|TensorFlow 1 version||View source on GitHub|
Instantiates a NASNet model in ImageNet mode.
See Migration guide for more details.
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
input_shape: Optional shape tuple, only to be specified if
include_topis 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.
None(random initialization) or
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
Nonemeans that the output of the model will be the 4D tensor output of the last convolutional layer.
avgmeans 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.
maxmeans that global max pooling will be applied.
classes: Optional number of classes to classify images into, only to be specified if
include_topis True, and if no
weightsargument is specified.
A Keras model instance.
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.