Module keras.api.keras.applications.inception_resnet_v2

Public API for tf.keras.applications.inception_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.inception_resnet_v2 namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.inception_resnet_v2 import decode_predictions
from keras.applications.inception_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.inception_resnet_v2", public_apis=None, deprecation=True,
      has_lite=False)

Functions

def InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the Inception-ResNet v2 architecture.

Reference: - Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (AAAI 2017)

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

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

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 InceptionResNetV2, call tf.keras.applications.inception_resnet_v2.preprocess_input on your inputs before passing them to the model. inception_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 (299, 299, 3) (with 'channels_last' data format) or (3, 299, 299) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. (150, 150, 3) would be one valid value.
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".
**kwargs
For backwards compatibility only.

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.inception_resnet_v2.InceptionResNetV2',
              'keras.applications.InceptionResNetV2')
def InceptionResNetV2(include_top=True,
                      weights='imagenet',
                      input_tensor=None,
                      input_shape=None,
                      pooling=None,
                      classes=1000,
                      classifier_activation='softmax',
                      **kwargs):
  """Instantiates the Inception-ResNet v2 architecture.

  Reference:
  - [Inception-v4, Inception-ResNet and the Impact of
     Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
    (AAAI 2017)

  This function returns a Keras image classification model,
  optionally loaded with weights pre-trained on ImageNet.

  For image classification use cases, see
  [this page for detailed examples](
    https://keras.io/api/applications/#usage-examples-for-image-classification-models).

  For transfer learning use cases, make sure to read the
  [guide to transfer learning & fine-tuning](
    https://keras.io/guides/transfer_learning/).

  Note: each Keras Application expects a specific kind of input preprocessing.
  For InceptionResNetV2, call
  `tf.keras.applications.inception_resnet_v2.preprocess_input`
  on your inputs before passing them to the model.
  `inception_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 `(299, 299, 3)` (with `'channels_last'` data format)
      or `(3, 299, 299)` (with `'channels_first'` data format).
      It should have exactly 3 inputs channels,
      and width and height should be no smaller than 75.
      E.g. `(150, 150, 3)` would be one valid value.
    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"`.
    **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=299,
      min_size=75,
      data_format=backend.image_data_format(),
      require_flatten=include_top,
      weights=weights)

  if input_tensor is None:
    img_input = layers.Input(shape=input_shape)
  else:
    if not backend.is_keras_tensor(input_tensor):
      img_input = layers.Input(tensor=input_tensor, shape=input_shape)
    else:
      img_input = input_tensor

  # Stem block: 35 x 35 x 192
  x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
  x = conv2d_bn(x, 32, 3, padding='valid')
  x = conv2d_bn(x, 64, 3)
  x = layers.MaxPooling2D(3, strides=2)(x)
  x = conv2d_bn(x, 80, 1, padding='valid')
  x = conv2d_bn(x, 192, 3, padding='valid')
  x = layers.MaxPooling2D(3, strides=2)(x)

  # Mixed 5b (Inception-A block): 35 x 35 x 320
  branch_0 = conv2d_bn(x, 96, 1)
  branch_1 = conv2d_bn(x, 48, 1)
  branch_1 = conv2d_bn(branch_1, 64, 5)
  branch_2 = conv2d_bn(x, 64, 1)
  branch_2 = conv2d_bn(branch_2, 96, 3)
  branch_2 = conv2d_bn(branch_2, 96, 3)
  branch_pool = layers.AveragePooling2D(3, strides=1, padding='same')(x)
  branch_pool = conv2d_bn(branch_pool, 64, 1)
  branches = [branch_0, branch_1, branch_2, branch_pool]
  channel_axis = 1 if backend.image_data_format() == 'channels_first' else 3
  x = layers.Concatenate(axis=channel_axis, name='mixed_5b')(branches)

  # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320
  for block_idx in range(1, 11):
    x = inception_resnet_block(
        x, scale=0.17, block_type='block35', block_idx=block_idx)

  # Mixed 6a (Reduction-A block): 17 x 17 x 1088
  branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')
  branch_1 = conv2d_bn(x, 256, 1)
  branch_1 = conv2d_bn(branch_1, 256, 3)
  branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid')
  branch_pool = layers.MaxPooling2D(3, strides=2, padding='valid')(x)
  branches = [branch_0, branch_1, branch_pool]
  x = layers.Concatenate(axis=channel_axis, name='mixed_6a')(branches)

  # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088
  for block_idx in range(1, 21):
    x = inception_resnet_block(
        x, scale=0.1, block_type='block17', block_idx=block_idx)

  # Mixed 7a (Reduction-B block): 8 x 8 x 2080
  branch_0 = conv2d_bn(x, 256, 1)
  branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid')
  branch_1 = conv2d_bn(x, 256, 1)
  branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid')
  branch_2 = conv2d_bn(x, 256, 1)
  branch_2 = conv2d_bn(branch_2, 288, 3)
  branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid')
  branch_pool = layers.MaxPooling2D(3, strides=2, padding='valid')(x)
  branches = [branch_0, branch_1, branch_2, branch_pool]
  x = layers.Concatenate(axis=channel_axis, name='mixed_7a')(branches)

  # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080
  for block_idx in range(1, 10):
    x = inception_resnet_block(
        x, scale=0.2, block_type='block8', block_idx=block_idx)
  x = inception_resnet_block(
      x, scale=1., activation=None, block_type='block8', block_idx=10)

  # Final convolution block: 8 x 8 x 1536
  x = conv2d_bn(x, 1536, 1, name='conv_7b')

  if include_top:
    # Classification block
    x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
    imagenet_utils.validate_activation(classifier_activation, weights)
    x = layers.Dense(classes, activation=classifier_activation,
                     name='predictions')(x)
  else:
    if pooling == 'avg':
      x = layers.GlobalAveragePooling2D()(x)
    elif pooling == 'max':
      x = layers.GlobalMaxPooling2D()(x)

  # Ensure that the model takes into account
  # any potential predecessors of `input_tensor`.
  if input_tensor is not None:
    inputs = layer_utils.get_source_inputs(input_tensor)
  else:
    inputs = img_input

  # Create model.
  model = training.Model(inputs, x, name='inception_resnet_v2')

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5'
      weights_path = data_utils.get_file(
          fname,
          BASE_WEIGHT_URL + fname,
          cache_subdir='models',
          file_hash='e693bd0210a403b3192acc6073ad2e96')
    else:
      fname = ('inception_resnet_v2_weights_'
               'tf_dim_ordering_tf_kernels_notop.h5')
      weights_path = data_utils.get_file(
          fname,
          BASE_WEIGHT_URL + fname,
          cache_subdir='models',
          file_hash='d19885ff4a710c122648d3b5c3b684e4')
    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.inception_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.inception_resnet_v2.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')