add: nn_network_predict done

1 files changed, 25 insertions, 1 deletions

diff --git a/src/nn.c b/src/nn.c
index 63dd643..904fffc 100644
--- a/src/nn.c
+++ b/src/nn.c
@@ -14,6 +14,30 @@ struct Cost NN_SQUARE = {
     .dfunc_out = square_dloss_out
 };
 
+void nn_network_predict(
+        double *output, size_t output_shape[2],
+        double *input, size_t input_shape[2],
+        Layer network[], size_t network_size)
+{
+    double **outs = calloc(network_size, sizeof(double *));
+    double **zouts = calloc(network_size, sizeof(double *));
+    size_t samples = input_shape[0];
+    for (size_t l = 0; l < network_size; l++) {
+        outs[l] = calloc(samples * network[l].neurons, sizeof(double));
+        zouts[l] = calloc(samples * network[l].neurons, sizeof(double));
+    }
+
+    nn_forward(outs, zouts, input, input_shape, network, network_size);
+    memmove(output, outs[network_size - 1], samples * output_shape[1] * sizeof(double));
+
+    for (size_t l = 0; l < network_size; l++) {
+        free(outs[l]);
+        free(zouts[l]);
+    }
+    free(outs);
+    free(zouts);
+}
+
 void nn_network_train(
         Layer network[], size_t network_size,
         double *input, size_t input_shape[2],
@@ -49,7 +73,7 @@ void nn_network_train(
                 network, network_size,
                 cost.dfunc_out, alpha);
         double *net_out = outs[network_size - 1];
-        fprintf(stdout, "epoch: %zu \t loss: %6.2lf\n",
+        fprintf(stdout, "epoch: %zu \t loss: %6.6lf\n",
                 epoch, get_avg_loss(labels, net_out, labels_shape, cost.func));
     }