Package | Description |
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org.deeplearning4j.nn.conf.layers |
Modifier and Type | Class and Description |
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class |
Upsampling1D
Upsampling 1D layer
Repeats each step size times along the temporal/sequence axis (dimension 2)For input shape [minibatch, channels, sequenceLength] output has shape [minibatch, channels, size *
sequenceLength] Example: If input (for a single example, with channels down page, and sequence from left to right) is: [ A1, A2, A3] [ B1, B2, B3] Then output with size = 2 is: [ A1, A1, A2, A2, A3, A3] [ B1, B1, B2, B2, B3, B2] |
class |
Upsampling2D
Upsampling 2D layer
Repeats each value (or rather, set of depth values) in the height and width dimensions by size[0] and size[1] times respectively. |
class |
Upsampling3D
Upsampling 3D layer
Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and size[2] If input has shape [minibatch, channels, depth, height, width] then output has shape [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width] |
Modifier and Type | Method and Description |
---|---|
BaseUpsamplingLayer |
BaseUpsamplingLayer.clone() |
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