tf.keras.applications.NASNetLarge

TensorFlow 1 version View source on GitHub

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.

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.

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.