remove custom asserts and default to ACTIVATION_RELU

Author: sleepybishop

src/rnn.c

@@ -92,17 +92,20 @@ void compute_dense(const DenseLayer *layer, float *output, const float *input)
          sum += layer->input_weights[j*stride + i]*input[j];
       output[i] = WEIGHTS_SCALE*sum;
    }
-   if (layer->activation == ACTIVATION_SIGMOID) {
+   switch (layer->activation) {
+     case ACTIVATION_SIGMOID:
       for (i=0;i<N;i++)
          output[i] = sigmoid_approx(output[i]);
-   } else if (layer->activation == ACTIVATION_TANH) {
+      break;
+    case ACTIVATION_TANH:
       for (i=0;i<N;i++)
          output[i] = tansig_approx(output[i]);
-   } else if (layer->activation == ACTIVATION_RELU) {
+      break;
+    default:
+    case ACTIVATION_RELU:
       for (i=0;i<N;i++)
          output[i] = relu(output[i]);
-   } else {
-     *(int*)0=0;
+      break;
    }
 }
 
@@ -145,14 +148,16 @@ void compute_gru(const GRULayer *gru, float *state, const float *input)
          sum += gru->input_weights[2*N + j*stride + i]*input[j];
       for (j=0;j<N;j++)
          sum += gru->recurrent_weights[2*N + j*stride + i]*state[j]*r[j];
-      if (gru->activation == ACTIVATION_SIGMOID) sum = sigmoid_approx(WEIGHTS_SCALE*sum);
-      else if (gru->activation == ACTIVATION_TANH) sum = tansig_approx(WEIGHTS_SCALE*sum);
-      else if (gru->activation == ACTIVATION_RELU) sum = relu(WEIGHTS_SCALE*sum);
-      else *(int*)0=0;
-      h[i] = z[i]*state[i] + (1-z[i])*sum;
-   }
-   for (i=0;i<N;i++)
-      state[i] = h[i];
+    switch (gru->activation) {
+      case ACTIVATION_SIGMOID: sum = sigmoid_approx(WEIGHTS_SCALE*sum);break;
+      case ACTIVATION_TANH: sum = tansig_approx(WEIGHTS_SCALE*sum); break;
+      default:
+      case  ACTIVATION_RELU: sum = relu(WEIGHTS_SCALE*sum); break;
+    }
+    h[i] = z[i]*state[i] + (1-z[i])*sum;
+  }
+  for (i=0;i<N;i++)
+    state[i] = h[i];
 }
 
 #define INPUT_SIZE 42