Merge pull request #156 from hello-fri-end/VAE

Fixed segfault of convolutional vae

mnist_vae_cnn/mnist_vae_cnn.cpp

@@ -60,9 +60,18 @@ int main()
   // Entire dataset(without labels) is loaded from a CSV file.
   // Each column represents a data point.
   arma::mat fullData;
-  data::Load("../data/mnist_train.csv", fullData, true, false);
+  data::Load("../data/mnist_train.csv", fullData, true, true);
+
+  // Originally on Kaggle dataset CSV file has header, so it's necessary to
+  // get rid of this row, in Armadillo representation it's the first column.
+  fullData =
+      fullData.submat(0, 1, fullData.n_rows -1, fullData.n_cols -1);
   fullData /= 255.0;
 
+  // Get rid of the labels
+  fullData = 
+      fullData.submat(1, 0, fullData.n_rows - 1, fullData.n_cols -1);
+
   if (isBinary)
   {
     fullData = arma::conv_to<arma::mat>::from(
@@ -76,10 +85,6 @@ int main()
   arma::mat train, validation;
   data::Split(fullData, validation, train, trainRatio);
 
-  // Loss is calculated on train_test data after each cycle.
-  arma::mat train_test, dump;
-  data::Split(train, dump, train_test, 0.045);
-
   /**
    * Model architecture:
    *
@@ -171,10 +176,13 @@ int main()
         0,   // Padding width.
         0,   // Padding height.
         10,  // Input width.
-        10); // Input height.
+        10,  // Input height.
+        14,  // Output width.
+        14); // Output height.
 
     decoder->Add<LeakyReLU<>>();
-    decoder->Add<TransposedConvolution<>>(16, 1, 15, 15, 1, 1, 1, 1, 14, 14);
+    decoder->Add<TransposedConvolution<>>
+        (16, 1, 15, 15, 1, 1, 0, 0, 14, 14, 28, 28);
 
     vaeModel.Add(decoder);
   }
@@ -193,9 +201,6 @@ int main()
       1e-8,         // Tolerance.
       true);
 
-  std::cout << "Initial loss -> "
-            << MeanTestLoss<MeanSModel>(vaeModel, train_test, 50) << std::endl;
-
   // Train neural network. If this is the first iteration, weights are
   // random, using current values as starting point otherwise.
   vaeModel.Train(train,