public void Train(ManagedArray input, ManagedArray output, ConvolutionalNeuralNetworkOptions opts) { var temp_input = new ManagedArray(input.x, input.y, opts.BatchSize, false); var temp_output = new ManagedArray(opts.BatchSize, output.y, false); var index_list = new ManagedIntList(opts.Items); for (var epoch = 0; epoch < opts.Epochs; epoch++) { var start = Profiler.now(); if (opts.Shuffle) { ManagedOps.Shuffle(index_list); } var rLVal = 0.0; rL.Clear(); for (var i = 0; i < opts.Items; i += opts.BatchSize) { if (opts.Shuffle) { ManagedOps.Copy3D(temp_input, input, 0, 0, i, index_list); ManagedOps.Copy2D(temp_output, output, i, 0, index_list); } else { ManagedOps.Copy3D(temp_input, input, 0, 0, i); ManagedOps.Copy2D(temp_output, output, i, 0); } FeedForward(temp_input, opts.Pool); BackPropagation(temp_output); ApplyGradients(opts); if (rL.Count == 0) { rL.Add(L); } rLVal = 0.99 * rL[rL.Count - 1] + 0.01 * L; rL.Add(rLVal); } Console.WriteLine("epoch {0}/{1} elapsed time is {2} ms - Error: {3}", (epoch + 1).ToString("D", ManagedMatrix.ci), opts.Epochs.ToString("D", ManagedMatrix.ci), Profiler.Elapsed(start).ToString("D", ManagedMatrix.ci), rLVal.ToString("0.000000", ManagedMatrix.ci)); } ManagedOps.Free(index_list); ManagedOps.Free(temp_input, temp_output); }