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 )
Instantiates the MobileNet architecture.
input_shape: optional shape tuple, only to be specified if
include_topis False (otherwise the input shape has to be
(224, 224, 3)(with
channels_lastdata format) or (3, 224, 224) (with
channels_firstdata 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
Nonemeans that the output of the model will be the 4D tensor output of the last convolutional layer. -
avgmeans 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. -
maxmeans that global max pooling will be applied.
classes: optional number of classes to classify images into, only to be specified if
include_topis True, and if no
weightsargument 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.