Instantiates the ResNet101V2 architecture.
include_top=True, weights='imagenet', input_tensor=None,
input_shape=None, pooling=None, classes=1000,
For image classification use cases, see
this page for detailed examples.
For transfer learning use cases, make sure to read the
guide to transfer learning & fine-tuning.
whether to include the fully-connected
layer at the top of the network.
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
to use as image input for the model.
optional shape tuple, only to be specified
include_top is False (otherwise the input shape
has to be
(224, 224, 3) (with
'channels_last' data format)
(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.
(200, 200, 3) would be one valid value.
Optional pooling mode for feature extraction
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
weights argument is specified.
str or callable. The activation function to use
on the "top" layer. Ignored unless
classifier_activation=None to return the logits of the "top" layer.
When loading pretrained weights,
classifier_activation can only