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TensorFlow 1 version
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View source on GitHub
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Upsampling layer for 2D inputs.
tf.keras.layers.UpSampling2D(
size=(2, 2), data_format=None, interpolation='nearest', **kwargs
)
Used in the notebooks
| Used in the guide |
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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 | |
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size
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Int, or tuple of 2 integers. The upsampling factors for rows and columns. |
data_format
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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".
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interpolation
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A string, one of nearest or bilinear.
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Input shape:
4D tensor with shape:
- If
data_formatis"channels_last":(batch_size, rows, cols, channels) - If
data_formatis"channels_first":(batch_size, channels, rows, cols)
Output shape:
4D tensor with shape:
- If
data_formatis"channels_last":(batch_size, upsampled_rows, upsampled_cols, channels) - If
data_formatis"channels_first":(batch_size, channels, upsampled_rows, upsampled_cols)