TensorFlow 1 version View source on GitHub

Instantiates the VGG19 architecture.

    include_top=True, weights='imagenet', input_tensor=None, input_shape=None,
    pooling=None, classes=1000, classifier_activation='softmax'

Used in the notebooks

Used in the guide Used in the tutorials

By default, it loads weights pre-trained on ImageNet. Check 'weights' for other options.

This model can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 224x224.


  • include_top: whether to include the 3 fully-connected layers 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 (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 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 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.
  • 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.
  • classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer.


A keras.Model instance.


  • ValueError: in case of invalid argument for weights, or invalid input shape.
  • ValueError: if classifier_activation is not softmax or None when using a pretrained top layer.