View source on GitHub |
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]]]]
Input shape | |
---|---|
4D tensor with shape:
|
Output shape | |
---|---|
4D tensor with shape:
|
Methods
from_config
@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
symbolic_call(
*args, **kwargs
)