Boolean, whether to include the fully-connected
layer at the top, as the last layer 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 imagenet.
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 shape tuple, only to be specified
if include_top is False (otherwise the input shape
has to be (299, 299, 3) (with channels_last data format)
or (3, 299, 299) (with channels_first data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 75.
E.g. (150, 150, 3) would be one valid value.
input_shape will be ignored if the input_tensor is provided.
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.
optional number of classes to classify images
into, only to be specified if include_top is True, and
if no weights argument is specified. Default 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.