rxNeuralNetmodel supports GPU acceleration. To enable GPU acceleration, you need to do a few things:
system.file("mxLibs/x64", package = "MicrosoftML").
rxNeuralNetmodels for binary classification. One with GPU acceleration and the other without. The parameter
miniBatchSizeis the number of training examples used to take a step in stochastic gradient descent. The bigger it is, the faster progress is made. But large step sizes can lead to difficulty for the algorithm to converge. We used ADADELTA here as our optimizer here as it can automatically adjust the learning rate.
miniBatchSizeis only used with GPU acceleration. Without GPU acceleration, it's by default set to 1.
rxNeuralNetthe number of nodes in the hidden layer defaults to 100. For a single hidden layer, it's usually recommended to be between the number of nodes in the input layer and number of nodes in the output layer. MicrosoftML also supports user-defined network architectures like convolution neural network s using the NET# language.
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