tf.keras.applications.NASNetLarge( input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000 )
Instantiates a NASNet model in ImageNet mode.
Note that only TensorFlow is supported for now,
therefore it only works with the data format
image_data_format='channels_last' in your Keras config
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.