fix return error - gradients is none now

2 files changed, 4 insertions, 14 deletions

diff --git a/__pycache__/losses.cpython-38.pyc b/__pycache__/losses.cpython-38.pyc
index c409cebf..e9a8d331 100644
--- a/__pycache__/losses.cpython-38.pyc
+++ b/__pycache__/losses.cpython-38.pyc
diff --git a/losses.py b/losses.py
index bf6f6973..16ca278e 100644
--- a/losses.py
+++ b/losses.py
@@ -69,26 +69,23 @@ class YourModel(tf.keras.Model):
        #         layer.trainable = False
 
         self.indexed_layers = [layer for layer in self.vgg16 if "conv1" in layer.name]
-        print(self.indexed_layers)
         self.desired = [layer.name for layer in self.vgg16 if "conv1" in layer.name]
 
         # create a map of the layers to their corresponding number of filters if it is a convolutional layer
         self.layer_to_filters = {layer.name: layer.filters for layer in self.vgg16 if "conv" in layer.name}
 
     def call(self, x):
-        layers = []
+        layers = np.empty(0)
         for layer in self.vgg16:
             # pass the x through
-            print('this is', x.shape)
-            print(layer.name)
             x = layer(x)
        #      print("Sotech117 is so so sus")
 
             # save the output of each layer if it is in the desired list
             if layer.name in self.desired:
-                layers.append(x)
+                layers = np.append(layers, x)
             
-            return x, np.array(layers)
+        return x, layers
 
     def loss_fn(self, p, a, x):
         _, photo_layers = self.call(p)
@@ -98,13 +95,10 @@ class YourModel(tf.keras.Model):
         content_l = self.content_loss(photo_layers, input_layers)
         style_l = self.style_loss(art_layers, input_layers)
         # Equation 7
-        print(content_l, style_l)
         return (self.alpha * content_l) + (self.beta * style_l)
         
     def content_loss(self, photo_layers, input_layers):
-        print(photo_layers, input_layers)
         L_content = tf.reduce_mean(tf.square(photo_layers - input_layers))
-        print(L_content)
         return L_content
     
     def layer_loss(self, art_layers, input_layers, layer):
@@ -113,8 +107,6 @@ class YourModel(tf.keras.Model):
         # vectorize the input_layers
         input_layers = tf.reshape(input_layers, (-1, input_layers.shape[-1]))
 
-        print('layer', layer.name, self.layer_to_filters[layer.name])
-
         # get the gram matrix
         input_dim = input_layers.shape[0]
         G = np.zeros((input_dim, input_dim))
@@ -140,15 +132,13 @@ class YourModel(tf.keras.Model):
         L_style = 0
         for layer in self.indexed_layers:
             L_style += self.layer_loss(art_layers, input_layers, layer)
-            print('this is style loss', L_style)
         return L_style
             
     def train_step(self):
        with tf.GradientTape() as tape:
               loss = self.loss_fn(self.content_image, self.style_image, self.x)
-       print(loss)
        gradients = tape.gradient(loss, self.x)
-       print(gradients)
+       print('gradients', gradients)
        self.optimizer.apply_gradients(zip(gradients, self.x))