MNIST CNN

Author: Oceania2018

test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionCNN.cs

@@ -36,17 +36,34 @@ public class DigitRecognitionCNN : IExample
 
         public string Name => "MNIST CNN";
 
-        const int img_h = 28;
-        const int img_w = 28;
+        string logs_path = "logs";
+
+        const int img_h = 28, img_w = 28; // MNIST images are 28x28
         int img_size_flat = img_h * img_w; // 784, the total number of pixels
         int n_classes = 10; // Number of classes, one class per digit
+        int n_channels = 1;
+
         // Hyper-parameters
         int epochs = 10;
         int batch_size = 100;
         float learning_rate = 0.001f;
-        int h1 = 200; // number of nodes in the 1st hidden layer
         Datasets<DataSetMnist> mnist;
 
+        // Network configuration
+        // 1st Convolutional Layer
+        int filter_size1 = 5;  // Convolution filters are 5 x 5 pixels.
+        int num_filters1 = 16; //  There are 16 of these filters.
+        int stride1 = 1;  // The stride of the sliding window
+
+        // 2nd Convolutional Layer
+        int filter_size2 = 5; // Convolution filters are 5 x 5 pixels.
+        int num_filters2 = 32;// There are 32 of these filters.
+        int stride2 = 1;  // The stride of the sliding window
+
+        // Fully-connected layer.
+        int h1 = 128; // Number of neurons in fully-connected layer.
+
+
         Tensor x, y;
         Tensor loss, accuracy;
         Operation optimizer;
@@ -123,6 +140,9 @@ private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = tr
         public void PrepareData()
         {
             mnist = MNIST.read_data_sets("mnist", one_hot: true);
+            print("Size of:");
+            print($"- Training-set:\t\t{len(mnist.train.data)}");
+            print($"- Validation-set:\t{len(mnist.validation.data)}");
         }
 
         public void Train(Session sess)