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.
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at
Note: each Keras Application expects a specific kind of input preprocessing.
For InceptionResNetV2, call
on your inputs before passing them to the model.
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.
weights, or invalid input shape.
Nonewhen using a pretrained top layer.