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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)
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This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
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For image classification use cases, see this page for detailed examples.
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For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
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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.
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Arguments
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include_top: Whether to include the fully-connected layer at the top of the network. Defaults to True.
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weights: One of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to 'imagenet'.
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input_tensor: Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
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input_shape: Optional shape tuple, only to be specified if include_top is False. It should have exactly 3 inputs channels.
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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.
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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).
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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".
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Returns
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A keras.Model instance.
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EfficientNetB6 function
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tf.keras.applications.EfficientNetB6(
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include_top=True,
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weights="imagenet",
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation="softmax",
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**kwargs
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)
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Instantiates the EfficientNetB6 architecture.
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Reference
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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.
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Arguments
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include_top: Whether to include the fully-connected layer at the top of the network. Defaults to True.
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weights: One of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to 'imagenet'.
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input_tensor: Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
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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.
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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).
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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".
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Returns
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A keras.Model instance.
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EfficientNetB7 function
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tf.keras.applications.EfficientNetB7(
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include_top=True,
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weights="imagenet",
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation="softmax",
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**kwargs
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)
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Instantiates the EfficientNetB7 architecture.
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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
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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.
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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).
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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".
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Returns
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A keras.Model instance.
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InceptionResNetV2
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InceptionResNetV2 function
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tf.keras.applications.InceptionResNetV2(
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include_top=True,
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