Module keras.applications.resnet_v2

ResNet v2 models for Keras.

Reference

Expand source code
# Copyright 2019 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
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# See the License for the specific language governing permissions and
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# ==============================================================================
# pylint: disable=invalid-name
"""ResNet v2 models for Keras.

Reference:
  - [Identity Mappings in Deep Residual Networks]
    (https://arxiv.org/abs/1603.05027) (CVPR 2016)
"""

from keras.applications import imagenet_utils
from keras.applications import resnet
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.applications.resnet_v2.ResNet50V2',
              'keras.applications.ResNet50V2')
def ResNet50V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet50V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 4, name='conv3')
    x = resnet.stack2(x, 256, 6, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet50v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)


@keras_export('keras.applications.resnet_v2.ResNet101V2',
              'keras.applications.ResNet101V2')
def ResNet101V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet101V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 4, name='conv3')
    x = resnet.stack2(x, 256, 23, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet101v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)


@keras_export('keras.applications.resnet_v2.ResNet152V2',
              'keras.applications.ResNet152V2')
def ResNet152V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet152V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 8, name='conv3')
    x = resnet.stack2(x, 256, 36, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet152v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)


@keras_export('keras.applications.resnet_v2.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.resnet_v2.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__

DOC = """

  Reference:
  - [Identity Mappings in Deep Residual Networks]
    (https://arxiv.org/abs/1603.05027) (CVPR 2016)

  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 ResNetV2, call `tf.keras.applications.resnet_v2.preprocess_input` on your
  inputs before passing them to the model.
  `resnet_v2.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 `(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(ResNet50V2, '__doc__', ResNet50V2.__doc__ + DOC)
setattr(ResNet101V2, '__doc__', ResNet101V2.__doc__ + DOC)
setattr(ResNet152V2, '__doc__', ResNet152V2.__doc__ + DOC)

Functions

def ResNet101V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax')

Instantiates the ResNet101V2 architecture.

Reference: - [Identity Mappings in Deep Residual Networks] (https://arxiv.org/abs/1603.05027) (CVPR 2016)

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 ResNetV2, call tf.keras.applications.resnet_v2.preprocess_input on your inputs before passing them to the model. resnet_v2.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 (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_v2.ResNet101V2',
              'keras.applications.ResNet101V2')
def ResNet101V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet101V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 4, name='conv3')
    x = resnet.stack2(x, 256, 23, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet101v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)
def ResNet152V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax')

Instantiates the ResNet152V2 architecture.

Reference: - [Identity Mappings in Deep Residual Networks] (https://arxiv.org/abs/1603.05027) (CVPR 2016)

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 ResNetV2, call tf.keras.applications.resnet_v2.preprocess_input on your inputs before passing them to the model. resnet_v2.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 (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_v2.ResNet152V2',
              'keras.applications.ResNet152V2')
def ResNet152V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet152V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 8, name='conv3')
    x = resnet.stack2(x, 256, 36, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet152v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)
def ResNet50V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax')

Instantiates the ResNet50V2 architecture.

Reference: - [Identity Mappings in Deep Residual Networks] (https://arxiv.org/abs/1603.05027) (CVPR 2016)

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 ResNetV2, call tf.keras.applications.resnet_v2.preprocess_input on your inputs before passing them to the model. resnet_v2.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 (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_v2.ResNet50V2',
              'keras.applications.ResNet50V2')
def ResNet50V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet50V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 4, name='conv3')
    x = resnet.stack2(x, 256, 6, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet50v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)
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.resnet_v2.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.resnet_v2.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(
      x, data_format=data_format, mode='tf')