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Instantiates the VGG16 model.


  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax"



whether to include the 3 fully-connected layers at the top of the network.


one of NULL (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.


optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.


optional shape tuple, only to be specified if include_top is FALSE (otherwise the input shape has to be (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 input channels, and width and height should be no smaller than 32. E.g. (200, 200, 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 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.


optional number of classes to classify images into, only to be specified if include_top is TRUE, and if no weights argument is specified.


A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be NULL or "softmax".


A model instance.


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.

The default input size for this model is 224x224.


Each Keras Application expects a specific kind of input preprocessing. For VGG16, call application_preprocess_inputs() on your inputs before passing them to the model. application_preprocess_inputs() will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.