Whether to include the fully-connected
layer at the top of the network. Defaults to True.
One of None (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded. Defaults to 'imagenet'.
Optional Keras tensor
(i.e. output of layers.Input())
to use as image input for the model.
Optional shape tuple, only to be specified
if include_top is False.
It should have exactly 3 inputs channels.
Optional pooling mode for feature extraction
when include_top is False. Defaults to None.
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
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 (number of
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.
Defaults to 'softmax'.
When loading pretrained weights, classifier_activation can only
be None or "softmax".