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.
Note: each 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 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.
