Inception V3 model, with weights pre-trained on ImageNet.

application_inception_v3(include_top = TRUE, weights = "imagenet",
  input_tensor = NULL, input_shape = NULL, pooling = NULL,
  classes = 1000)

inception_v3_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_v3_preprocess_input() function should be used for image preprocessing.

Reference