Upsampling layer for 2D inputs.

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

[[0]: R:[0 [[1]: R:[1

layer_upsampling_2d(object, size = c(2L, 2L), data_format = NULL,
  batch_size = NULL, name = NULL, trainable = NULL, weights = NULL)

Arguments

object

Model or layer object
size

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

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".
batch_size

Fixed batch size for layer
name

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.
trainable

Whether the layer weights will be updated during training.
weights

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

Other convolutional layers: layer_conv_1d, layer_conv_2d_transpose, layer_conv_2d, layer_conv_3d_transpose, layer_conv_3d, layer_conv_lstm_2d, layer_cropping_1d, layer_cropping_2d, layer_cropping_3d, layer_depthwise_conv_2d, layer_separable_conv_1d, layer_separable_conv_2d, layer_upsampling_1d, layer_upsampling_3d, layer_zero_padding_1d, layer_zero_padding_2d, layer_zero_padding_3d