Repeats the rows and columns of the data by size[[0]] and size[[1]] respectively.

  size = c(2L, 2L),
  data_format = NULL,
  interpolation = "nearest",
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL



What to call the new Layer instance with. Typically a keras Model, another Layer, or a tf.Tensor/KerasTensor. If object is missing, the Layer instance is returned, otherwise, layer(object) is returned.


int, or list of 2 integers. The upsampling factors for rows and columns.


A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".


A string, one of nearest or bilinear. Note that CNTK does not support yet the bilinear upscaling and that with Theano, only size=(2, 2) is possible.


Fixed batch size for layer


An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.


Whether the layer weights will be updated during training.


Initial weights for layer.

Input shape

4D tensor with shape:

  • If data_format is "channels_last": (batch, rows, cols, channels)

  • If data_format is "channels_first": (batch, channels, rows, cols)

Output shape

4D tensor with shape:

  • If data_format is "channels_last": (batch, upsampled_rows, upsampled_cols, channels)

  • If data_format is "channels_first": (batch, channels, upsampled_rows, upsampled_cols)

See also