tf.keras.applications.ResNet101V2

Instantiates the ResNet101V2 architecture.

tf.keras.applications.ResNet101V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'
)

Reference:

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.

include_top whether to include the fully-connected layer 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. When loading pretrained weights, classifier_activation can only be None or "softmax".

A Model instance.