tf.keras.applications.MobileNet

Aliases:

  • tf.keras.applications.MobileNet
  • tf.keras.applications.mobilenet.MobileNet
tf.keras.applications.MobileNet(
    input_shape=None,
    alpha=1.0,
    depth_multiplier=1,
    dropout=0.001,
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    pooling=None,
    classes=1000
)

Defined in tensorflow/python/keras/applications/mobilenet.py.

Instantiates the MobileNet architecture.

To load a MobileNet model via load_model, import the custom objects relu6 and pass them to the custom_objects parameter. E.g. model = load_model('mobilenet.h5', custom_objects={ 'relu6': mobilenet.relu6})

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) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
  • alpha: controls the width of the network. - If alpha < 1.0, proportionally decreases the number of filters in each layer. - If alpha > 1.0, proportionally increases the number of filters in each layer. - If alpha = 1, default number of filters from the paper are used at each layer.
  • depth_multiplier: depth multiplier for depthwise convolution (also called the resolution multiplier)
  • dropout: dropout rate
  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
  • 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.