diff options
Diffstat (limited to 'losses.py')
| -rw-r--r-- | losses.py | 10 |
1 files changed, 5 insertions, 5 deletions
@@ -11,16 +11,16 @@ class YourModel(tf.keras.Model): def __init__(self, content_image, style_image): #normalize these images to float values super(YourModel, self).__init__() - self.content_image = transform.resize(content_image, tf.shape(style_image), anti_aliasing=True) + self.content_image = transform.resize(content_image, tf.shape(style_image), anti_aliasing=True, preserve_range=True).astype('uint8') self.content_image = tf.expand_dims(self.content_image, axis=0) print(self.content_image) #perhaps consider cropping to avoid distortion - self.style_image = transform.resize(style_image, tf.shape(style_image), anti_aliasing=True) + self.style_image = transform.resize(style_image, tf.shape(style_image), anti_aliasing=True, preserve_range=True).astype('uint8') self.style_image = tf.expand_dims(self.style_image, axis=0) #self.x = tf.Variable(initial_value = self.content_image.numpy().astype(np.float32), trainable=True) self.x = tf.Variable(initial_value = np.random.rand(self.content_image.shape[0], - self.content_image.shape[1], self.content_image.shape[2], self.content_image.shape[3]).astype(np.float32), trainable=True) + self.content_image.shape[1], self.content_image.shape[2], self.content_image.shape[3]).astype('uint8'), trainable=True) self.alpha = hp.alpha self.beta = hp.beta @@ -117,7 +117,7 @@ class YourModel(tf.keras.Model): return (self.alpha * content_l) + (self.beta * style_l) def content_loss(self, photo_layers, input_layers): - L_content = tf.constant(0.0) + L_content = tf.constant(0.0).astype('uint8') for i in range(len(photo_layers)): pl = photo_layers[i] il = input_layers[i] @@ -166,7 +166,7 @@ class YourModel(tf.keras.Model): for i in range(len(art_layers)): art_layer = art_layers[i] input_layer = input_layers[i] - L_style = tf.math.add(L_style, self.layer_loss(art_layer, input_layer)*(1/5)) + L_style = tf.math.add(L_style, (1/5)*self.layer_loss(art_layer, input_layer)) #print('style loss', L_style) return L_style |