2 files changed, 12 insertions, 8 deletions

diff --git a/src/nn.c b/src/nn.c
index ca04003..83fd77a 100644
--- a/src/nn.c
+++ b/src/nn.c
@@ -3,7 +3,7 @@
 static void fill_random_weights(double *weights, double *bias, size_t rows, size_t cols);
 
 void nn_backward(
-        double **weights,
+        double **weights, double **bias,
         double **Zout, double **Outs,
         double *Input, size_t input_shape[2],
         double *Labels, size_t labels_shape[2],
@@ -33,19 +33,20 @@ void nn_backward(
                 double *zout = Zout[l] + sample * network[l].neurons;
                 double *out_prev = Outs[l - 1] + sample * network[l-1].neurons;
                 nn_layer_out_delta(delta, dcost_out, zout, network[l].neurons, network[l].activation_derivative);
-                nn_layer_backward(weights[l], weigths_shape, delta, out_prev, network[l], alpha);
+                nn_layer_backward(weights[l], bias[l], weigths_shape, delta, out_prev, network[l], alpha);
             } else if (l == 0) {
                 size_t weigths_next_shape[2] = {network[l+1].input_nodes, network[l+1].neurons};
                 double *zout = Zout[l] + sample * network[l].neurons;
                 double *input = Input + sample * input_shape[1];
                 nn_layer_hidden_delta(delta, delta_next, zout, weights[l+1], weigths_next_shape, network[l].activation_derivative);
-                nn_layer_backward(weights[l], weigths_shape, delta, input, network[l], alpha);
+                nn_layer_backward(weights[l], bias[l], weigths_shape, delta, input, network[l], alpha);
+                break;
             } else {
                 size_t weigths_next_shape[2] = {network[l+1].input_nodes, network[l+1].neurons};
                 double *zout = Zout[l] + sample * network[l].neurons;
                 double *out_prev = Outs[l - 1] + sample * network[l-1].neurons;
                 nn_layer_hidden_delta(delta, delta_next, zout, weights[l+1], weigths_next_shape, network[l].activation_derivative);
-                nn_layer_backward(weights[l], weigths_shape, delta, out_prev, network[l], alpha);
+                nn_layer_backward(weights[l], bias[l], weigths_shape, delta, out_prev, network[l], alpha);
             }
             memcpy(delta_next, delta, weigths_shape[1] * sizeof(double));
         }
@@ -57,7 +58,7 @@ void nn_backward(
 }
 
 void nn_layer_backward(
-        double *weights, size_t weigths_shape[2],
+        double *weights, double *bias, size_t weigths_shape[2],
         double *delta, double *out_prev,
         Layer layer, double alpha)
 {
@@ -65,9 +66,12 @@ void nn_layer_backward(
         for (size_t j = 0; j < weigths_shape[1]; j++) {
             size_t index = weigths_shape[1] * i + j;
             double dcost_w = delta[j] * out_prev[i];
-            weights[index] = layer.weights[index] + alpha * dcost_w;
+            weights[index] = layer.weights[index] - alpha * dcost_w;
         }
     }
+
+    for (size_t j = 0; j < weigths_shape[1]; j++)
+        bias[j] = layer.bias[j] - alpha * delta[j];
 }
 
 void nn_layer_hidden_delta(
diff --git a/src/nn.h b/src/nn.h
index c07a943..13b5f7c 100644
--- a/src/nn.h
+++ b/src/nn.h
@@ -37,7 +37,7 @@ void nn_forward(
         Layer network[], size_t network_size);
 
 void nn_backward(
-        double **weights,
+        double **weights, double **bias,
         double **zout, double **outs,
         double *input, size_t input_shape[2],
         double *labels, size_t labels_shape[2],
@@ -51,7 +51,7 @@ void nn_layer_forward(
         double *input, size_t input_shape[2]);
 
 void nn_layer_backward(
-        double *weights, size_t weigths_shape[2],
+        double *weights, double *bias, size_t weigths_shape[2],
         double *delta, double *out_prev,
         Layer layer, double alpha);