It optimizes toward something but quality is gone.

doodle.py

@@ -247,11 +247,11 @@ def __init__(self):
             else:
                 shape = self.style_img_original.shape[:2]
 
-            self.content_map_original = np.zeros(shape+(1,))
+            self.content_map_original = np.zeros(shape+(3,))
             args.semantic_weight = 0.0
 
         if self.style_map_original is None:
-            self.style_map_original = np.zeros(self.style_img_original.shape[:2]+(1,))
+            self.style_map_original = np.zeros(self.style_img_original.shape[:2]+(3,))
             args.semantic_weight = 0.0
 
         if self.content_img_original is None:
@@ -445,27 +445,25 @@ 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(9.0 / args.semantic_weight)
-        assert semantic_weight > 0.0
+        current_best, semantic_weight = [], math.sqrt(9.0 / args.semantic_weight) if args.semantic_weight else None
 
         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] /= (norms_m * 3.0)
-            patches[:,-3:] /= (norms_s * semantic_weight)
+            weights = patches.astype(np.float32)
+            weights[:,:-3] /= (norms_m * 3.0)
+            if semantic_weight: weights[:,-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] /= (nm * 3.0) # TODO: Use exact number of channels.
-            f[:,-3:] /= (ns * semantic_weight)
+            if semantic_weight: f[:,-3:] /= (ns * semantic_weight)
 
             best, cost = self.compute_matches[l](f)
-            print('best', best[:25], '\ncost', cost[:25])
-            current_best.append(patches[best])
+            current_best.append(patches[best].astype(np.float32))
 
         grads, *losses = self.compute_grad_and_losses(current_img, self.content_map, *current_best)
         if np.isnan(grads).any():