Module keras.api.keras.applications.resnet_v2

Public API for tf.keras.applications.resnet_v2 namespace.

Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.applications.resnet_v2 namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.applications.resnet_v2 import ResNet101V2
from keras.applications.resnet_v2 import ResNet152V2
from keras.applications.resnet_v2 import ResNet50V2
from keras.applications.resnet_v2 import decode_predictions
from keras.applications.resnet_v2 import preprocess_input

del _print_function

from tensorflow.python.util import module_wrapper as _module_wrapper

if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
      _sys.modules[__name__], "keras.applications.resnet_v2", public_apis=None, deprecation=True,
      has_lite=False)

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')