Module keras.api.keras.applications.resnet
Public API for tf.keras.applications.resnet 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 namespace.
"""
from __future__ import print_function as _print_function
import sys as _sys
from keras.applications.resnet import ResNet101
from keras.applications.resnet import ResNet152
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.resnet", public_apis=None, deprecation=True,
has_lite=False)
Functions
def ResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs)
-
Instantiates the ResNet101 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
isFalse
. -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 noweights
argument is specified. classifier_activation
- A
str
or callable. The activation function to use on the "top" layer. Ignored unlessinclude_top=True
. Setclassifier_activation=None
to return the logits of the "top" layer. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A Keras model instance.
Expand source code
@keras_export('keras.applications.resnet.ResNet101', 'keras.applications.ResNet101') def ResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNet101 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, 23, name='conv4') return stack1(x, 512, 3, name='conv5') return ResNet(stack_fn, False, True, 'resnet101', include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)
def ResNet152(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs)
-
Instantiates the ResNet152 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
isFalse
. -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 noweights
argument is specified. classifier_activation
- A
str
or callable. The activation function to use on the "top" layer. Ignored unlessinclude_top=True
. Setclassifier_activation=None
to return the logits of the "top" layer. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A Keras model instance.
Expand source code
@keras_export('keras.applications.resnet.ResNet152', 'keras.applications.ResNet152') def ResNet152(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNet152 architecture.""" def stack_fn(x): x = stack1(x, 64, 3, stride1=1, name='conv2') x = stack1(x, 128, 8, name='conv3') x = stack1(x, 256, 36, name='conv4') return stack1(x, 512, 3, name='conv5') return ResNet(stack_fn, False, True, 'resnet152', include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)
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
isFalse
. -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 noweights
argument is specified. classifier_activation
- A
str
or callable. The activation function to use on the "top" layer. Ignored unlessinclude_top=True
. Setclassifier_activation=None
to return the logits of the "top" layer. When loading pretrained weights,classifier_activation
can only beNone
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 atf.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 atf.Tensor
with typefloat32
.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')