NasNetLarge and NasNetMobile

[source]

NASNetLarge function

tf_keras.applications.NASNetLarge(
    input_shape=None,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
)

Instantiates a NASNet model in ImageNet mode.

Reference

• Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your TF-Keras config at ~/.keras/keras.json.

Note: each TF-Keras Application expects a specific kind of input preprocessing. For NASNet, call tf.keras.applications.nasnet.preprocess_input on your inputs before passing them to the model.

Arguments

• input_shape: Optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (331, 331, 3) for NASNetLarge. It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (224, 224, 3) would be one valid value.
• include_top: Whether to include the fully-connected layer at the top of the network.
• weights: None (random initialization) or imagenet (ImageNet weights). For loading imagenet weights, input_shape should be (331, 331, 3)
• input_tensor: Optional TF-Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• 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 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.
• 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.

Raises

• ValueError: in case of invalid argument for weights, or invalid input shape.
• RuntimeError: If attempting to run this model with a backend that does not support separable convolutions.

[source]

NASNetMobile function

tf_keras.applications.NASNetMobile(
    input_shape=None,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
)

Instantiates a Mobile NASNet model in ImageNet mode.

Reference

• Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your TF-Keras config at ~/.keras/keras.json.

Note: each TF-Keras Application expects a specific kind of input preprocessing. For NASNet, call tf.keras.applications.nasnet.preprocess_input on your inputs before passing them to the model.

Arguments

• input_shape: Optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) for NASNetMobile It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (224, 224, 3) would be one valid value.
• include_top: Whether to include the fully-connected layer at the top of the network.
• weights: None (random initialization) or imagenet (ImageNet weights). For loading imagenet weights, input_shape should be (224, 224, 3)
• input_tensor: Optional TF-Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• 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 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.
• 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.

Raises

• ValueError: In case of invalid argument for weights, or invalid input shape.
• RuntimeError: If attempting to run this model with a backend that does not support separable convolutions.