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딥러닝 사용자 지정 훈련 루프

딥러닝 훈련 루프 및 손실 함수 사용자 지정

trainingOptions 함수가 작업에 필요한 훈련 옵션을 제공하지 않거나 사용자 지정한 출력 계층이 필요한 손실 함수를 지원하지 않을 경우에는 사용자 지정 훈련 루프를 정의할 수 있습니다. 계층 그래프를 사용하여 만들 수 없는 신경망의 경우 사용자 지정 신경망을 함수로 정의할 수 있습니다. 자세한 내용은 사용자 지정 훈련 루프, 손실 함수 및 신경망 정의 항목을 참조하십시오.


모두 확장

dlnetworkDeep learning network for custom training loops
resetState신경망의 상태 파라미터 재설정
plot신경망 아키텍처 플로팅
addInputLayerAdd input layer to network
addLayersAdd layers to layer graph or network
removeLayers계층 그래프 또는 신경망에서 계층 제거
connectLayersConnect layers in layer graph or network
disconnectLayers계층 그래프 또는 신경망에서 계층 연결 끊기
replaceLayerReplace layer in layer graph or network
summaryPrint network summary
initializeInitialize learnable and state parameters of a dlnetwork
networkDataLayoutDeep learning network data layout for learnable parameter initialization
forwardCompute deep learning network output for training
predictCompute deep learning network output for inference
adamupdateUpdate parameters using adaptive moment estimation (Adam)
rmspropupdate Update parameters using root mean squared propagation (RMSProp)
sgdmupdate Update parameters using stochastic gradient descent with momentum (SGDM)
dlupdate Update parameters using custom function
minibatchqueueCreate mini-batches for deep learning
onehotencodeEncode data labels into one-hot vectors
onehotdecodeDecode probability vectors into class labels
padsequencesPad or truncate sequence data to same length
trainingProgressMonitorMonitor and plot training progress for deep learning custom training loops
dlarray사용자 지정 훈련 루프를 위한 딥러닝 배열
dlgradientCompute gradients for custom training loops using automatic differentiation
dlfevalEvaluate deep learning model for custom training loops
dimsdlarray의 차원 레이블
finddimFind dimensions with specified label
stripdimsRemove dlarray data format
extractdatadlarray에서 데이터 추출
isdlarrayCheck if object is dlarray
functionToLayerGraphConvert deep learning model function to a layer graph
dlconvDeep learning convolution
dltranspconvDeep learning transposed convolution
lstmLong short-term memory
gruGated recurrent unit
attentionDot-product attention
embedEmbed discrete data
fullyconnectSum all weighted input data and apply a bias
dlode45Deep learning solution of nonstiff ordinary differential equation (ODE)
reluReLU(Rectified Linear Unit) 활성화 적용
leakyreluApply leaky rectified linear unit activation
geluApply Gaussian error linear unit (GELU) activation
batchnormNormalize data across all observations for each channel independently
crosschannelnormCross channel square-normalize using local responses
groupnormNormalize data across grouped subsets of channels for each observation independently
instancenormNormalize across each channel for each observation independently
layernormNormalize data across all channels for each observation independently
avgpoolPool data to average values over spatial dimensions
maxpoolPool data to maximum value
maxunpoolUnpool the output of a maximum pooling operation
softmaxApply softmax activation to channel dimension
sigmoid시그모이드 활성화 적용
sigmoid시그모이드 활성화 적용
crossentropyCross-entropy loss for classification tasks
l1lossL1 loss for regression tasks
l2lossL2 loss for regression tasks
huberHuber loss for regression tasks
mseHalf mean squared error
ctcConnectionist temporal classification (CTC) loss for unaligned sequence classification
dlaccelerateAccelerate deep learning function for custom training loops
AcceleratedFunctionAccelerated deep learning function
clearCacheClear accelerated deep learning function trace cache

도움말 항목

사용자 지정 훈련 루프

모델 함수

자동 미분

딥러닝 함수 가속