tf.keras.applications.inception_resnet_v2.InceptionResNetV2

Instantiates the Inception-ResNet v2 architecture.

View aliases

Main aliases

tf.keras.applications.InceptionResNetV2

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.applications.InceptionResNetV2, tf.compat.v1.keras.applications.inception_resnet_v2.InceptionResNetV2

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:

• Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (AAAI 2017)

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 tf.keras.applications.inception_resnet_v2.preprocess_input on your inputs before passing them to the model. inception_resnet_v2.preprocess_input will scale input pixels between -1 and 1.

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. 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 be applied.
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.