ai.djl.nn.norm

Class Dropout

java.lang.Object

ai.djl.nn.AbstractBlock

ai.djl.nn.norm.Dropout

All Implemented Interfaces:

Block

public class Dropout
extends AbstractBlock

A dropout layer benefits a network by allowing some units (neurons), and hence their respective connections, of a network to be randomly and temporarily removed by setting its value to 0 only during training by specified probability \(p\), usually set to 0.5. The use of dropout acts as if multiple networks with different architectures had been trained, and during test/inference, the removed unit's output is multiplied by \(p\) as an approximation of the averaged output of all the possible network architectures for that unit. The implementation of dropout gives state-of-the-art performances for diverse tasks as shown in the proposal's paper, suggesting its general-use capability.

The idea of dropout itself was proposed in 2014, with the purpose of improving the performance of large networks due to co-adaptation, where some connections are stronger and learned more while other connections become weaker and loses their impact on the prediction, resulting in network overfitting. It was also created as an alternative for costly networks, such as large or ensemble networks, by removing several units, hence creating different thinned network architectures and simulates multiple networks within a single network, greatly reducing the computation cost.

Dropout is recommended to be used when one is trying to optimize an overfitting network or when large dataset is available. It is still quite commonly used in many publications due to its generalization capability. However, using dropout may not prevent overfitting due to variation and limited size of the dataset, and it is reported that dropout layer increases training time by 2-3 times since different simulated multiple networks are trained for each iteration, thus resulting in noisy parameter updates.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Dropout.Builder The Builder to construct a Dropout type of Block.

Field Summary

Fields inherited from class ai.djl.nn.AbstractBlock

children, inputNames, inputShapes, parameters, parameterShapeCallbacks, version

Method Summary

Modifier and Type	Method and Description
static Dropout.Builder	builder() Creates a builder to build a Dropout.
static NDList	dropout(NDArray input) Applies Dropout to the input.
static NDList	dropout(NDArray input, float rate) Applies Dropout to the input.
static NDList	dropout(NDArray input, float rate, boolean training) Applies Dropout to the input.
NDList	forward(ParameterStore parameterStore, NDList inputs, boolean training, ai.djl.util.PairList<java.lang.String,java.lang.Object> params) Applies the operating function of the block once.
Shape[]	getOutputShapes(NDManager manager, Shape[] inputShapes) Returns the expected output shapes of the block for the specified input shapes.
void	loadMetadata(byte version, java.io.DataInputStream is) Overwrite this to load additional metadata with the parameter values.
java.lang.String	toString()

Methods inherited from class ai.djl.nn.AbstractBlock

addChildBlock, addParameter, addParameter, addParameter, beforeInitialize, cast, clear, describeInput, getChildren, getDirectParameters, getParameters, getParameterShape, initialize, initializeChildBlocks, isInitialized, loadParameters, readInputShapes, saveInputShapes, saveMetadata, saveParameters, setInitializer, setInitializer

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface ai.djl.nn.Block

forward, forward, validateLayout

Method Detail

forward

public NDList forward(ParameterStore parameterStore,
                      NDList inputs,
                      boolean training,
                      ai.djl.util.PairList<java.lang.String,java.lang.Object> params)

Applies the operating function of the block once. This method should be called only on blocks that are initialized.

Parameters:

parameterStore - the parameter store

inputs - the input NDList

training - true for a training forward pass

params - optional parameters

Returns:

the output of the forward pass

getOutputShapes

public Shape[] getOutputShapes(NDManager manager,
                               Shape[] inputShapes)

Returns the expected output shapes of the block for the specified input shapes.

Parameters:

manager - an NDManager

inputShapes - the shapes of the inputs

Returns:

the expected output shapes of the block

loadMetadata

public void loadMetadata(byte version,
                         java.io.DataInputStream is)
                  throws java.io.IOException,
                         MalformedModelException

Overwrite this to load additional metadata with the parameter values.

If you overwrite AbstractBlock.saveMetadata(DataOutputStream) or need to provide backward compatibility to older binary formats, you prabably need to overwrite this. This default implementation checks if the version number fits, if not it throws an MalformedModelException. After that it restores the input shapes.

Overrides:

loadMetadata in class AbstractBlock

Parameters:

version - the version used for loading this metadata.

is - the input stream we are loading from

Throws:

java.io.IOException - loading failed

MalformedModelException - data can be loaded but has wrong format

toString

public java.lang.String toString()

Overrides:

toString in class AbstractBlock

dropout

public static NDList dropout(NDArray input)

Applies Dropout to the input.

Parameters:

input - input to apply dropout

Returns:

output

dropout

public static NDList dropout(NDArray input,
                             float rate)

Applies Dropout to the input.

Parameters:

input - input to apply dropout

rate - Fraction of the input units to drop

Returns:

output

dropout

public static NDList dropout(NDArray input,
                             float rate,
                             boolean training)

Applies Dropout to the input.

Parameters:

input - input to apply dropout

rate - Fraction of the input units to drop

training - apply dropout if true

Returns:

output

builder

public static Dropout.Builder builder()

Creates a builder to build a Dropout.

Returns:

a new builder