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tf.keras.applications.NASNetLarge

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Instantiates a NASNet model in ImageNet mode.

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.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/applications/NASNetLarge