2 files changed, 9 insertions, 16 deletions
diff --git a/cdcgan.py b/cdcgan.py
index 8d59a03..895def2 100755
--- a/cdcgan.py
+++ b/cdcgan.py
@@ -65,22 +65,16 @@ 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())
-
-        for i in range(self.conv_layers):
-            model.add(Conv2D(64, kernel_size=3, padding="same"))
-            model.add(BatchNormalization())
+        model.add(Conv2DTranspose(256, kernel_size=3, padding="same", strides=(2,2)))
+        model.add(BatchNormalization())
+        model.add(Activation("relu"))
 
-            model.add(Activation("relu"))
+        model.add(Conv2DTranspose(128, kernel_size=3, padding="same", strides=(2,2)))
+        model.add(BatchNormalization())
+        model.add(Activation("relu"))
 
-        model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
+        model.add(Conv2DTranspose(64, kernel_size=3, padding="same"))
         model.add(Activation("tanh"))
 
         #model.summary()
diff --git a/dcgan.py b/dcgan.py
index 4317994..a362f69 100644
--- a/dcgan.py
+++ b/dcgan.py
@@ -1,4 +1,5 @@
 from __future__ import print_function, division
+import tensorflow.keras as keras
 from keras.datasets import mnist
 from keras.layers import Input, Dense, Reshape, Flatten, Dropout
 from keras.layers import BatchNormalization, Activation, ZeroPadding2D
@@ -76,7 +77,7 @@ class DCGAN():
         model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
         model.add(Activation("tanh"))
 
-        #model.summary()
+        model.summary()
 
         noise = Input(shape=(self.latent_dim,))
         img = model(noise)
@@ -191,8 +192,6 @@ class DCGAN():
         fig.savefig("images/mnist_%d.png" % epoch)
         plt.close()
 
-'''
 if __name__ == '__main__':
     dcgan = DCGAN()
     dcgan.train(epochs=4000, batch_size=32, save_interval=50)
-'''