# tf.keras.applications.NASNetMobile

### Aliases:

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


Instantiates a Mobile 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 (224, 224, 3) for NASNetMobile 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.