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]]]]

Args
`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)`

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)`

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)

Attributes
`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.

Attributes

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 a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

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

Returns
A layer instance.

`symbolic_call`