tf.keras.applications.InceptionResNetV2( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", **kwargs )
Instantiates the Inception-ResNet v2 architecture.
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.
Note: each Keras Application expects a specific kind of input preprocessing.
For InceptionResNetV2, call
on your inputs before passing them to the model.
will scale input pixels between -1 and 1.
None(random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
layers.Input()) to use as image input for the model.
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.
Nonemeans 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.
True, and if no
weightsargument is specified.
stror callable. The activation function to use on the "top" layer. Ignored unless
classifier_activation=Noneto return the logits of the "top" layer. When loading pretrained weights,
classifier_activationcan only be