tf.keras.layers.UpSampling2D

Upsampling layer for 2D inputs.

Inherits From: Layer, Module

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

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

Examples:

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 = tf.keras.layers.UpSampling2D(size=(1, 2))(x)
print(y)
tf.Tensor(
  [[[[ 0  1  2]
     [ 0  1  2]]
    [[ 3  4  5]
     [ 3  4  5]]]
   [[[ 6  7  8]
     [ 6  7  8]]
    [[ 9 10 11]
     [ 9 10 11]]]], shape=(2, 2, 2, 3), dtype=int64)

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). 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.

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)