hi

1 files changed, 27 insertions, 10 deletions

diff --git a/losses.py b/losses.py
index 7198ebf4..5564ca33 100644
--- a/losses.py
+++ b/losses.py
@@ -1,8 +1,10 @@
 import tensorflow as tf
+import numpy as np
 from tensorflow.keras.layers import \
     Conv2D, AveragePooling2D
 from skimage import transform
 import hyperparameters as hp
+
 class YourModel(tf.keras.Model):
     """ Your own neural network model. """
 
@@ -11,17 +13,26 @@ class YourModel(tf.keras.Model):
        
         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)
         self.style_image = tf.expand_dims(self.style_image, axis=0)
-        self.x = tf.Variable(tf.expand_dims(tf.random.uniform(tf.shape(content_image)), axis=0), trainable=True) 
+        #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.alpha = hp.alpha
         self.beta = hp.beta
 
-        print(self.x.shape)
+        self.photo_layers = None
+        self.art_layers = None
+
 
-        print(self.content_image.shape, self.style_image.shape)
+
+        #(self.x.shape)
+
+        #print(self.content_image.shape, self.style_image.shape)
 
         self.optimizer = tf.keras.optimizers.Adam()
 
@@ -88,14 +99,20 @@ class YourModel(tf.keras.Model):
         return x, layers
 
     def loss_fn(self, p, a, x):
-        _, photo_layers = self.call(p)
-        _, art_layers = self.call(a)
-        _, input_layers = self.call(x)
-
-        content_l = self.content_loss(photo_layers, input_layers)
-        style_l = self.style_loss(art_layers, input_layers)
+       # print(p)
+       if(self.photo_layers == None):
+              _, self.photo_layers = self.call(p)
+       # print(a)
+       if(self.art_layers == None): 
+              _, self.art_layers = self.call(a)
+       # print(x) 
+       _, input_layers = self.call(x)
+         
+
+       content_l = self.content_loss(self.photo_layers, input_layers)
+       style_l = self.style_loss(self.art_layers, input_layers)
         # Equation 7
-        return (self.alpha * content_l) + (self.beta * style_l)
+       return (self.alpha * content_l) + (self.beta * style_l)
         
     def content_loss(self, photo_layers, input_layers):
         L_content = tf.constant(0.0)