tf.keras.applications.NASNetLarge

Aliases:

• tf.keras.applications.NASNetLarge
• tf.keras.applications.nasnet.NASNetLarge

tf.keras.applications.NASNetLarge(
    input_shape=None,
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    pooling=None,
    classes=1000
)

Defined in tensorflow/python/keras/_impl/keras/applications/nasnet.py.

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 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.