Fixes for semantic weight calculation.

doodle.py

@@ -445,25 +445,26 @@ def evaluate(self, Xn):
         current_features = self.compute_features(current_img, self.content_map)
 
         # Iterate through each of the style layers one by one, computing best matches.
-        current_best, semantic_weight = [], math.sqrt(args.semantic_weight)
+        current_best, semantic_weight = [], math.sqrt(9.0 / args.semantic_weight)
         assert semantic_weight > 0.0
 
         for l, f in zip(self.style_layers, current_features):
             layer = self.model.network['nn'+l]
             patches, norms_m, norms_s = self.style_data[l]
 
             patches = patches.astype(np.float32)
-            patches[:,:-3] /= (3.0 * norms_m.astype(np.float32))
-            patches[:,-3:] /= (3.0 * norms_s.astype(np.float32) * semantic_weight)
+            patches[:,:-3] /= (norms_m * 3.0)
+            patches[:,-3:] /= (norms_s * semantic_weight)
             layer.W.set_value(patches)
 
             nm = np.sqrt(np.sum(f[:,:-3] ** 2.0, axis=(1,), keepdims=True))
             ns = np.sqrt(np.sum(f[:,-3:] ** 2.0, axis=(1,), keepdims=True))
 
-            f[:,:-3] /= (3.0 * nm) # TODO: Use exact number of channels.
-            f[:,-3:] /= (3.0 * ns * semantic_weight)
+            f[:,:-3] /= (nm * 3.0) # TODO: Use exact number of channels.
+            f[:,-3:] /= (ns * semantic_weight)
 
             best, cost = self.compute_matches[l](f)
+            print('best', best[:25], '\ncost', cost[:25])
             current_best.append(patches[best])
 
         grads, *losses = self.compute_grad_and_losses(current_img, self.content_map, *current_best)