Module keras.api.keras.applications.resnet50

Public API for tf.keras.applications.resnet50 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.resnet50 namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.applications.resnet import ResNet50
from keras.applications.resnet import decode_predictions
from keras.applications.resnet 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.resnet50", public_apis=None, deprecation=True,
      has_lite=False)

Functions

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 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')