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Instantiates the MobileNet architecture.

Reference paper:

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in the tf.keras.backend.image_data_format().

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. Default to None. input_shape will be ignored if the input_tensor is provided.
alpha Controls the width of the network. This is known as the width multiplier in the MobileNet paper. - 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. Default to 1.0.
depth_multiplier Depth multiplier for depthwise convolution. This is called the resolution multiplier in the MobileNet paper. Default to 1.0.
dropout Dropout rate. Default to 0.001.
include_top Boolean, whether to include the fully-connected layer at the top of the network. Default to True.
weights One of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Default to imagenet.
input_tensor Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model. input_tensor is useful for sharing inputs between multiple different networks. Default to None.
pooling Optional pooling mode for feature extraction when include_top is False.

  • None (default) means that the output of the model will be the 4D tensor output of the last convolutional block.
  • avg means that global average pooling will be applied to the output of the last convolutional block, 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. Defaults to 1000.
classifier_activation A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer.
**kwargs For backwards compatibility only.

A keras.Model instance.

ValueError in case of invalid argument for weights, or invalid input shape.
ValueError if classifier_activation is not softmax or None when using a pretrained top layer.