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► Keras 3 API documentation / Layers API / Reshaping layers / UpSampling2D layer

UpSampling2D layer

[source]

UpSampling2D class

keras.layers.UpSampling2D(
    size=(2, 2), data_format=None, interpolation="nearest", **kwargs
)

Upsampling layer for 2D inputs.

The implementation uses interpolative resizing, given the resize method (specified by the interpolation argument). Use interpolation=nearest to repeat the rows and columns of the data.

Example

>>> input_shape = (2, 2, 1, 3)
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
>>> print(x)
[[[[ 0  1  2]]
  [[ 3  4  5]]]
 [[[ 6  7  8]]
  [[ 9 10 11]]]]
>>> y = keras.layers.UpSampling2D(size=(1, 2))(x)
>>> print(y)
[[[[ 0  1  2]
   [ 0  1  2]]
  [[ 3  4  5]
   [ 3  4  5]]]
 [[[ 6  7  8]
   [ 6  7  8]]
  [[ 9 10 11]
   [ 9 10 11]]]]

Arguments

  • size: Int, or tuple 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_size, height, width, channels) while "channels_first" corresponds to inputs with shape (batch_size, channels, height, width). When unspecified, uses image_data_format value found in your Keras config file at ~/.keras/keras.json (if exists) else "channels_last". Defaults to "channels_last".
  • interpolation: A string, one of "bicubic", "bilinear", "lanczos3", "lanczos5", "nearest".

Input shape

4D tensor with shape: - If data_format is "channels_last": (batch_size, rows, cols, channels) - If data_format is "channels_first": (batch_size, channels, rows, cols)

Output shape

4D tensor with shape: - If data_format is "channels_last": (batch_size, upsampled_rows, upsampled_cols, channels) - If data_format is "channels_first": (batch_size, channels, upsampled_rows, upsampled_cols)

UpSampling2D layer
UpSampling2D class