Module keras.applications.resnet

ResNet models for Keras.

Reference

Expand source code
# 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
"""ResNet models for Keras.

Reference:
  - [Deep Residual Learning for Image Recognition](
      https://arxiv.org/abs/1512.03385) (CVPR 2015)
"""

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


BASE_WEIGHTS_PATH = (
    'https://storage.googleapis.com/tensorflow/keras-applications/resnet/')
WEIGHTS_HASHES = {
    'resnet50': ('2cb95161c43110f7111970584f804107',
                 '4d473c1dd8becc155b73f8504c6f6626'),
    'resnet101': ('f1aeb4b969a6efcfb50fad2f0c20cfc5',
                  '88cf7a10940856eca736dc7b7e228a21'),
    'resnet152': ('100835be76be38e30d865e96f2aaae62',
                  'ee4c566cf9a93f14d82f913c2dc6dd0c'),
    'resnet50v2': ('3ef43a0b657b3be2300d5770ece849e0',
                   'fac2f116257151a9d068a22e544a4917'),
    'resnet101v2': ('6343647c601c52e1368623803854d971',
                    'c0ed64b8031c3730f411d2eb4eea35b5'),
    'resnet152v2': ('a49b44d1979771252814e80f8ec446f9',
                    'ed17cf2e0169df9d443503ef94b23b33'),
    'resnext50': ('67a5b30d522ed92f75a1f16eef299d1a',
                  '62527c363bdd9ec598bed41947b379fc'),
    'resnext101':
        ('34fb605428fcc7aa4d62f44404c11509', '0f678c91647380debd923963594981b3')
}

layers = None


def ResNet(stack_fn,
           preact,
           use_bias,
           model_name='resnet',
           include_top=True,
           weights='imagenet',
           input_tensor=None,
           input_shape=None,
           pooling=None,
           classes=1000,
           classifier_activation='softmax',
           **kwargs):
  """Instantiates the ResNet, ResNetV2, and ResNeXt architecture.

  Args:
    stack_fn: a function that returns output tensor for the
      stacked residual blocks.
    preact: whether to use pre-activation or not
      (True for ResNetV2, False for ResNet and ResNeXt).
    use_bias: whether to use biases for convolutional layers or not
      (True for ResNet and ResNetV2, False for ResNeXt).
    model_name: string, model name.
    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 `(224, 224, 3)` (with `channels_last` data format)
      or `(3, 224, 224)` (with `channels_first` data format).
      It should have exactly 3 inputs channels.
    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 layer.
      - `avg` means that global average pooling
          will be applied to the output of the
          last convolutional layer, 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"`.
    **kwargs: For backwards compatibility only.

  Returns:
    A `keras.Model` instance.
  """
  global layers
  if 'layers' in kwargs:
    layers = kwargs.pop('layers')
  else:
    layers = VersionAwareLayers()
  if kwargs:
    raise ValueError('Unknown argument(s): %s' % (kwargs,))
  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=224,
      min_size=32,
      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

  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  x = layers.ZeroPadding2D(
      padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)
  x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name='conv1_conv')(x)

  if not preact:
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name='conv1_bn')(x)
    x = layers.Activation('relu', name='conv1_relu')(x)

  x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)
  x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)

  x = stack_fn(x)

  if preact:
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name='post_bn')(x)
    x = layers.Activation('relu', name='post_relu')(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(name='avg_pool')(x)
    elif pooling == 'max':
      x = layers.GlobalMaxPooling2D(name='max_pool')(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=model_name)

  # Load weights.
  if (weights == 'imagenet') and (model_name in WEIGHTS_HASHES):
    if include_top:
      file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5'
      file_hash = WEIGHTS_HASHES[model_name][0]
    else:
      file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5'
      file_hash = WEIGHTS_HASHES[model_name][1]
    weights_path = data_utils.get_file(
        file_name,
        BASE_WEIGHTS_PATH + file_name,
        cache_subdir='models',
        file_hash=file_hash)
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model


def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None):
  """A residual block.

  Args:
    x: input tensor.
    filters: integer, filters of the bottleneck layer.
    kernel_size: default 3, kernel size of the bottleneck layer.
    stride: default 1, stride of the first layer.
    conv_shortcut: default True, use convolution shortcut if True,
        otherwise identity shortcut.
    name: string, block label.

  Returns:
    Output tensor for the residual block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  if conv_shortcut:
    shortcut = layers.Conv2D(
        4 * filters, 1, strides=stride, name=name + '_0_conv')(x)
    shortcut = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut)
  else:
    shortcut = x

  x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
  x = layers.Activation('relu', name=name + '_1_relu')(x)

  x = layers.Conv2D(
      filters, kernel_size, padding='SAME', name=name + '_2_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
  x = layers.Activation('relu', name=name + '_2_relu')(x)

  x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x)

  x = layers.Add(name=name + '_add')([shortcut, x])
  x = layers.Activation('relu', name=name + '_out')(x)
  return x


def stack1(x, filters, blocks, stride1=2, name=None):
  """A set of stacked residual blocks.

  Args:
    x: input tensor.
    filters: integer, filters of the bottleneck layer in a block.
    blocks: integer, blocks in the stacked blocks.
    stride1: default 2, stride of the first layer in the first block.
    name: string, stack label.

  Returns:
    Output tensor for the stacked blocks.
  """
  x = block1(x, filters, stride=stride1, name=name + '_block1')
  for i in range(2, blocks + 1):
    x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i))
  return x


def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None):
  """A residual block.

  Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer.
      kernel_size: default 3, kernel size of the bottleneck layer.
      stride: default 1, stride of the first layer.
      conv_shortcut: default False, use convolution shortcut if True,
        otherwise identity shortcut.
      name: string, block label.

  Returns:
    Output tensor for the residual block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  preact = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_preact_bn')(x)
  preact = layers.Activation('relu', name=name + '_preact_relu')(preact)

  if conv_shortcut:
    shortcut = layers.Conv2D(
        4 * filters, 1, strides=stride, name=name + '_0_conv')(preact)
  else:
    shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x

  x = layers.Conv2D(
      filters, 1, strides=1, use_bias=False, name=name + '_1_conv')(preact)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
  x = layers.Activation('relu', name=name + '_1_relu')(x)

  x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
  x = layers.Conv2D(
      filters,
      kernel_size,
      strides=stride,
      use_bias=False,
      name=name + '_2_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
  x = layers.Activation('relu', name=name + '_2_relu')(x)

  x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
  x = layers.Add(name=name + '_out')([shortcut, x])
  return x


def stack2(x, filters, blocks, stride1=2, name=None):
  """A set of stacked residual blocks.

  Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer in a block.
      blocks: integer, blocks in the stacked blocks.
      stride1: default 2, stride of the first layer in the first block.
      name: string, stack label.

  Returns:
      Output tensor for the stacked blocks.
  """
  x = block2(x, filters, conv_shortcut=True, name=name + '_block1')
  for i in range(2, blocks):
    x = block2(x, filters, name=name + '_block' + str(i))
  x = block2(x, filters, stride=stride1, name=name + '_block' + str(blocks))
  return x


def block3(x,
           filters,
           kernel_size=3,
           stride=1,
           groups=32,
           conv_shortcut=True,
           name=None):
  """A residual block.

  Args:
    x: input tensor.
    filters: integer, filters of the bottleneck layer.
    kernel_size: default 3, kernel size of the bottleneck layer.
    stride: default 1, stride of the first layer.
    groups: default 32, group size for grouped convolution.
    conv_shortcut: default True, use convolution shortcut if True,
        otherwise identity shortcut.
    name: string, block label.

  Returns:
    Output tensor for the residual block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  if conv_shortcut:
    shortcut = layers.Conv2D(
        (64 // groups) * filters,
        1,
        strides=stride,
        use_bias=False,
        name=name + '_0_conv')(x)
    shortcut = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut)
  else:
    shortcut = x

  x = layers.Conv2D(filters, 1, use_bias=False, name=name + '_1_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
  x = layers.Activation('relu', name=name + '_1_relu')(x)

  c = filters // groups
  x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
  x = layers.DepthwiseConv2D(
      kernel_size,
      strides=stride,
      depth_multiplier=c,
      use_bias=False,
      name=name + '_2_conv')(x)
  x_shape = backend.shape(x)[:-1]
  x = backend.reshape(x, backend.concatenate([x_shape, (groups, c, c)]))
  x = layers.Lambda(
      lambda x: sum(x[:, :, :, :, i] for i in range(c)),
      name=name + '_2_reduce')(x)
  x = backend.reshape(x, backend.concatenate([x_shape, (filters,)]))
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
  x = layers.Activation('relu', name=name + '_2_relu')(x)

  x = layers.Conv2D(
      (64 // groups) * filters, 1, use_bias=False, name=name + '_3_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x)

  x = layers.Add(name=name + '_add')([shortcut, x])
  x = layers.Activation('relu', name=name + '_out')(x)
  return x


def stack3(x, filters, blocks, stride1=2, groups=32, name=None):
  """A set of stacked residual blocks.

  Args:
    x: input tensor.
    filters: integer, filters of the bottleneck layer in a block.
    blocks: integer, blocks in the stacked blocks.
    stride1: default 2, stride of the first layer in the first block.
    groups: default 32, group size for grouped convolution.
    name: string, stack label.

  Returns:
    Output tensor for the stacked blocks.
  """
  x = block3(x, filters, stride=stride1, groups=groups, name=name + '_block1')
  for i in range(2, blocks + 1):
    x = block3(
        x,
        filters,
        groups=groups,
        conv_shortcut=False,
        name=name + '_block' + str(i))
  return x


@keras_export('keras.applications.resnet50.ResNet50',
              'keras.applications.resnet.ResNet50',
              'keras.applications.ResNet50')
def ResNet50(include_top=True,
             weights='imagenet',
             input_tensor=None,
             input_shape=None,
             pooling=None,
             classes=1000,
             **kwargs):
  """Instantiates the ResNet50 architecture."""

  def stack_fn(x):
    x = stack1(x, 64, 3, stride1=1, name='conv2')
    x = stack1(x, 128, 4, name='conv3')
    x = stack1(x, 256, 6, name='conv4')
    return stack1(x, 512, 3, name='conv5')

  return ResNet(stack_fn, False, True, 'resnet50', include_top, weights,
                input_tensor, input_shape, pooling, classes, **kwargs)


@keras_export('keras.applications.resnet.ResNet101',
              'keras.applications.ResNet101')
def ResNet101(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000,
              **kwargs):
  """Instantiates the ResNet101 architecture."""

  def stack_fn(x):
    x = stack1(x, 64, 3, stride1=1, name='conv2')
    x = stack1(x, 128, 4, name='conv3')
    x = stack1(x, 256, 23, name='conv4')
    return stack1(x, 512, 3, name='conv5')

  return ResNet(stack_fn, False, True, 'resnet101', include_top, weights,
                input_tensor, input_shape, pooling, classes, **kwargs)


@keras_export('keras.applications.resnet.ResNet152',
              'keras.applications.ResNet152')
def ResNet152(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000,
              **kwargs):
  """Instantiates the ResNet152 architecture."""

  def stack_fn(x):
    x = stack1(x, 64, 3, stride1=1, name='conv2')
    x = stack1(x, 128, 8, name='conv3')
    x = stack1(x, 256, 36, name='conv4')
    return stack1(x, 512, 3, name='conv5')

  return ResNet(stack_fn, False, True, 'resnet152', include_top, weights,
                input_tensor, input_shape, pooling, classes, **kwargs)


@keras_export('keras.applications.resnet50.preprocess_input',
              'keras.applications.resnet.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(
      x, data_format=data_format, mode='caffe')


@keras_export('keras.applications.resnet50.decode_predictions',
              'keras.applications.resnet.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_CAFFE,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__

DOC = """

  Reference:
  - [Deep Residual Learning for Image Recognition](
      https://arxiv.org/abs/1512.03385) (CVPR 2015)

  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 ResNet, call `tf.keras.applications.resnet.preprocess_input` on your
  inputs before passing them to the model.
  `resnet.preprocess_input` will convert the input images from RGB to BGR,
  then will zero-center each color channel with respect to the ImageNet dataset,
  without scaling.

  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 `(224, 224, 3)` (with `'channels_last'` data format)
      or `(3, 224, 224)` (with `'channels_first'` data format).
      It should have exactly 3 inputs channels,
      and width and height should be no smaller than 32.
      E.g. `(200, 200, 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.
"""

setattr(ResNet50, '__doc__', ResNet50.__doc__ + DOC)
setattr(ResNet101, '__doc__', ResNet101.__doc__ + DOC)
setattr(ResNet152, '__doc__', ResNet152.__doc__ + DOC)

Functions

def ResNet(stack_fn, preact, use_bias, model_name='resnet', include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the ResNet, ResNetV2, and ResNeXt architecture.

Args

stack_fn
a function that returns output tensor for the stacked residual blocks.
preact
whether to use pre-activation or not (True for ResNetV2, False for ResNet and ResNeXt).
use_bias
whether to use biases for convolutional layers or not (True for ResNet and ResNetV2, False for ResNeXt).
model_name
string, model name.
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 (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels.
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 layer. - avg means that global average pooling will be applied to the output of the last convolutional layer, 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".
**kwargs
For backwards compatibility only.

Returns

A keras.Model instance.

Expand source code
def ResNet(stack_fn,
           preact,
           use_bias,
           model_name='resnet',
           include_top=True,
           weights='imagenet',
           input_tensor=None,
           input_shape=None,
           pooling=None,
           classes=1000,
           classifier_activation='softmax',
           **kwargs):
  """Instantiates the ResNet, ResNetV2, and ResNeXt architecture.

  Args:
    stack_fn: a function that returns output tensor for the
      stacked residual blocks.
    preact: whether to use pre-activation or not
      (True for ResNetV2, False for ResNet and ResNeXt).
    use_bias: whether to use biases for convolutional layers or not
      (True for ResNet and ResNetV2, False for ResNeXt).
    model_name: string, model name.
    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 `(224, 224, 3)` (with `channels_last` data format)
      or `(3, 224, 224)` (with `channels_first` data format).
      It should have exactly 3 inputs channels.
    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 layer.
      - `avg` means that global average pooling
          will be applied to the output of the
          last convolutional layer, 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"`.
    **kwargs: For backwards compatibility only.

  Returns:
    A `keras.Model` instance.
  """
  global layers
  if 'layers' in kwargs:
    layers = kwargs.pop('layers')
  else:
    layers = VersionAwareLayers()
  if kwargs:
    raise ValueError('Unknown argument(s): %s' % (kwargs,))
  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=224,
      min_size=32,
      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

  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  x = layers.ZeroPadding2D(
      padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)
  x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name='conv1_conv')(x)

  if not preact:
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name='conv1_bn')(x)
    x = layers.Activation('relu', name='conv1_relu')(x)

  x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)
  x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)

  x = stack_fn(x)

  if preact:
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name='post_bn')(x)
    x = layers.Activation('relu', name='post_relu')(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(name='avg_pool')(x)
    elif pooling == 'max':
      x = layers.GlobalMaxPooling2D(name='max_pool')(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=model_name)

  # Load weights.
  if (weights == 'imagenet') and (model_name in WEIGHTS_HASHES):
    if include_top:
      file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5'
      file_hash = WEIGHTS_HASHES[model_name][0]
    else:
      file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5'
      file_hash = WEIGHTS_HASHES[model_name][1]
    weights_path = data_utils.get_file(
        file_name,
        BASE_WEIGHTS_PATH + file_name,
        cache_subdir='models',
        file_hash=file_hash)
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model
def ResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs)

Instantiates the ResNet101 architecture.

Reference: - Deep Residual Learning for Image Recognition (CVPR 2015)

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.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call tf.keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

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 (224, 224, 3) (with 'channels_last' data format) or (3, 224, 224) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 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.resnet.ResNet101',
              'keras.applications.ResNet101')
def ResNet101(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000,
              **kwargs):
  """Instantiates the ResNet101 architecture."""

  def stack_fn(x):
    x = stack1(x, 64, 3, stride1=1, name='conv2')
    x = stack1(x, 128, 4, name='conv3')
    x = stack1(x, 256, 23, name='conv4')
    return stack1(x, 512, 3, name='conv5')

  return ResNet(stack_fn, False, True, 'resnet101', include_top, weights,
                input_tensor, input_shape, pooling, classes, **kwargs)
def ResNet152(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs)

Instantiates the ResNet152 architecture.

Reference: - Deep Residual Learning for Image Recognition (CVPR 2015)

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.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call tf.keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

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 (224, 224, 3) (with 'channels_last' data format) or (3, 224, 224) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 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.resnet.ResNet152',
              'keras.applications.ResNet152')
def ResNet152(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000,
              **kwargs):
  """Instantiates the ResNet152 architecture."""

  def stack_fn(x):
    x = stack1(x, 64, 3, stride1=1, name='conv2')
    x = stack1(x, 128, 8, name='conv3')
    x = stack1(x, 256, 36, name='conv4')
    return stack1(x, 512, 3, name='conv5')

  return ResNet(stack_fn, False, True, 'resnet152', include_top, weights,
                input_tensor, input_shape, pooling, classes, **kwargs)
def ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs)

Instantiates the ResNet50 architecture.

Reference: - Deep Residual Learning for Image Recognition (CVPR 2015)

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.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call tf.keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

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 (224, 224, 3) (with 'channels_last' data format) or (3, 224, 224) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 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.resnet50.ResNet50',
              'keras.applications.resnet.ResNet50',
              'keras.applications.ResNet50')
def ResNet50(include_top=True,
             weights='imagenet',
             input_tensor=None,
             input_shape=None,
             pooling=None,
             classes=1000,
             **kwargs):
  """Instantiates the ResNet50 architecture."""

  def stack_fn(x):
    x = stack1(x, 64, 3, stride1=1, name='conv2')
    x = stack1(x, 128, 4, name='conv3')
    x = stack1(x, 256, 6, name='conv4')
    return stack1(x, 512, 3, name='conv5')

  return ResNet(stack_fn, False, True, 'resnet50', include_top, weights,
                input_tensor, input_shape, pooling, classes, **kwargs)
def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None)

A residual block.

Args

x
input tensor.
filters
integer, filters of the bottleneck layer.
kernel_size
default 3, kernel size of the bottleneck layer.
stride
default 1, stride of the first layer.
conv_shortcut
default True, use convolution shortcut if True, otherwise identity shortcut.
name
string, block label.

Returns

Output tensor for the residual block.

Expand source code
def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None):
  """A residual block.

  Args:
    x: input tensor.
    filters: integer, filters of the bottleneck layer.
    kernel_size: default 3, kernel size of the bottleneck layer.
    stride: default 1, stride of the first layer.
    conv_shortcut: default True, use convolution shortcut if True,
        otherwise identity shortcut.
    name: string, block label.

  Returns:
    Output tensor for the residual block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  if conv_shortcut:
    shortcut = layers.Conv2D(
        4 * filters, 1, strides=stride, name=name + '_0_conv')(x)
    shortcut = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut)
  else:
    shortcut = x

  x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
  x = layers.Activation('relu', name=name + '_1_relu')(x)

  x = layers.Conv2D(
      filters, kernel_size, padding='SAME', name=name + '_2_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
  x = layers.Activation('relu', name=name + '_2_relu')(x)

  x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x)

  x = layers.Add(name=name + '_add')([shortcut, x])
  x = layers.Activation('relu', name=name + '_out')(x)
  return x
def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None)

A residual block.

Args

x
input tensor.
filters
integer, filters of the bottleneck layer.
kernel_size
default 3, kernel size of the bottleneck layer.
stride
default 1, stride of the first layer.
conv_shortcut
default False, use convolution shortcut if True, otherwise identity shortcut.
name
string, block label.

Returns

Output tensor for the residual block.

Expand source code
def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None):
  """A residual block.

  Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer.
      kernel_size: default 3, kernel size of the bottleneck layer.
      stride: default 1, stride of the first layer.
      conv_shortcut: default False, use convolution shortcut if True,
        otherwise identity shortcut.
      name: string, block label.

  Returns:
    Output tensor for the residual block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  preact = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_preact_bn')(x)
  preact = layers.Activation('relu', name=name + '_preact_relu')(preact)

  if conv_shortcut:
    shortcut = layers.Conv2D(
        4 * filters, 1, strides=stride, name=name + '_0_conv')(preact)
  else:
    shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x

  x = layers.Conv2D(
      filters, 1, strides=1, use_bias=False, name=name + '_1_conv')(preact)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
  x = layers.Activation('relu', name=name + '_1_relu')(x)

  x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
  x = layers.Conv2D(
      filters,
      kernel_size,
      strides=stride,
      use_bias=False,
      name=name + '_2_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
  x = layers.Activation('relu', name=name + '_2_relu')(x)

  x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
  x = layers.Add(name=name + '_out')([shortcut, x])
  return x
def block3(x, filters, kernel_size=3, stride=1, groups=32, conv_shortcut=True, name=None)

A residual block.

Args

x
input tensor.
filters
integer, filters of the bottleneck layer.
kernel_size
default 3, kernel size of the bottleneck layer.
stride
default 1, stride of the first layer.
groups
default 32, group size for grouped convolution.
conv_shortcut
default True, use convolution shortcut if True, otherwise identity shortcut.
name
string, block label.

Returns

Output tensor for the residual block.

Expand source code
def block3(x,
           filters,
           kernel_size=3,
           stride=1,
           groups=32,
           conv_shortcut=True,
           name=None):
  """A residual block.

  Args:
    x: input tensor.
    filters: integer, filters of the bottleneck layer.
    kernel_size: default 3, kernel size of the bottleneck layer.
    stride: default 1, stride of the first layer.
    groups: default 32, group size for grouped convolution.
    conv_shortcut: default True, use convolution shortcut if True,
        otherwise identity shortcut.
    name: string, block label.

  Returns:
    Output tensor for the residual block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  if conv_shortcut:
    shortcut = layers.Conv2D(
        (64 // groups) * filters,
        1,
        strides=stride,
        use_bias=False,
        name=name + '_0_conv')(x)
    shortcut = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut)
  else:
    shortcut = x

  x = layers.Conv2D(filters, 1, use_bias=False, name=name + '_1_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
  x = layers.Activation('relu', name=name + '_1_relu')(x)

  c = filters // groups
  x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
  x = layers.DepthwiseConv2D(
      kernel_size,
      strides=stride,
      depth_multiplier=c,
      use_bias=False,
      name=name + '_2_conv')(x)
  x_shape = backend.shape(x)[:-1]
  x = backend.reshape(x, backend.concatenate([x_shape, (groups, c, c)]))
  x = layers.Lambda(
      lambda x: sum(x[:, :, :, :, i] for i in range(c)),
      name=name + '_2_reduce')(x)
  x = backend.reshape(x, backend.concatenate([x_shape, (filters,)]))
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
  x = layers.Activation('relu', name=name + '_2_relu')(x)

  x = layers.Conv2D(
      (64 // groups) * filters, 1, use_bias=False, name=name + '_3_conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x)

  x = layers.Add(name=name + '_add')([shortcut, x])
  x = layers.Activation('relu', name=name + '_out')(x)
  return x
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.resnet50.decode_predictions',
              'keras.applications.resnet.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 images are converted from RGB to BGR, then each color channel is zero-centered with respect to the ImageNet dataset, without scaling.

Raises

ValueError
In case of unknown data_format argument.
Expand source code
@keras_export('keras.applications.resnet50.preprocess_input',
              'keras.applications.resnet.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(
      x, data_format=data_format, mode='caffe')
def stack1(x, filters, blocks, stride1=2, name=None)

A set of stacked residual blocks.

Args

x
input tensor.
filters
integer, filters of the bottleneck layer in a block.
blocks
integer, blocks in the stacked blocks.
stride1
default 2, stride of the first layer in the first block.
name
string, stack label.

Returns

Output tensor for the stacked blocks.

Expand source code
def stack1(x, filters, blocks, stride1=2, name=None):
  """A set of stacked residual blocks.

  Args:
    x: input tensor.
    filters: integer, filters of the bottleneck layer in a block.
    blocks: integer, blocks in the stacked blocks.
    stride1: default 2, stride of the first layer in the first block.
    name: string, stack label.

  Returns:
    Output tensor for the stacked blocks.
  """
  x = block1(x, filters, stride=stride1, name=name + '_block1')
  for i in range(2, blocks + 1):
    x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i))
  return x
def stack2(x, filters, blocks, stride1=2, name=None)

A set of stacked residual blocks.

Args

x
input tensor.
filters
integer, filters of the bottleneck layer in a block.
blocks
integer, blocks in the stacked blocks.
stride1
default 2, stride of the first layer in the first block.
name
string, stack label.

Returns

Output tensor for the stacked blocks.

Expand source code
def stack2(x, filters, blocks, stride1=2, name=None):
  """A set of stacked residual blocks.

  Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer in a block.
      blocks: integer, blocks in the stacked blocks.
      stride1: default 2, stride of the first layer in the first block.
      name: string, stack label.

  Returns:
      Output tensor for the stacked blocks.
  """
  x = block2(x, filters, conv_shortcut=True, name=name + '_block1')
  for i in range(2, blocks):
    x = block2(x, filters, name=name + '_block' + str(i))
  x = block2(x, filters, stride=stride1, name=name + '_block' + str(blocks))
  return x
def stack3(x, filters, blocks, stride1=2, groups=32, name=None)

A set of stacked residual blocks.

Args

x
input tensor.
filters
integer, filters of the bottleneck layer in a block.
blocks
integer, blocks in the stacked blocks.
stride1
default 2, stride of the first layer in the first block.
groups
default 32, group size for grouped convolution.
name
string, stack label.

Returns

Output tensor for the stacked blocks.

Expand source code
def stack3(x, filters, blocks, stride1=2, groups=32, name=None):
  """A set of stacked residual blocks.

  Args:
    x: input tensor.
    filters: integer, filters of the bottleneck layer in a block.
    blocks: integer, blocks in the stacked blocks.
    stride1: default 2, stride of the first layer in the first block.
    groups: default 32, group size for grouped convolution.
    name: string, stack label.

  Returns:
    Output tensor for the stacked blocks.
  """
  x = block3(x, filters, stride=stride1, groups=groups, name=name + '_block1')
  for i in range(2, blocks + 1):
    x = block3(
        x,
        filters,
        groups=groups,
        conv_shortcut=False,
        name=name + '_block' + str(i))
  return x