tf.keras.applications.NASNetLarge

View source on GitHub

Instantiates a NASNet model in ImageNet mode.

View aliases

Main aliases

tf.keras.applications.nasnet.NASNetLarge

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.applications.NASNetLarge, tf.compat.v1.keras.applications.nasnet.NASNetLarge

tf.keras.applications.NASNetLarge(
    input_shape=None, include_top=True, weights='imagenet', input_tensor=None,
    pooling=None, classes=1000
)

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

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)
input_tensor	Optional 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.

Returns
A 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.