testing

1 files changed, 4 insertions, 4 deletions

diff --git a/losses.py b/losses.py
index 51f387a4..99b1eded 100644
--- a/losses.py
+++ b/losses.py
@@ -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, preserve_range=True).astype('uint8')
+        self.content_image = transform.resize(content_image, tf.shape(style_image), anti_aliasing=True)
         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, preserve_range=True).astype('uint8')
+        self.style_image = transform.resize(style_image, tf.shape(style_image), anti_aliasing=True)
         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('uint8'), trainable=True) 
+        self.content_image.shape[1], self.content_image.shape[2], self.content_image.shape[3]).astype(np.float32), 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).astype('uint8')
+        L_content = tf.constant(0.0)
         for i in range(len(photo_layers)):
               pl = photo_layers[i]
               il = input_layers[i]