8 files changed, 11 insertions, 11 deletions

diff --git a/__pycache__/hyperparameters.cpython-38.pyc b/__pycache__/hyperparameters.cpython-38.pyc
index e42d48d1..12ab00e4 100644
--- a/__pycache__/hyperparameters.cpython-38.pyc
+++ b/__pycache__/hyperparameters.cpython-38.pyc
diff --git a/__pycache__/losses.cpython-38.pyc b/__pycache__/losses.cpython-38.pyc
index 07bf7a33..d25c70d0 100644
--- a/__pycache__/losses.cpython-38.pyc
+++ b/__pycache__/losses.cpython-38.pyc
diff --git a/content.jpg b/content.jpg
new file mode 100644
index 00000000..163629cd
--- /dev/null
+++ b/content.jpg
diff --git a/hyperparameters.py b/hyperparameters.py
index a15d04ac..75528742 100644
--- a/hyperparameters.py
+++ b/hyperparameters.py
@@ -9,17 +9,17 @@ Number of epochs. If you experiment with more complex networks you
 might need to increase this. Likewise if you add regularization that
 slows training.
 """
-num_epochs = 10000
+num_epochs = 500
 
 """
 A critical parameter that can dramatically affect whether training
 succeeds or fails. The value for this depends significantly on which
 optimizer is used. Refer to the default learning rate parameter
 """
-learning_rate = .002
+learning_rate = 1e2
 
 momentum = 0.01
 
-alpha = 1e-2
+alpha = 1
 
-beta = 5e1
+beta = 100
diff --git a/losses.py b/losses.py
index f08a734c..51f387a4 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)
+        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
             
diff --git a/main.py b/main.py
index 45714611..2d8c216a 100644
--- a/main.py
+++ b/main.py
@@ -13,6 +13,7 @@ from losses import YourModel
 from skimage.io import imread, imsave
 from matplotlib import pyplot as plt
 import numpy as np
+from skimage import transform
 
 
 def parse_args():
@@ -53,8 +54,7 @@ def main():
 
     content_image = imread(ARGS.content)
     style_image = imread(ARGS.style)
-    style_image = transform.resize(style_image, content_image.shape, anti_aliasing=True)
-    
+    style_image = transform.resize(style_image, content_image.shape).astype('uint8')
     my_model = YourModel(content_image=content_image, style_image=style_image)
     my_model.vgg16.build([1, 255, 255, 3])
     my_model.vgg16.load_weights('vgg16_imagenet.h5', by_name=True)
diff --git a/save.jpg b/save.jpg
index 9155df97..e35f2cbf 100644
--- a/save.jpg
+++ b/save.jpg
diff --git a/style.jpg b/style.jpg
new file mode 100644
index 00000000..105ac2d1
--- /dev/null
+++ b/style.jpg