({{, (RefLink[LearningRate, paclet:ref/LearningRate]) 
is an option for (RefLink[NetTrain, paclet:ref/NetTrain]) that specifies the rate at which to adjust neural net weights in order to minimize the training loss.}})

OpenersNotesSection(Spacer1)Details

(RefLink[LearningRate, paclet:ref/LearningRate])->(RefLink[Automatic, paclet:ref/Automatic]) specifies that (RefLink[NetTrain, paclet:ref/NetTrain]) should choose a learning rate automatically.

(RefLink[LearningRate, paclet:ref/LearningRate])->r specifies that the learning rate should be the number r.

The base learning rate for all learned weights is established using (RefLink[LearningRate, paclet:ref/LearningRate]). Use (RefLink[LearningRateMultipliers, paclet:ref/LearningRateMultipliers]) to modify this rate for particular parts of the net.

Typical learning rates are numbers much smaller than 1; values of between 0.001 and 0.01 are common. Choosing a learning rate that is too high can cause the net to fail to converge to a good solution. Choosing a learning rate that is too low will cause the training process to take longer than necessary.

Learning rates are usually not comparable across different optimizers specified via the (RefLink[Method, paclet:ref/Method]) option of (RefLink[NetTrain, paclet:ref/NetTrain]).

OpenersPrimaryExamplesSection(Spacer1)Examples  (1)

OpenersExampleSection0(Spacer1)Basic Examples  (1)

Train a network with a learning rate of 0.01:

NetTrain[NetModel["LeNet"],"MNIST",LearningRate->0.01]

({{DocumentationFooterIconsRelatedFunction.png, ({{See Also}, {(RefLink[NetTrain, paclet:ref/NetTrain])  ▪  (RefLink[LearningRateMultipliers, paclet:ref/LearningRateMultipliers])}})}})

({{DocumentationFooterIconsRelatedGuide.png, ({{Related Guides}, {▪(RefLinkPlain[Neural Networks, paclet:guide/NeuralNetworks])}})}})