toplevel function

2 files changed, 25 insertions, 19 deletions

diff --git a/src/rnn.c b/src/rnn.c
index 12ffb19..b6b83e2 100644
--- a/src/rnn.c
+++ b/src/rnn.c
@@ -158,12 +158,29 @@ void compute_gru(const GRULayer *gru, float *state, const float *input)
 #if 1
 #define INPUT_SIZE 42
 
-int main() {
-  float vad_out[MAX_NEURONS] = {0};
-  float input[INPUT_SIZE];
+void compute_rnn(RNNState *rnn, float *gains, float *vad, const float *input) {
+  int i;
   float dense_out[MAX_NEURONS];
   float noise_input[MAX_NEURONS*3];
   float denoise_input[MAX_NEURONS*3];
+  compute_dense(&input_dense, dense_out, input);
+  compute_gru(&vad_gru, rnn->vad_gru_state, dense_out);
+  compute_dense(&vad_output, vad, rnn->vad_gru_state);
+  for (i=0;i<INPUT_DENSE_SIZE;i++) noise_input[i] = dense_out[i];
+  for (i=0;i<VAD_GRU_SIZE;i++) noise_input[i+INPUT_DENSE_SIZE] = rnn->vad_gru_state[i];
+  for (i=0;i<INPUT_SIZE;i++) noise_input[i+INPUT_DENSE_SIZE+VAD_GRU_SIZE] = input[i];
+  compute_gru(&noise_gru, rnn->noise_gru_state, noise_input);
+
+  for (i=0;i<VAD_GRU_SIZE;i++) denoise_input[i] = rnn->vad_gru_state[i];
+  for (i=0;i<NOISE_GRU_SIZE;i++) denoise_input[i+VAD_GRU_SIZE] = rnn->noise_gru_state[i];
+  for (i=0;i<INPUT_SIZE;i++) denoise_input[i+VAD_GRU_SIZE+NOISE_GRU_SIZE] = input[i];
+  compute_gru(&denoise_gru, rnn->denoise_gru_state, denoise_input);
+  compute_dense(&denoise_output, gains, rnn->denoise_gru_state);
+}
+
+int main() {
+  float vad_out[MAX_NEURONS] = {0};
+  float input[INPUT_SIZE];
   float gains[DENOISE_OUTPUT_SIZE];
   RNNState rnn;
   RNN_CLEAR(&rnn, 1);
@@ -173,24 +190,9 @@ int main() {
     for (i=0;i<INPUT_SIZE;i++) scanf("%f", &input[i]);
     for (i=0;i<45;i++) scanf("%f", &vad_out[0]);
     if (feof(stdin)) break;
-    compute_dense(&input_dense, dense_out, input);
-    compute_gru(&vad_gru, rnn.vad_gru_state, dense_out);
-    compute_dense(&vad_output, vad_out, rnn.vad_gru_state);
-#if 1
-    for (i=0;i<INPUT_DENSE_SIZE;i++) noise_input[i] = dense_out[i];
-    for (i=0;i<VAD_GRU_SIZE;i++) noise_input[i+INPUT_DENSE_SIZE] = rnn.vad_gru_state[i];
-    for (i=0;i<INPUT_SIZE;i++) noise_input[i+INPUT_DENSE_SIZE+VAD_GRU_SIZE] = input[i];
-    compute_gru(&noise_gru, rnn.noise_gru_state, noise_input);
-
-    for (i=0;i<VAD_GRU_SIZE;i++) denoise_input[i] = rnn.vad_gru_state[i];
-    for (i=0;i<NOISE_GRU_SIZE;i++) denoise_input[i+VAD_GRU_SIZE] = rnn.noise_gru_state[i];
-    for (i=0;i<INPUT_SIZE;i++) denoise_input[i+VAD_GRU_SIZE+NOISE_GRU_SIZE] = input[i];
-    compute_gru(&denoise_gru, rnn.denoise_gru_state, denoise_input);
-
-    compute_dense(&denoise_output, gains, rnn.denoise_gru_state);
+    compute_rnn(&rnn, gains, vad_out, input);
 
     for (i=0;i<22;i++) printf("%f ", gains[i]);
-#endif
     printf("%f\n", vad_out[0]);
   }
 }
diff --git a/src/rnn.h b/src/rnn.h
index a040cec..3235d93 100644
--- a/src/rnn.h
+++ b/src/rnn.h
@@ -54,8 +54,12 @@ typedef struct {
   int activation;
 } GRULayer;
 
+typedef struct RNNState RNNState;
+
 void compute_dense(const DenseLayer *layer, float *output, const float *input);
 
 void compute_gru(const GRULayer *gru, float *state, const float *input);
 
+void compute_rnn(RNNState *rnn, float *gains, float *vad, const float *input);
+
 #endif /* _MLP_H_ */