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 | 
Instantiates the Inception-ResNet v2 architecture.
tf.keras.applications.inception_resnet_v2.InceptionResNetV2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax',
    **kwargs
)
Reference:
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.
Args | |
|---|---|
include_top
 | 
whether to include the fully-connected layer at the top of the network. | 
weights
 | 
one of None (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
 | 
input_tensor
 | 
optional Keras tensor (i.e. output of layers.Input())
to use as image input for the model.
 | 
input_shape
 | 
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.
 | 
pooling
 | 
Optional pooling mode for feature extraction
when include_top is False.
  | 
classes
 | 
optional number of classes to classify images
into, only to be specified if include_top is True, and
if no weights argument is specified.
 | 
classifier_activation
 | 
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".
 | 
**kwargs
 | 
For backwards compatibility only. | 
Returns | |
|---|---|
A keras.Model instance.
 | 
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