Merge branch 'master' into sgan

bgan/bgan.py

@@ -36,7 +36,7 @@ def __init__(self):
         self.generator = self.build_generator()
 
         # The generator takes noise as input and generated imgs
-        z = Input(shape=(100,))
+        z = Input(shape=(self.latent_dim,))
         img = self.generator(z)
 
         # For the combined model we will only train the generator

cgan/cgan.py

@@ -35,7 +35,7 @@ def __init__(self):
 
         # The generator takes noise and the target label as input
         # and generates the corresponding digit of that label
-        noise = Input(shape=(100,))
+        noise = Input(shape=(self.latent_dim,))
         label = Input(shape=(1,))
         img = self.generator([noise, label])
 

cogan/cogan.py

@@ -39,7 +39,7 @@ def __init__(self):
         self.g1, self.g2 = self.build_generators()
 
         # The generator takes noise as input and generated imgs
-        z = Input(shape=(100,))
+        z = Input(shape=(self.latent_dim,))
         img1 = self.g1(z)
         img2 = self.g2(z)
 

dcgan/dcgan.py

@@ -35,7 +35,7 @@ def __init__(self):
         self.generator = self.build_generator()
 
         # The generator takes noise as input and generates imgs
-        z = Input(shape=(100,))
+        z = Input(shape=(self.latent_dim,))
         img = self.generator(z)
 
         # For the combined model we will only train the generator

lsgan/lsgan.py

@@ -34,7 +34,7 @@ def __init__(self):
         self.generator = self.build_generator()
 
         # The generator takes noise as input and generated imgs
-        z = Input(shape=(100,))
+        z = Input(shape=(self.latent_dim,))
         img = self.generator(z)
 
         # For the combined model we will only train the generator

sgan/sgan.py

@@ -206,3 +206,4 @@ def save(model, model_name):
 if __name__ == '__main__':
     sgan = SGAN()
     sgan.train(epochs=20000, batch_size=32, sample_interval=50)
+

wgan/wgan.py

@@ -39,7 +39,7 @@ def __init__(self):
         self.generator = self.build_generator()
 
         # The generator takes noise as input and generated imgs
-        z = Input(shape=(100,))
+        z = Input(shape=(self.latent_dim,))
         img = self.generator(z)
 
         # For the combined model we will only train the generator

wgan_gp/wgan_gp.py

@@ -57,7 +57,7 @@ def __init__(self):
         real_img = Input(shape=self.img_shape)
 
         # Noise input
-        z_disc = Input(shape=(100,))
+        z_disc = Input(shape=(self.latent_dim,))
         # Generate image based of noise (fake sample)
         fake_img = self.generator(z_disc)