From 2ce97744ff9357f74299d6498dd0e5510a7ddb7a Mon Sep 17 00:00:00 2001
From: Vasil Zlatanov <v@skozl.com>
Date: Mon, 4 Mar 2019 16:26:35 +0000
Subject: Add dense_layers argument for CGAN()

---
 cgan.py | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)
 mode change 100644 => 100755 cgan.py

(limited to 'cgan.py')

diff --git a/cgan.py b/cgan.py
old mode 100644
new mode 100755
index 3c89121..68256f3
--- a/cgan.py
+++ b/cgan.py
@@ -9,11 +9,12 @@ from keras.layers.convolutional import UpSampling2D, Conv2D
 from keras.models import Sequential, Model
 from keras.optimizers import Adam
 import matplotlib.pyplot as plt
+from IPython.display import clear_output
 
 import numpy as np
 
 class CGAN():
-    def __init__(self):
+    def __init__(self, dense_layers = 3):
         # Input shape
         self.img_rows = 28
         self.img_cols = 28
@@ -21,6 +22,7 @@ class CGAN():
         self.img_shape = (self.img_rows, self.img_cols, self.channels)
         self.num_classes = 10
         self.latent_dim = 100
+        self.dense_layers = dense_layers
 
         optimizer = Adam(0.0002, 0.5)
 
@@ -56,15 +58,12 @@ class CGAN():
 
         model = Sequential()
 
-        model.add(Dense(256, input_dim=self.latent_dim))
-        model.add(LeakyReLU(alpha=0.2))
-        model.add(BatchNormalization(momentum=0.8))
-        model.add(Dense(512))
-        model.add(LeakyReLU(alpha=0.2))
-        model.add(BatchNormalization(momentum=0.8))
-        model.add(Dense(1024))
-        model.add(LeakyReLU(alpha=0.2))
-        model.add(BatchNormalization(momentum=0.8))
+        for i in range(self.dense_layers):
+            output_size = 2**(8+i)
+            model.add(Dense(output_size, input_dim=self.latent_dim))
+            model.add(LeakyReLU(alpha=0.2))
+            model.add(BatchNormalization(momentum=0.8))
+
         model.add(Dense(np.prod(self.img_shape), activation='tanh'))
         model.add(Reshape(self.img_shape))
 
@@ -223,7 +222,7 @@ class CGAN():
 
 '''
 if __name__ == '__main__':
-    cgan = CGAN()
+    cgan = CGAN(dense_layers=1)
     cgan.train(epochs=7000, batch_size=32, sample_interval=200)
     train, test, tr_labels, te_labels = cgan.generate_data()
     print(train.shape, test.shape)
-- 
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