HenryAI/KerasAPIReference.txt · Datasets at Hugging Face

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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.

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 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] (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.

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 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] (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.

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 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.

EfficientNet B0 to B7

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.

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 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] (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.

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 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] (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.

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 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.

EfficientNet B0 to B7