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)
whether to include the fully-connected layer at the top of the network.
NULL (random initialization), imagenet (ImageNet
weights), or the path to the weights file to be loaded.
optional Keras tensor to use as image input for the model.
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 75. E.g. (150, 150, 3) would be one valid value.
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.
optional number of classes to classify images into, only to be
specified if include_top is TRUE, and if no weights argument is
specified.
Input tensor for preprocessing
A Keras model instance.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(http://arxiv.org/abs/1512.00567)
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.