UpSampling2D layer
UpSampling2D class
tf_keras.layers.UpSampling2D(
size=(2, 2), data_format=None, interpolation="nearest", **kwargs
)
Upsampling layer for 2D inputs.
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) orchannels_first. The ordering of the dimensions in the inputs.channels_lastcorresponds to inputs with shape(batch_size, height, width, channels)whilechannels_firstcorresponds to inputs with shape(batch_size, channels, height, width). When unspecified, usesimage_data_formatvalue found in your TF-Keras config file at~/.keras/keras.json(if exists) else 'channels_last'. Defaults to 'channels_last'. - interpolation: A string, one of
"area","bicubic","bilinear","gaussian","lanczos3","lanczos5","mitchellcubic","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)
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)