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.
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 for depthwise convolution. This is
called the resolution multiplier in the MobileNet paper. Default to 1.0.
Dropout rate. Default to 0.001.
Boolean, whether to include the fully-connected layer at the
top of the network. Default to True.
One of None (random initialization), 'imagenet' (pre-training
on ImageNet), or the path to the weights file to be loaded. Default to
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.
Optional pooling mode for feature extraction when include_top
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.
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.
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.