public void MNISTTwoHiddenLayerNetworkGPUTest()
{
// Parameters
var learningRate = 0.1f;
var epochs = 5;
var numGPUs = 4;
var mnist = new Mnist();
mnist.ReadDataSets("/tmp");
int batchSize = 400;
int numBatches = mnist.TrainImages.Length / batchSize;
using (var graph = new TFGraph())
{
var X = graph.Placeholder(TFDataType.Float, new TFShape(-1, 784));
var Y = graph.Placeholder(TFDataType.Float, new TFShape(-1, 10));
var Xs = graph.Split(graph.Const(0), X, numGPUs);
var Ys = graph.Split(graph.Const(0), Y, numGPUs);
var sgd = new SGD(graph, learningRate, 0.9f);
TFOutput[] costs = new TFOutput[numGPUs];
TFOutput[] accuracys = new TFOutput[numGPUs];
var variablesAndGradients = new Dictionary <Variable, List <TFOutput> >();
for (int i = 0; i < numGPUs; i++)
{
using (var device = graph.WithDevice("/GPU:" + i))
{
(costs[i], _, accuracys[i]) = CreateNetwork(graph, Xs[i], Ys[i], variablesAndGradients);
foreach (var gv in sgd.ComputeGradient(costs[i], colocateGradientsWithOps: true))
{
if (!variablesAndGradients.ContainsKey(gv.variable))
{
variablesAndGradients[gv.variable] = new List <TFOutput>();
}
variablesAndGradients[gv.variable].Add(gv.gradient);
}
}
}
var cost = graph.ReduceMean(graph.Stack(costs));
var accuracy = graph.ReduceMean(graph.Stack(accuracys));
var gradientsAndVariables = new (TFOutput gradient, Variable variable)[variablesAndGradients.Count];
