Inception-ResNet v2 model, with weights trained on ImageNet

Inception-ResNet v2 model, with weights trained on ImageNet

application_inception_resnet_v2(include_top = TRUE, weights = "imagenet",
  input_tensor = NULL, input_shape = NULL, pooling = NULL,
  classes = 1000)

inception_resnet_v2_preprocess_input(x)

Arguments

include_top

whether to include the fully-connected layer at the top of the network.
weights

NULL (random initialization), imagenet (ImageNet weights), or the path to the weights file to be loaded.
input_tensor

optional Keras tensor to use as image input for the model.
input_shape

optional shape list, only to be specified if include_top is FALSE (otherwise the input shape has to be (299, 299, 3). It should have exactly 3 inputs channels, and width and height should be no smaller than 71. E.g. (150, 150, 3) would be one valid value.
pooling

Optional pooling mode for feature extraction when include_top is FALSE.

  • NULL 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.
x

Input tensor for preprocessing

Value

A Keras model instance.

Details

Do note that the input image format for this model is different than for the VGG16 and ResNet models (299x299 instead of 224x224).

The inception_resnet_v2_preprocess_input() function should be used for image preprocessing.

Reference