`ResNet50`

function```
tf_keras.applications.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 TF-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.

**Arguments**

**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 TF-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 TF-Keras model instance.

`ResNet101`

function```
tf_keras.applications.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 TF-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.

**Arguments**

**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 TF-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 TF-Keras model instance.

`ResNet152`

function```
tf_keras.applications.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 TF-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.

**Arguments**

**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 TF-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 TF-Keras model instance.

`ResNet50V2`

function```
tf_keras.applications.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 (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 TF-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.

**Arguments**

**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 TF-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.

`ResNet101V2`

function```
tf_keras.applications.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 (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 TF-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.

**Arguments**

**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 TF-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.

`ResNet152V2`

function```
tf_keras.applications.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 (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 TF-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.

**Arguments**

**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 TF-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.