1 files changed, 10 insertions, 8 deletions

diff --git a/src/lib/src/train.c b/src/lib/src/train.c
index 027de66..3061a99 100644
--- a/src/lib/src/train.c
+++ b/src/lib/src/train.c
@@ -129,7 +129,7 @@ void nnTrain(
   nnMatrix* errors = calloc(net->num_layers, sizeof(nnMatrix));
 
   // Allocate the weight transpose matrices up front for backpropagation.
-  nnMatrix* weights_T = calloc(net->num_layers, sizeof(nnMatrix));
+  //nnMatrix* weights_T = calloc(net->num_layers, sizeof(nnMatrix));
 
   // Allocate the weight delta matrices.
   nnMatrix* weight_deltas = calloc(net->num_layers, sizeof(nnMatrix));
@@ -143,7 +143,7 @@ void nnTrain(
   nnMatrix* outputs_T = calloc(net->num_layers, sizeof(nnMatrix));
 
   assert(errors != 0);
-  assert(weights_T != 0);
+  //assert(weights_T != 0);
   assert(weight_deltas != 0);
   assert(gradient_elems);
   assert(outputs_T);
@@ -155,8 +155,8 @@ void nnTrain(
 
     errors[l] = nnMatrixMake(1, layer_weights->cols);
 
-    weights_T[l] = nnMatrixMake(layer_weights->cols, layer_weights->rows);
-    nnMatrixTranspose(layer_weights, &weights_T[l]);
+    //weights_T[l] = nnMatrixMake(layer_weights->cols, layer_weights->rows);
+    //nnMatrixTranspose(layer_weights, &weights_T[l]);
 
     weight_deltas[l] = nnMatrixMake(layer_weights->rows, layer_weights->cols);
 
@@ -267,7 +267,9 @@ void nnTrain(
 
         // Backpropagate the error before updating weights.
         if (l > 0) {
-          nnMatrixMul(gradient, &weights_T[l], &errors[l-1]);
+          // G * W^T == G *^T W.
+          //nnMatrixMul(gradient, &weights_T[l], &errors[l-1]);
+          nnMatrixMulRows(gradient, layer_weights, &errors[l-1]);
         }
 
         // Update weights.
@@ -278,7 +280,7 @@ void nnTrain(
         nnMatrixSub(layer_weights, &weight_deltas[l], layer_weights);
 
         // Update weight transpose matrix for the next training iteration.
-        nnMatrixTranspose(layer_weights, &weights_T[l]);
+        //nnMatrixTranspose(layer_weights, &weights_T[l]);
 
         // Update biases.
         // This is the same formula as for weights, except that the o_j term is
@@ -319,7 +321,7 @@ void nnTrain(
   for (int l = 0; l < net->num_layers; ++l) {
     nnMatrixDel(&errors[l]);
     nnMatrixDel(&outputs_T[l]);
-    nnMatrixDel(&weights_T[l]);
+    //nnMatrixDel(&weights_T[l]);
     nnMatrixDel(&weight_deltas[l]);
 
     nnGradientElements* elems = &gradient_elems[l];
@@ -340,7 +342,7 @@ void nnTrain(
   nnMatrixDel(&training_inputs_T);
   free(errors);
   free(outputs_T);
-  free(weights_T);
+  //free(weights_T);
   free(weight_deltas);
   free(gradient_elems);
 }