Instantiates the Inception-ResNet v2 architecture.
application_inception_resnet_v2(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
name = "inception_resnet_v2"
)A model instance.
whether to include the fully-connected layer at the top of the network.
one of NULL (random initialization),
"imagenet" (pre-training on ImageNet),
or the path to the weights file to be loaded.
optional Keras tensor
(i.e. output of keras_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 (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.
Optional pooling mode for feature extraction
when include_top is FALSE.
NULL 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.
A str or callable.
The activation function to use on the "top" layer.
Ignored unless include_top=TRUE.
Set classifier_activation=NULL to return the logits
of the "top" layer. When loading pretrained weights,
classifier_activation can only be NULL or "softmax".
The name of the model (string).
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
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.