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
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_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
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_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
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_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 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 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')