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KerasAPIReference.txt

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EfficientNetB0 function

tf.keras.applications.EfficientNetB0(

include_top=True,

weights="imagenet",

input_tensor=None,

input_shape=None,

pooling=None,

classes=1000,

classifier_activation="softmax",

**kwargs

)

Instantiates the EfficientNetB0 architecture.

Reference

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Arguments

include_top: Whether to include the fully-connected layer at the top of the network. Defaults to True.

weights: One of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to 'imagenet'.

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. It should have exactly 3 inputs channels.

pooling: Optional pooling mode for feature extraction when include_top is False. Defaults to None. - None means that the output of the model will be the 4D tensor output of the last convolutional layer. - avg means that global average pooling will be applied to the output of the last convolutional layer, 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. Defaults to 1000 (number of ImageNet classes).

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. Defaults to 'softmax'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

EfficientNetB1 function

tf.keras.applications.EfficientNetB1(

include_top=True,

weights="imagenet",

input_tensor=None,

input_shape=None,

pooling=None,

classes=1000,

classifier_activation="softmax",

**kwargs

)

Instantiates the EfficientNetB1 architecture.

Reference

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Arguments

include_top: Whether to include the fully-connected layer at the top of the network. Defaults to True.

weights: One of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to 'imagenet'.

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. It should have exactly 3 inputs channels.

pooling: Optional pooling mode for feature extraction when include_top is False. Defaults to None. - None means that the output of the model will be the 4D tensor output of the last convolutional layer. - avg means that global average pooling will be applied to the output of the last convolutional layer, 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. Defaults to 1000 (number of ImageNet classes).

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. Defaults to 'softmax'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

EfficientNetB2 function

tf.keras.applications.EfficientNetB2(

include_top=True,

weights="imagenet",

input_tensor=None,

input_shape=None,

pooling=None,

classes=1000,

classifier_activation="softmax",

**kwargs

)

Instantiates the EfficientNetB2 architecture.

Reference

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Arguments