Class FlattenLayer

java.lang.Object

deepnetts.net.layers.AbstractLayer<TensorBase, TensorBase, TensorBase>

deepnetts.net.layers.FlattenLayer

All Implemented Interfaces:

Backward, Forward, Layer<TensorBase>, Serializable

public class FlattenLayer
extends AbstractLayer<TensorBase, TensorBase, TensorBase>

Transforms outputs from previous 3D layer into a flatten 1D tensor in forward pass, Backward pass propagates weighted errors/deltas from the next fully connected layer. Automatically added after 2D or 3D layer to transition to fully connected layers.

See Also:

• Serialized Form

Field Summary

Fields inherited from class AbstractLayer

activation, activationType, backwardImpl, batchMode, batchSize, biases, cudaHandles, deltaBiases, deltas, deltaWeights, depth, forwardImpl, gradients, height, inputs, learningRate, mode, momentum, networkType, nextLayer, numThreads, optimizer, optimizerType, outputs, prevDeltaBiases, prevDeltaWeights, prevLayer, randomWeightsType, regL1, regL2, threadPool, trainable, weights, width

Constructor Summary

Constructors

Constructor	Description
FlattenLayer()

Method Summary

Modifier and Type	Method	Description
void	applyWeightChanges()	Applies weight changes to current weights Must be diferent for convolutional does nothing for MaxPooling Same for FullyConnected and OutputLayer
void	backward()	This method should implement backward pass in subclasses
void	forward()	This method should implement forward pass in subclasses
void	init()	This method should implement layer initialization in subclasses, when a layer is added to the network (create weights, outputs, deltas, randomization etc.).
void	initTransientFields()
void	setOptimizerType(OptimizerType optType)
String	toString()

Methods inherited from class AbstractLayer

getActivation, getActivationType, getBackwardAcc, getBatchSize, getBiases, getDeltaBiases, getDeltas, getDeltaWeights, getDepth, getForwardAcc, getGradients, getHeight, getL1Regularization, getL1WeightSum, getL2Regularization, getL2WeightSum, getLearningRate, getMode, getMomentum, getNetworkType, getNextLayer, getNumThreads, getOptimizer, getOptimizerType, getOutputs, getPrevDeltaBiases, getPrevDeltaWeights, getPrevlayer, getWeights, getWidth, isBatchMode, isTrainable, setActivationType, setBatchMode, setBatchSize, setBiases, setCudaHandles, setDeltas, setL1Regularization, setL2Regularization, setLearningRate, setMode, setMomentum, setNetworkType, setNextlayer, setOutputs, setPrevDeltaWeights, setPrevLayer, setThreadPool, setTrainable, setWeights, setWeights

Methods inherited from class Object

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

Constructor Details

FlattenLayer

public FlattenLayer()

Method Details

init

public void init()

Description copied from class: AbstractLayer

This method should implement layer initialization in subclasses, when a layer is added to the network (create weights, outputs, deltas, randomization etc.).

Specified by:

init in class AbstractLayer<TensorBase, TensorBase, TensorBase>

initTransientFields

public void initTransientFields()

Overrides:

initTransientFields in class AbstractLayer<TensorBase, TensorBase, TensorBase>

forward

public void forward()

Description copied from class: AbstractLayer

This method should implement forward pass in subclasses

Specified by:

forward in interface Forward

Specified by:

forward in interface Layer<TensorBase>

Specified by:

forward in class AbstractLayer<TensorBase, TensorBase, TensorBase>

backward

public void backward()

Description copied from class: AbstractLayer

This method should implement backward pass in subclasses

Specified by:

backward in interface Backward

Specified by:

backward in interface Layer<TensorBase>

Specified by:

backward in class AbstractLayer<TensorBase, TensorBase, TensorBase>

setOptimizerType

public void setOptimizerType(OptimizerType optType)

Overrides:

setOptimizerType in class AbstractLayer<TensorBase, TensorBase, TensorBase>

applyWeightChanges

public void applyWeightChanges()

Description copied from class: AbstractLayer

Applies weight changes to current weights Must be diferent for convolutional does nothing for MaxPooling Same for FullyConnected and OutputLayer

Specified by:

applyWeightChanges in class AbstractLayer<TensorBase, TensorBase, TensorBase>

toString

public String toString()

Overrides:

toString in class Object