Module keras.api.keras.applications.nasnet

Public API for tf.keras.applications.nasnet 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.nasnet namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.applications.nasnet import NASNetLarge
from keras.applications.nasnet import NASNetMobile
from keras.applications.nasnet import decode_predictions
from keras.applications.nasnet 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.nasnet", public_apis=None, deprecation=True,
      has_lite=False)

Functions

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

Instantiates a NASNet model in ImageNet mode.

Reference: - Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json.

Note: each Keras Application expects a specific kind of input preprocessing. For NASNet, call tf.keras.applications.nasnet.preprocess_input on your inputs before passing them to the model.

Args

input_shape
Optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (331, 331, 3) for NASNetLarge. It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (224, 224, 3) would be one valid value.
include_top
Whether to include the fully-connected layer at the top of the network.
weights
None (random initialization) or imagenet (ImageNet weights) For loading imagenet weights, input_shape should be (331, 331, 3)
input_tensor
Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
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.

Returns

A Keras model instance.

Raises

ValueError
in case of invalid argument for weights, or invalid input shape.
RuntimeError
If attempting to run this model with a backend that does not support separable convolutions.
Expand source code
@keras_export('keras.applications.nasnet.NASNetLarge',
              'keras.applications.NASNetLarge')
def NASNetLarge(input_shape=None,
                include_top=True,
                weights='imagenet',
                input_tensor=None,
                pooling=None,
                classes=1000):
  """Instantiates a NASNet model in ImageNet mode.

  Reference:
  - [Learning Transferable Architectures for Scalable Image Recognition](
      https://arxiv.org/abs/1707.07012) (CVPR 2018)

  Optionally loads weights pre-trained on ImageNet.
  Note that the data format convention used by the model is
  the one specified in your Keras config at `~/.keras/keras.json`.

  Note: each Keras Application expects a specific kind of input preprocessing.
  For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
  inputs before passing them to the model.

  Args:
      input_shape: Optional shape tuple, only to be specified
          if `include_top` is False (otherwise the input shape
          has to be `(331, 331, 3)` for NASNetLarge.
          It should have exactly 3 inputs channels,
          and width and height should be no smaller than 32.
          E.g. `(224, 224, 3)` would be one valid value.
      include_top: Whether to include the fully-connected
          layer at the top of the network.
      weights: `None` (random initialization) or
          `imagenet` (ImageNet weights)
          For loading `imagenet` weights, `input_shape` should be (331, 331, 3)
      input_tensor: Optional Keras tensor (i.e. output of
          `layers.Input()`)
          to use as image input for the model.
      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.

  Returns:
      A Keras model instance.

  Raises:
      ValueError: in case of invalid argument for `weights`,
          or invalid input shape.
      RuntimeError: If attempting to run this model with a
          backend that does not support separable convolutions.
  """
  return NASNet(
      input_shape,
      penultimate_filters=4032,
      num_blocks=6,
      stem_block_filters=96,
      skip_reduction=True,
      filter_multiplier=2,
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      pooling=pooling,
      classes=classes,
      default_size=331)
def NASNetMobile(input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)

Instantiates a Mobile NASNet model in ImageNet mode.

Reference: - Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json.

Note: each Keras Application expects a specific kind of input preprocessing. For NASNet, call tf.keras.applications.nasnet.preprocess_input on your inputs before passing them to the model.

Args

input_shape
Optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) for NASNetMobile It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (224, 224, 3) would be one valid value.
include_top
Whether to include the fully-connected layer at the top of the network.
weights
None (random initialization) or imagenet (ImageNet weights) For loading imagenet weights, input_shape should be (224, 224, 3)
input_tensor
Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
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.

Returns

A Keras model instance.

Raises

ValueError
In case of invalid argument for weights, or invalid input shape.
RuntimeError
If attempting to run this model with a backend that does not support separable convolutions.
Expand source code
@keras_export('keras.applications.nasnet.NASNetMobile',
              'keras.applications.NASNetMobile')
def NASNetMobile(input_shape=None,
                 include_top=True,
                 weights='imagenet',
                 input_tensor=None,
                 pooling=None,
                 classes=1000):
  """Instantiates a Mobile NASNet model in ImageNet mode.

  Reference:
  - [Learning Transferable Architectures for Scalable Image Recognition](
      https://arxiv.org/abs/1707.07012) (CVPR 2018)

  Optionally loads weights pre-trained on ImageNet.
  Note that the data format convention used by the model is
  the one specified in your Keras config at `~/.keras/keras.json`.

  Note: each Keras Application expects a specific kind of input preprocessing.
  For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
  inputs before passing them to the model.

  Args:
      input_shape: Optional shape tuple, only to be specified
          if `include_top` is False (otherwise the input shape
          has to be `(224, 224, 3)` for NASNetMobile
          It should have exactly 3 inputs channels,
          and width and height should be no smaller than 32.
          E.g. `(224, 224, 3)` would be one valid value.
      include_top: Whether to include the fully-connected
          layer at the top of the network.
      weights: `None` (random initialization) or
          `imagenet` (ImageNet weights)
          For loading `imagenet` weights, `input_shape` should be (224, 224, 3)
      input_tensor: Optional Keras tensor (i.e. output of
          `layers.Input()`)
          to use as image input for the model.
      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.

  Returns:
      A Keras model instance.

  Raises:
      ValueError: In case of invalid argument for `weights`,
          or invalid input shape.
      RuntimeError: If attempting to run this model with a
          backend that does not support separable convolutions.
  """
  return NASNet(
      input_shape,
      penultimate_filters=1056,
      num_blocks=4,
      stem_block_filters=32,
      skip_reduction=False,
      filter_multiplier=2,
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      pooling=pooling,
      classes=classes,
      default_size=224)
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.nasnet.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.nasnet.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')