Add repeateable conv_layers to dcgan

1 files changed, 15 insertions, 8 deletions

diff --git a/dcgan.py b/dcgan.py
index b48e99f..a0a26c9 100644
--- a/dcgan.py
+++ b/dcgan.py
@@ -15,13 +15,14 @@ import sys
 import numpy as np
 
 class DCGAN():
-    def __init__(self):
+    def __init__(self, conv_layers = 1):
         # Input shape
         self.img_rows = 28
         self.img_cols = 28
         self.channels = 1
         self.img_shape = (self.img_rows, self.img_cols, self.channels)
         self.latent_dim = 100
+        self.conv_layers = conv_layers
 
         optimizer = Adam(0.002, 0.5)
 
@@ -56,13 +57,19 @@ class DCGAN():
         model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
         model.add(Reshape((7, 7, 128)))
         model.add(UpSampling2D())
-        model.add(Conv2D(128, kernel_size=3, padding="same"))
-        model.add(BatchNormalization())
-        model.add(Activation("relu"))
+
+        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(Conv2D(64, kernel_size=3, padding="same"))
-        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(Activation("relu"))
+
         model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
         model.add(Activation("tanh"))
 
@@ -183,4 +190,4 @@ class DCGAN():
 if __name__ == '__main__':
     dcgan = DCGAN()
     dcgan.train(epochs=4000, batch_size=32, save_interval=50)
-'''
\ No newline at end of file
+'''