Module keras.applications.xception

Xception V1 model for Keras.

On ImageNet, this model gets to a top-1 validation accuracy of 0.790 and a top-5 validation accuracy of 0.945.

Reference

Expand source code
# Copyright 2016 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
"""Xception V1 model for Keras.

On ImageNet, this model gets to a top-1 validation accuracy of 0.790
and a top-5 validation accuracy of 0.945.

Reference:
  - [Xception: Deep Learning with Depthwise Separable Convolutions](
      https://arxiv.org/abs/1610.02357) (CVPR 2017)
"""

import tensorflow.compat.v2 as tf

from keras import backend
from keras.applications import imagenet_utils
from keras.engine import training
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils
from tensorflow.python.util.tf_export import keras_export


TF_WEIGHTS_PATH = (
    'https://storage.googleapis.com/tensorflow/keras-applications/'
    'xception/xception_weights_tf_dim_ordering_tf_kernels.h5')
TF_WEIGHTS_PATH_NO_TOP = (
    'https://storage.googleapis.com/tensorflow/keras-applications/'
    'xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5')

layers = VersionAwareLayers()


@keras_export('keras.applications.xception.Xception',
              'keras.applications.Xception')
def Xception(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the Xception architecture.

  Reference:
  - [Xception: Deep Learning with Depthwise Separable Convolutions](
      https://arxiv.org/abs/1610.02357) (CVPR 2017)

  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/).

  The default input image size for this model is 299x299.

  Note: each Keras Application expects a specific kind of input preprocessing.
  For Xception, call `tf.keras.applications.xception.preprocess_input` on your
  inputs before passing them to the model.
  `xception.preprocess_input` will scale input pixels between -1 and 1.

  Args:
    include_top: whether to include the fully-connected
      layer at the top of the network.
    weights: one of `None` (random initialization),
      'imagenet' (pre-training on ImageNet),
      or the path to the weights file to be loaded.
    input_tensor: optional Keras tensor
      (i.e. output of `layers.Input()`)
      to use as image input for the model.
    input_shape: optional shape tuple, only to be specified
      if `include_top` is False (otherwise the input shape
      has to be `(299, 299, 3)`.
      It should have exactly 3 inputs channels,
      and width and height should be no smaller than 71.
      E.g. `(150, 150, 3)` would be one valid value.
    pooling: Optional pooling mode for feature extraction
      when `include_top` is `False`.
      - `None` 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.
    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 tf.io.gfile.exists(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=71,
      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

  channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1

  x = layers.Conv2D(
      32, (3, 3),
      strides=(2, 2),
      use_bias=False,
      name='block1_conv1')(img_input)
  x = layers.BatchNormalization(axis=channel_axis, name='block1_conv1_bn')(x)
  x = layers.Activation('relu', name='block1_conv1_act')(x)
  x = layers.Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block1_conv2_bn')(x)
  x = layers.Activation('relu', name='block1_conv2_act')(x)

  residual = layers.Conv2D(
      128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = layers.BatchNormalization(axis=channel_axis)(residual)

  x = layers.SeparableConv2D(
      128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block2_sepconv2_act')(x)
  x = layers.SeparableConv2D(
      128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv2_bn')(x)

  x = layers.MaxPooling2D((3, 3),
                          strides=(2, 2),
                          padding='same',
                          name='block2_pool')(x)
  x = layers.add([x, residual])

  residual = layers.Conv2D(
      256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = layers.BatchNormalization(axis=channel_axis)(residual)

  x = layers.Activation('relu', name='block3_sepconv1_act')(x)
  x = layers.SeparableConv2D(
      256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block3_sepconv2_act')(x)
  x = layers.SeparableConv2D(
      256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv2_bn')(x)

  x = layers.MaxPooling2D((3, 3),
                          strides=(2, 2),
                          padding='same',
                          name='block3_pool')(x)
  x = layers.add([x, residual])

  residual = layers.Conv2D(
      728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = layers.BatchNormalization(axis=channel_axis)(residual)

  x = layers.Activation('relu', name='block4_sepconv1_act')(x)
  x = layers.SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block4_sepconv2_act')(x)
  x = layers.SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv2_bn')(x)

  x = layers.MaxPooling2D((3, 3),
                          strides=(2, 2),
                          padding='same',
                          name='block4_pool')(x)
  x = layers.add([x, residual])

  for i in range(8):
    residual = x
    prefix = 'block' + str(i + 5)

    x = layers.Activation('relu', name=prefix + '_sepconv1_act')(x)
    x = layers.SeparableConv2D(
        728, (3, 3),
        padding='same',
        use_bias=False,
        name=prefix + '_sepconv1')(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name=prefix + '_sepconv1_bn')(x)
    x = layers.Activation('relu', name=prefix + '_sepconv2_act')(x)
    x = layers.SeparableConv2D(
        728, (3, 3),
        padding='same',
        use_bias=False,
        name=prefix + '_sepconv2')(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name=prefix + '_sepconv2_bn')(x)
    x = layers.Activation('relu', name=prefix + '_sepconv3_act')(x)
    x = layers.SeparableConv2D(
        728, (3, 3),
        padding='same',
        use_bias=False,
        name=prefix + '_sepconv3')(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name=prefix + '_sepconv3_bn')(x)

    x = layers.add([x, residual])

  residual = layers.Conv2D(
      1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = layers.BatchNormalization(axis=channel_axis)(residual)

  x = layers.Activation('relu', name='block13_sepconv1_act')(x)
  x = layers.SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x)
  x = layers.BatchNormalization(
      axis=channel_axis, name='block13_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block13_sepconv2_act')(x)
  x = layers.SeparableConv2D(
      1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x)
  x = layers.BatchNormalization(
      axis=channel_axis, name='block13_sepconv2_bn')(x)

  x = layers.MaxPooling2D((3, 3),
                          strides=(2, 2),
                          padding='same',
                          name='block13_pool')(x)
  x = layers.add([x, residual])

  x = layers.SeparableConv2D(
      1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x)
  x = layers.BatchNormalization(
      axis=channel_axis, name='block14_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block14_sepconv1_act')(x)

  x = layers.SeparableConv2D(
      2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x)
  x = layers.BatchNormalization(
      axis=channel_axis, name='block14_sepconv2_bn')(x)
  x = layers.Activation('relu', name='block14_sepconv2_act')(x)

  if include_top:
    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='xception')

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      weights_path = data_utils.get_file(
          'xception_weights_tf_dim_ordering_tf_kernels.h5',
          TF_WEIGHTS_PATH,
          cache_subdir='models',
          file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
    else:
      weights_path = data_utils.get_file(
          'xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
          TF_WEIGHTS_PATH_NO_TOP,
          cache_subdir='models',
          file_hash='b0042744bf5b25fce3cb969f33bebb97')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model


@keras_export('keras.applications.xception.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.xception.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__

Functions

def Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax')

Instantiates the Xception architecture.

Reference: - Xception: Deep Learning with Depthwise Separable Convolutions (CVPR 2017)

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

The default input image size for this model is 299x299.

Note: each Keras Application expects a specific kind of input preprocessing. For Xception, call tf.keras.applications.xception.preprocess_input on your inputs before passing them to the model. xception.preprocess_input will scale input pixels between -1 and 1.

Args

include_top
whether to include the fully-connected layer at the top of the network.
weights
one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
input_tensor
optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
input_shape
optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (299, 299, 3). It should have exactly 3 inputs channels, and width and height should be no smaller than 71. E.g. (150, 150, 3) would be one valid value.
pooling
Optional pooling mode for feature extraction when include_top is False. - None 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.
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.

Expand source code
@keras_export('keras.applications.xception.Xception',
              'keras.applications.Xception')
def Xception(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the Xception architecture.

  Reference:
  - [Xception: Deep Learning with Depthwise Separable Convolutions](
      https://arxiv.org/abs/1610.02357) (CVPR 2017)

  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/).

  The default input image size for this model is 299x299.

  Note: each Keras Application expects a specific kind of input preprocessing.
  For Xception, call `tf.keras.applications.xception.preprocess_input` on your
  inputs before passing them to the model.
  `xception.preprocess_input` will scale input pixels between -1 and 1.

  Args:
    include_top: whether to include the fully-connected
      layer at the top of the network.
    weights: one of `None` (random initialization),
      'imagenet' (pre-training on ImageNet),
      or the path to the weights file to be loaded.
    input_tensor: optional Keras tensor
      (i.e. output of `layers.Input()`)
      to use as image input for the model.
    input_shape: optional shape tuple, only to be specified
      if `include_top` is False (otherwise the input shape
      has to be `(299, 299, 3)`.
      It should have exactly 3 inputs channels,
      and width and height should be no smaller than 71.
      E.g. `(150, 150, 3)` would be one valid value.
    pooling: Optional pooling mode for feature extraction
      when `include_top` is `False`.
      - `None` 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.
    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 tf.io.gfile.exists(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=71,
      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

  channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1

  x = layers.Conv2D(
      32, (3, 3),
      strides=(2, 2),
      use_bias=False,
      name='block1_conv1')(img_input)
  x = layers.BatchNormalization(axis=channel_axis, name='block1_conv1_bn')(x)
  x = layers.Activation('relu', name='block1_conv1_act')(x)
  x = layers.Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block1_conv2_bn')(x)
  x = layers.Activation('relu', name='block1_conv2_act')(x)

  residual = layers.Conv2D(
      128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = layers.BatchNormalization(axis=channel_axis)(residual)

  x = layers.SeparableConv2D(
      128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block2_sepconv2_act')(x)
  x = layers.SeparableConv2D(
      128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv2_bn')(x)

  x = layers.MaxPooling2D((3, 3),
                          strides=(2, 2),
                          padding='same',
                          name='block2_pool')(x)
  x = layers.add([x, residual])

  residual = layers.Conv2D(
      256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = layers.BatchNormalization(axis=channel_axis)(residual)

  x = layers.Activation('relu', name='block3_sepconv1_act')(x)
  x = layers.SeparableConv2D(
      256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block3_sepconv2_act')(x)
  x = layers.SeparableConv2D(
      256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv2_bn')(x)

  x = layers.MaxPooling2D((3, 3),
                          strides=(2, 2),
                          padding='same',
                          name='block3_pool')(x)
  x = layers.add([x, residual])

  residual = layers.Conv2D(
      728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = layers.BatchNormalization(axis=channel_axis)(residual)

  x = layers.Activation('relu', name='block4_sepconv1_act')(x)
  x = layers.SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block4_sepconv2_act')(x)
  x = layers.SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x)
  x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv2_bn')(x)

  x = layers.MaxPooling2D((3, 3),
                          strides=(2, 2),
                          padding='same',
                          name='block4_pool')(x)
  x = layers.add([x, residual])

  for i in range(8):
    residual = x
    prefix = 'block' + str(i + 5)

    x = layers.Activation('relu', name=prefix + '_sepconv1_act')(x)
    x = layers.SeparableConv2D(
        728, (3, 3),
        padding='same',
        use_bias=False,
        name=prefix + '_sepconv1')(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name=prefix + '_sepconv1_bn')(x)
    x = layers.Activation('relu', name=prefix + '_sepconv2_act')(x)
    x = layers.SeparableConv2D(
        728, (3, 3),
        padding='same',
        use_bias=False,
        name=prefix + '_sepconv2')(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name=prefix + '_sepconv2_bn')(x)
    x = layers.Activation('relu', name=prefix + '_sepconv3_act')(x)
    x = layers.SeparableConv2D(
        728, (3, 3),
        padding='same',
        use_bias=False,
        name=prefix + '_sepconv3')(x)
    x = layers.BatchNormalization(
        axis=channel_axis, name=prefix + '_sepconv3_bn')(x)

    x = layers.add([x, residual])

  residual = layers.Conv2D(
      1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = layers.BatchNormalization(axis=channel_axis)(residual)

  x = layers.Activation('relu', name='block13_sepconv1_act')(x)
  x = layers.SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x)
  x = layers.BatchNormalization(
      axis=channel_axis, name='block13_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block13_sepconv2_act')(x)
  x = layers.SeparableConv2D(
      1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x)
  x = layers.BatchNormalization(
      axis=channel_axis, name='block13_sepconv2_bn')(x)

  x = layers.MaxPooling2D((3, 3),
                          strides=(2, 2),
                          padding='same',
                          name='block13_pool')(x)
  x = layers.add([x, residual])

  x = layers.SeparableConv2D(
      1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x)
  x = layers.BatchNormalization(
      axis=channel_axis, name='block14_sepconv1_bn')(x)
  x = layers.Activation('relu', name='block14_sepconv1_act')(x)

  x = layers.SeparableConv2D(
      2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x)
  x = layers.BatchNormalization(
      axis=channel_axis, name='block14_sepconv2_bn')(x)
  x = layers.Activation('relu', name='block14_sepconv2_act')(x)

  if include_top:
    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='xception')

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      weights_path = data_utils.get_file(
          'xception_weights_tf_dim_ordering_tf_kernels.h5',
          TF_WEIGHTS_PATH,
          cache_subdir='models',
          file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
    else:
      weights_path = data_utils.get_file(
          'xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
          TF_WEIGHTS_PATH_NO_TOP,
          cache_subdir='models',
          file_hash='b0042744bf5b25fce3cb969f33bebb97')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model
def decode_predictions(preds, top=5)

Decodes the prediction of an ImageNet model.

Args

preds
Numpy array encoding a batch of predictions.
top
Integer, how many top-guesses to return. Defaults to 5.

Returns

A list of lists of top class prediction tuples (class_name, class_description, score). One list of tuples per sample in batch input.

Raises

ValueError
In case of invalid shape of the pred array (must be 2D).
Expand source code
@keras_export('keras.applications.xception.decode_predictions')
def decode_predictions(preds, top=5):
  return imagenet_utils.decode_predictions(preds, top=top)
def preprocess_input(x, data_format=None)

Preprocesses a tensor or Numpy array encoding a batch of images.

Usage example with applications.MobileNet:

i = tf.keras.layers.Input([None, None, 3], dtype = tf.uint8)
x = tf.cast(i, tf.float32)
x = tf.keras.applications.mobilenet.preprocess_input(x)
core = tf.keras.applications.MobileNet()
x = core(x)
model = tf.keras.Model(inputs=[i], outputs=[x])

image = tf.image.decode_png(tf.io.read_file('file.png'))
result = model(image)

Args

x
A floating point numpy.array or a tf.Tensor, 3D or 4D with 3 color channels, with values in the range [0, 255]. The preprocessed data are written over the input data if the data types are compatible. To avoid this behaviour, numpy.copy(x) can be used.
data_format
Optional data format of the image tensor/array. Defaults to None, in which case the global setting tf.keras.backend.image_data_format() is used (unless you changed it, it defaults to "channels_last").

Returns

Preprocessed numpy.array or a tf.Tensor with type float32.

The inputs pixel values are scaled between -1 and 1, sample-wise.

Raises

ValueError
In case of unknown data_format argument.
Expand source code
@keras_export('keras.applications.xception.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')