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タスクに必要な学習オプションが関数 trainingOptions に用意されていない場合、または必要な損失関数をカスタム出力層がサポートしていない場合、カスタム学習ループを定義できます。層グラフを使用して作成できないネットワークの場合、カスタム ネットワークを関数として定義できます。詳細については、カスタム学習ループ、損失関数、およびネットワークの定義を参照してください。



dlnetworkDeep learning network for custom training loops
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
initializeInitialize learnable and state parameters of a dlnetwork
resetStateニューラル ネットワークの状態パラメーターのリセット
dlarrayDeep learning array for custom training loops
dlgradientCompute gradients for custom training loops using automatic differentiation
dlfevalEvaluate deep learning model for custom training loops
dimsdlarray の次元ラベル
stripdimsdlarray データ形式の削除
extractdatadlarray からのデータの抽出
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
embedEmbed discrete data
fullyconnectSum all weighted input data and apply a bias
dlode45Deep learning solution of nonstiff ordinary differential equation (ODE)
reluApply rectified linear unit activation
leakyrelu漏洩 (leaky) 正規化線形ユニット活性化の適用
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
softmaxApply softmax activation to channel dimension
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