2 files changed, 10 insertions, 8 deletions
diff --git a/cgan.py b/cgan.py
index 6406244..68bb2cc 100755
--- a/cgan.py
+++ b/cgan.py
@@ -15,7 +15,7 @@ from tqdm import tqdm
 import numpy as np
 
 class CGAN():
-    def __init__(self, dense_layers = 3):
+    def __init__(self, dense_layers = 3, dropout=0.4):
         # Input shape
         self.img_rows = 28
         self.img_cols = 28
@@ -24,6 +24,7 @@ class CGAN():
         self.num_classes = 10
         self.latent_dim = 100
         self.dense_layers = dense_layers
+        self.dropout = dropout
 
         optimizer = Adam(0.0002, 0.5)
 
@@ -87,10 +88,10 @@ class CGAN():
         model.add(LeakyReLU(alpha=0.2))
         model.add(Dense(512))
         model.add(LeakyReLU(alpha=0.2))
-        model.add(Dropout(0.4))
+        model.add(Dropout(self.dropout))
         model.add(Dense(512))
         model.add(LeakyReLU(alpha=0.2))
-        model.add(Dropout(0.4))
+        model.add(Dropout(self.dropout))
         model.add(Dense(1, activation='sigmoid'))
         
         #model.summary()
diff --git a/dcgan.py b/dcgan.py
index 347f61e..7844843 100644
--- a/dcgan.py
+++ b/dcgan.py
@@ -17,7 +17,7 @@ import sys
 import numpy as np
 
 class DCGAN():
-    def __init__(self, conv_layers = 1):
+    def __init__(self, conv_layers = 1, dropout = 0.25):
         # Input shape
         self.img_rows = 28
         self.img_cols = 28
@@ -25,6 +25,7 @@ class DCGAN():
         self.img_shape = (self.img_rows, self.img_cols, self.channels)
         self.latent_dim = 100
         self.conv_layers = conv_layers
+        self.dropout = dropout
 
         optimizer = Adam(0.002, 0.5)
 
@@ -88,20 +89,20 @@ class DCGAN():
 
         model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
         model.add(LeakyReLU(alpha=0.2))
-        model.add(Dropout(0.25))
+        model.add(Dropout(self.dropout))
         model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
         model.add(ZeroPadding2D(padding=((0,1),(0,1))))
         model.add(BatchNormalization())
         model.add(LeakyReLU(alpha=0.2))
-        model.add(Dropout(0.25))
+        model.add(Dropout(self.dropout))
         model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
         model.add(BatchNormalization())
         model.add(LeakyReLU(alpha=0.2))
-        model.add(Dropout(0.25))
+        model.add(Dropout(self.dropout))
         model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
         model.add(BatchNormalization())
         model.add(LeakyReLU(alpha=0.2))
-        model.add(Dropout(0.25))
+        model.add(Dropout(self.dropout))
         model.add(Flatten())
         model.add(Dense(1, activation='sigmoid'))