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TensorFlow 1 version
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View source on GitHub
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Upsampling layer for 3D inputs.
tf.keras.layers.UpSampling3D(
size=(2, 2, 2), data_format=None, **kwargs
)
Repeats the 1st, 2nd and 3rd dimensions
of the data by size[0], size[1] and size[2] respectively.
Examples:
input_shape = (2, 1, 2, 1, 3)x = tf.constant(1, shape=input_shape)y = tf.keras.layers.UpSampling3D(size=2)(x)print(y.shape)(2, 2, 4, 2, 3)
Arguments | |
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size
|
Int, or tuple of 3 integers. The upsampling factors for dim1, dim2 and dim3. |
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, spatial_dim1, spatial_dim2, spatial_dim3, channels)
while channels_first corresponds to inputs with shape
(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3).
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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Input shape:
5D tensor with shape:
- If
data_formatis"channels_last":(batch_size, dim1, dim2, dim3, channels) - If
data_formatis"channels_first":(batch_size, channels, dim1, dim2, dim3)
Output shape:
5D tensor with shape:
- If
data_formatis"channels_last":(batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels) - If
data_formatis"channels_first":(batch_size, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)