tf.keras.applications.InceptionResNetV2

Aliases:

• tf.keras.applications.InceptionResNetV2
• tf.keras.applications.inception_resnet_v2.InceptionResNetV2

tf.keras.applications.InceptionResNetV2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000
)

Defined in tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py.

Instantiates the Inception-ResNet v2 architecture.

Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set "image_data_format": "channels_last" in your Keras config at ~/.keras/keras.json.

The model and the weights are compatible with TensorFlow, Theano and CNTK backends. The data format convention used by the model is the one specified in your Keras config file.

Note that the default input image size for this model is 299x299, instead of 224x224 as in the VGG16 and ResNet models. Also, the input preprocessing function is different (i.e., do not use imagenet_utils.preprocess_input() with this model. Use preprocess_input() defined in this module instead).

Arguments:

• 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 139. 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 layer. - 'avg' means that global average pooling will be applied to the output of the last convolutional layer, 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.

Returns:

A Keras Model instance.

Raises:

• ValueError: in case of invalid argument for weights, or invalid input shape.