Update tiled net.

Author: ameritusweb

examples/gravnet/ParallelReverseAutoDiff.GravNetExample/TiledNet.cs

@@ -81,7 +81,7 @@ public void AdjustLearningRate(double learningRate)
         /// <returns>The task.</returns>
         public async Task Initialize()
         {
-            var initialAdamIteration = 1;
+            var initialAdamIteration = 462;
             var model = new TiledNetwork.TiledNetwork(this.numLayers, this.numNodes, this.numFeatures, this.learningRate, this.clipValue, "tilednet");
             model.Parameters.AdamIteration = initialAdamIteration;
             this.TiledNetwork = model;
@@ -122,7 +122,7 @@ public void SaveWeights()
         /// </summary>
         public void ApplyWeights()
         {
-            var guid = "tiled_05842a36-7e13-4ba8-a981-df0c5a2b50c5_11";
+            var guid = "tiled_0a7edd07-8034-4d91-b7fd-68fb84a22a48_462";
             var dir = $"E:\\vnnstore\\{guid}";
             for (int i = 0; i < this.modelLayers.Count; ++i)
             {

examples/gravnet/ParallelReverseAutoDiff.GravNetExample/TiledNetTrainer.cs

@@ -14,7 +14,7 @@ public async Task Train()
                 CudaBlas.Instance.Initialize();
                 TiledNet net = new TiledNet(512, 6144, 3, 0.01d, 4d);
                 await net.Initialize();
-                //net.ApplyWeights();
+                net.ApplyWeights();
 
                 var pngFiles = Directory.GetFiles(@"E:\images\inputs\svg", "*.png");
 
@@ -106,18 +106,20 @@ await files.WithRepeatAsync(async (pngFile, token) =>
                     //}
 
 
-                    //Console.WriteLine($"Iteration {i} Output X: {output[0, 0]}, Output Y: {output[0, 1]}, Grad: {gradient[0, 0]}, {gradient[0, 1]}");
-                    //Console.WriteLine($"Loss: {loss[0, 0]}, Perc: {perc}");
+                    Console.WriteLine($"Iteration {i}");
+                    Console.WriteLine($"Loss: {lossAndGradient[0, 0].Item1[0, 0]}, {lossAndGradient[0, 1].Item1[0, 0]}, {lossAndGradient[0, 2].Item1[0, 0]}");
+                    Console.WriteLine($"Loss: {lossAndGradient[1, 0].Item1[0, 0]}, {lossAndGradient[1, 1].Item1[0, 0]}, {lossAndGradient[1, 2].Item1[0, 0]}");
+                    Console.WriteLine($"Loss: {lossAndGradient[2, 0].Item1[0, 0]}, {lossAndGradient[2, 1].Item1[0, 0]}, {lossAndGradient[2, 2].Item1[0, 0]}");
                     //Console.WriteLine($"O1 X: {o1[0, 0]}, O1 Y: {o1[0, 1]}, Loss: {loss[0, 0]}, {loss0[0, 0]}, {loss1[0, 0]}");
-                    await net.Backward(lossAndGradient);
-                    net.ApplyGradients();
+                    //await net.Backward(lossAndGradient);
+                    //net.ApplyGradients();
                     //}
 
                     await net.Reset();
                     Thread.Sleep(1000);
                     if (i % 11 == 10)
                     {
-                        net.SaveWeights();
+                        //net.SaveWeights();
                     }
 
                     //if (token.UsageCount == 0)