layer_upsampling_2d: Upsampling layer for 2D inputs.

Description

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

Usage

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

Arguments

object

What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:

missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.

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".

interpolation

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.

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