whether to include the 3 fully-connected
layers at the top of the network.
one of None (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
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 (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.
Optional pooling mode for feature extraction
when include_top is False.
None 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
optional number of classes to classify images
into, only to be specified if include_top is True, and
if no weights argument is specified.
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.
When loading pretrained weights, classifier_activation can only
be None or "softmax".