Produce variable output gen

1 files changed, 14 insertions, 10 deletions

diff --git a/cgan.py b/cgan.py
index d27b11b..0e3dacd 100755
--- a/cgan.py
+++ b/cgan.py
@@ -194,19 +194,23 @@ class CGAN():
         fig.savefig("images/%d.png" % epoch)
         plt.close()
         
-    def generate_data(self):
-      noise_train = np.random.normal(0, 1, (55000, 100))
-      noise_test = np.random.normal(0, 1, (10000, 100))
-      noise_val = np.random.normal(0, 1, (5000, 100))
+    def generate_data(self, split):
+      train_size = int((55000*100/split)-55000)
+      val_size = int(train_size/11)
+      test_size = 2*val_size
 
-      labels_train = np.zeros(55000).reshape(-1, 1)
-      labels_test = np.zeros(10000).reshape(-1, 1)
-      labels_val = np.zeros(5000).reshape(-1, 1)
+      noise_train = np.random.normal(0, 1, (train_size, 100))
+      noise_test = np.random.normal(0, 1, (test_size, 100))
+      noise_val = np.random.normal(0, 1, (val_size, 100))
 
+      labels_train = np.zeros(train_size).reshape(-1, 1)
+      labels_test = np.zeros(test_size).reshape(-1, 1)
+      labels_val = np.zeros(val_size).reshape(-1, 1)
+      
       for i in range(10):
-        labels_train[i*5500:-1] = i
-        labels_test[i*1000:-1] = i
-        labels_val[i*500:-1] = i
+        labels_train[i*int(train_size/10):-1] = i
+        labels_test[i*int(test_size/10):-1] = i
+        labels_val[i*int(val_size/10):-1] = i
 
       train_data = self.generator.predict([noise_train, labels_train])
       test_data = self.generator.predict([noise_test, labels_test])