public class Upsampling2D extends BaseUpsamplingLayer
[minibatch, channels, height, width]
then
output has shape [minibatch, channels, height*size[0], width*size[1]]
Input (slice for one example and channel) [ A, B ] [ C, D ] Size = [2, 2] Output (slice for one example and channel) [ A, A, B, B ] [ A, A, B, B ] [ C, C, D, D ] [ C, C, D, D ]
Modifier and Type | Class and Description |
---|---|
static class |
Upsampling2D.Builder |
BaseUpsamplingLayer.UpsamplingBuilder<T extends BaseUpsamplingLayer.UpsamplingBuilder<T>>
Modifier and Type | Field and Description |
---|---|
protected CNN2DFormat |
format |
protected int[] |
size |
constraints, iDropout, layerName
Modifier | Constructor and Description |
---|---|
protected |
Upsampling2D(BaseUpsamplingLayer.UpsamplingBuilder builder) |
Modifier and Type | Method and Description |
---|---|
Upsampling2D |
clone() |
LayerMemoryReport |
getMemoryReport(InputType inputType)
This is a report of the estimated memory consumption for the given layer
|
InputType |
getOutputType(int layerIndex,
InputType inputType)
For a given type of input to this layer, what is the type of the output?
|
InputPreProcessor |
getPreProcessorForInputType(InputType inputType)
For the given type of input to this layer, what preprocessor (if any) is required?
|
Layer |
instantiate(NeuralNetConfiguration conf,
Collection<TrainingListener> trainingListeners,
int layerIndex,
INDArray layerParamsView,
boolean initializeParams,
DataType networkDataType) |
void |
setNIn(InputType inputType,
boolean override)
Set the nIn value (number of inputs, or input channels for CNNs) based on the given input
type
|
getGradientNormalization, getGradientNormalizationThreshold, getRegularizationByParam, initializer, isPretrainParam
getUpdaterByParam, initializeConstraints, resetLayerDefaultConfig, setDataType
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
getLayerName
protected int[] size
protected CNN2DFormat format
protected Upsampling2D(BaseUpsamplingLayer.UpsamplingBuilder builder)
public Upsampling2D clone()
clone
in class BaseUpsamplingLayer
public Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType)
instantiate
in class Layer
public InputType getOutputType(int layerIndex, InputType inputType)
Layer
getOutputType
in class Layer
layerIndex
- Index of the layerinputType
- Type of input for the layerpublic InputPreProcessor getPreProcessorForInputType(InputType inputType)
Layer
InputPreProcessor
for this layer, such as a CnnToFeedForwardPreProcessor
getPreProcessorForInputType
in class BaseUpsamplingLayer
inputType
- InputType to this layerpublic LayerMemoryReport getMemoryReport(InputType inputType)
Layer
getMemoryReport
in class Layer
inputType
- Input type to the layer. Memory consumption is often a function of the input
typepublic void setNIn(InputType inputType, boolean override)
Layer
setNIn
in class NoParamLayer
inputType
- Input type for this layeroverride
- If false: only set the nIn value if it's not already set. If true: set it
regardless of whether it's already set or not.Copyright © 2020. All rights reserved.