tf.keras.layers.UpSampling2D

Upsampling layer for 2D inputs.

Inherits From: Layer, Operation

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

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]]]]

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".

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)

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)

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config A Python dictionary, typically the output of get_config.

Returns
An operation instance.