1 files changed, 13 insertions, 14 deletions
diff --git a/cdcgan.py b/cdcgan.py
index 8d59a03..effc89b 100755
--- a/cdcgan.py
+++ b/cdcgan.py
@@ -7,7 +7,7 @@ from keras.datasets import mnist
 from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
 from keras.layers import BatchNormalization, Embedding, Activation, ZeroPadding2D
 from keras.layers import LeakyReLU
-from keras.layers import UpSampling2D, Conv2D
+from keras.layers import UpSampling2D, Conv2D, Conv2DTranspose
 from keras.models import Sequential, Model
 from keras.optimizers import Adam
 
@@ -65,25 +65,22 @@ class CDCGAN():
 
         model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
         model.add(Reshape((7, 7, 128)))
-        model.add(UpSampling2D())
 
-        for i in range(self.conv_layers):
-            model.add(Conv2D(128, kernel_size=3, padding="same"))
-            model.add(BatchNormalization())
-            model.add(Activation("relu"))
-
-        model.add(UpSampling2D())
+        model.add(Conv2DTranspose(256, kernel_size=3, padding="same", strides=(2,2)))
+        model.add(BatchNormalization())
+        model.add(Activation("relu"))
 
-        for i in range(self.conv_layers):
-            model.add(Conv2D(64, kernel_size=3, padding="same"))
-            model.add(BatchNormalization())
+        model.add(Conv2DTranspose(128, kernel_size=3, padding="same", strides=(2,2)))
+        model.add(BatchNormalization())
+        model.add(Activation("relu"))
 
-            model.add(Activation("relu"))
+        model.add(Conv2DTranspose(64, kernel_size=3, padding="same"))
+        model.add(BatchNormalization())
+        model.add(Activation("relu"))
 
-        model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
+        model.add(Conv2DTranspose(1, kernel_size=3, padding="same"))
         model.add(Activation("tanh"))
 
-        #model.summary()
 
         noise = Input(shape=(self.latent_dim,))
         label = Input(shape=(1,), dtype='int32')
@@ -91,6 +88,8 @@ class CDCGAN():
         model_input = multiply([noise, label_embedding])
         img = model(model_input)
 
+        #model.summary()
+
         return Model([noise, label], img)
 
     def build_discriminator(self):