tf.keras.layers.UpSampling2D

TensorFlow 1 version View source on GitHub

Upsampling layer for 2D inputs.

Inherits From: Layer

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

Used in the notebooks

Used in the guide

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) 

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). 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)