ai.djl.nn.norm

Class Dropout

java.lang.Object

ai.djl.nn.AbstractBlock

ai.djl.nn.ParameterBlock

ai.djl.nn.norm.Dropout

All Implemented Interfaces:

Block

public class Dropout
extends ParameterBlock

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

inputNames, inputShapes

Method Summary

Modifier and Type	Method and Description
static Dropout.Builder	builder() Creates a builder to build a Dropout.
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.
java.util.List<Parameter>	getDirectParameters() Returns a list of all the direct parameters of the block.
Shape[]	getOutputShapes(NDManager manager, Shape[] inputShapes) Returns the expected output shapes of the block for the specified input shapes.
Shape	getParameterShape(java.lang.String name, Shape[] inputShapes) Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
void	loadParameters(NDManager manager, java.io.DataInputStream is) Loads the parameters from the given input stream.
void	saveParameters(java.io.DataOutputStream os) Writes the parameters of the block to the given outputStream.

Methods inherited from class ai.djl.nn.ParameterBlock

getChildren, initialize, toString

Methods inherited from class ai.djl.nn.AbstractBlock

beforeInitialize, cast, clear, describeInput, getParameters, isInitialized, readInputShapes, saveInputShapes, 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, 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

getDirectParameters

public java.util.List<Parameter> getDirectParameters()

Returns a list of all the direct parameters of the block.

Returns:

the list of Parameter

getParameterShape

public Shape getParameterShape(java.lang.String name,
                               Shape[] inputShapes)

Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.

Parameters:

name - the name of the parameter

inputShapes - the shapes of the input to the block

Returns:

the shape of the parameter specified

saveParameters

public void saveParameters(java.io.DataOutputStream os)
                    throws java.io.IOException

Writes the parameters of the block to the given outputStream.

Parameters:

os - the outputstream to save the parameters to

Throws:

java.io.IOException - if an I/O error occurs

loadParameters

public void loadParameters(NDManager manager,
                           java.io.DataInputStream is)
                    throws java.io.IOException,
                           MalformedModelException

Loads the parameters from the given input stream.

Parameters:

manager - an NDManager to create the parameter arrays

is - the inputstream that stream the parameter values

Throws:

java.io.IOException - if an I/O error occurs

MalformedModelException - if the model file is corrupted or unsupported

builder

public static Dropout.Builder builder()

Creates a builder to build a Dropout.

Returns:

a new builder