    for i in range(2):
      branch1x1 = conv2d_bn(x, 320, 1, 1)
  
      branch3x3 = conv2d_bn(x, 384, 1, 1)
      branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
      branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
      branch3x3 = layers.concatenate([branch3x3_1, branch3x3_2],
                                     axis=channel_axis,
                                     name='mixed9_' + str(i))
  
      branch3x3dbl = conv2d_bn(x, 448, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
      branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
      branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
      branch3x3dbl = layers.concatenate([branch3x3dbl_1, branch3x3dbl_2],
                                        axis=channel_axis)
  
      branch_pool = layers.AveragePooling2D((3, 3),
                                            strides=(1, 1),
                                            padding='same')(
                                                x)
      branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
      x = layers.concatenate([branch1x1, branch3x3, branch3x3dbl, branch_pool],
                             axis=channel_axis,
                             name='mixed' + str(9 + i))
    if include_top:
      # Classification block
      x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
      imagenet_utils.validate_activation(classifier_activation, weights)
      x = layers.Dense(classes, activation=classifier_activation,
                       name='predictions')(x)
    else:
      if pooling == 'avg':
        x = layers.GlobalAveragePooling2D()(x)
      elif pooling == 'max':
        x = layers.GlobalMaxPooling2D()(x)
  
    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
      inputs = layer_utils.get_source_inputs(input_tensor)
    else:
      inputs = img_input
    # Create model.
    model = training.Model(inputs, x, name='inception_v3')
  
    # Load weights.
    if weights == 'imagenet':
      if include_top:
        weights_path = data_utils.get_file(
            'inception_v3_weights_tf_dim_ordering_tf_kernels.h5',
            WEIGHTS_PATH,
            cache_subdir='models',
            file_hash='9a0d58056eeedaa3f26cb7ebd46da564')
      else:
        weights_path = data_utils.get_file(
            'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
            WEIGHTS_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='bcbd6486424b2319ff4ef7d526e38f63')
      model.load_weights(weights_path)
    elif weights is not None:
      model.load_weights(weights)
  
    return model
  
  
  def conv2d_bn(x,
                filters,
                num_row,
                num_col,
                padding='same',
                strides=(1, 1),
                name=None):
    """Utility function to apply conv + BN.
  
    Args:
      x: input tensor.
      filters: filters in `Conv2D`.
      num_row: height of the convolution kernel.
      num_col: width of the convolution kernel.
      padding: padding mode in `Conv2D`.
      strides: strides in `Conv2D`.
      name: name of the ops; will become `name + '_conv'`
        for the convolution and `name + '_bn'` for the
        batch norm layer.
  
    Returns:
      Output tensor after applying `Conv2D` and `BatchNormalization`.
    """
    if name is not None:
      bn_name = name + '_bn'
      conv_name = name + '_conv'
    else:
      bn_name = None
      conv_name = None
    if backend.image_data_format() == 'channels_first':
      bn_axis = 1
    else:
      bn_axis = 3
    x = layers.Conv2D(
        filters, (num_row, num_col),
        strides=strides,
        padding=padding,
        use_bias=False,
        name=conv_name)(
            x)
    x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
    x = layers.Activation('relu', name=name)(x)
    return x
  
  
  @keras_export('keras.applications.inception_v3.preprocess_input')
  def preprocess_input(x, data_format=None):
    return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
  
  
  @keras_export('keras.applications.inception_v3.decode_predictions')
  def decode_predictions(preds, top=5):
    return imagenet_utils.decode_predictions(preds, top=top)
  
  
  preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
      mode='',
      ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
      error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
  decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
  