diff options
-rw-r--r-- | __pycache__/losses.cpython-38.pyc | bin | 4701 -> 4603 bytes | |||
-rw-r--r-- | losses.py | 9 | ||||
-rw-r--r-- | main.py | 3 | ||||
-rw-r--r-- | save.jpg | bin | 40051 -> 39951 bytes | |||
-rw-r--r-- | vgg16_imagenet.h5 | bin | 0 -> 58909648 bytes |
5 files changed, 6 insertions, 6 deletions
diff --git a/__pycache__/losses.cpython-38.pyc b/__pycache__/losses.cpython-38.pyc Binary files differindex 9678ad00..1112f0ad 100644 --- a/__pycache__/losses.cpython-38.pyc +++ b/__pycache__/losses.cpython-38.pyc @@ -64,15 +64,17 @@ class YourModel(tf.keras.Model): for layer in self.vgg16: layer.trainable = False + self.layer_to_filters = {layer.name: layer.filters for layer in self.vgg16 if "conv" in layer.name} self.indexed_layers = [layer for layer in self.vgg16 if "conv1" in layer.name] self.desired = [layer.name for layer in self.vgg16 if "conv1" in layer.name] + self.vgg16 = tf.keras.Sequential(self.vgg16, name="vgg") + # 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 = [] - for layer in self.vgg16: + for layer in self.vgg16.layers: # pass the x through x = layer(x) # print("Sotech117 is so so sus") @@ -159,6 +161,3 @@ class YourModel(tf.keras.Model): print(type(self.x)) print(type(gradients)) self.optimizer.apply_gradients(zip(gradients, [self.x])) - - - @@ -53,7 +53,8 @@ def main(): style_image = imread(ARGS.style) style_image = np.resize(style_image, (255, 255, 3)) my_model = YourModel(content_image=content_image, style_image=style_image) - # my_model.vgg16.load_weights(ARGS.load_vgg, by_name=True) + my_model.vgg16.build([1, 255, 255, 3]) + my_model.vgg16.load_weights('vgg16_imagenet.h5', by_name=True) train(my_model) final_image = tf.squeeze(my_model.x) Binary files differdiff --git a/vgg16_imagenet.h5 b/vgg16_imagenet.h5 Binary files differnew file mode 100644 index 00000000..08138b10 --- /dev/null +++ b/vgg16_imagenet.h5 |