choose latent distribution using --z_dist from { 'normal01': standard normal; 'uniform_signed': (-1,1); 'uniform_unsigned':(0,1) }

Author: memoakten

main.py

@@ -25,7 +25,9 @@
 flags.DEFINE_boolean("train", False, "True for training, False for testing [False]")
 flags.DEFINE_boolean("crop", False, "True for training, False for testing [False]")
 flags.DEFINE_boolean("visualize", False, "True for visualizing, False for nothing [False]")
-flags.DEFINE_integer("generate_test_images", 100, "Number of images to generate during test. [100]")
+flags.DEFINE_integer("z_dim", 100, "dimensions of z")
+flags.DEFINE_string("z_dist", "uniform_signed", "'normal01' or 'uniform_unsigned' or uniform_signed")
+#flags.DEFINE_integer("generate_test_images", 100, "Number of images to generate during test. [100]")
 FLAGS = flags.FLAGS
 
 def main(_):
@@ -56,7 +58,7 @@ def main(_):
           batch_size=FLAGS.batch_size,
           sample_num=FLAGS.batch_size,
           y_dim=10,
-          z_dim=FLAGS.generate_test_images,
+          z_dim=FLAGS.z_dim,
           dataset_name=FLAGS.dataset,
           input_fname_pattern=FLAGS.input_fname_pattern,
           crop=FLAGS.crop,
@@ -72,7 +74,7 @@ def main(_):
           output_height=FLAGS.output_height,
           batch_size=FLAGS.batch_size,
           sample_num=FLAGS.batch_size,
-          z_dim=FLAGS.generate_test_images,
+          z_dim=FLAGS.z_dim,
           dataset_name=FLAGS.dataset,
           input_fname_pattern=FLAGS.input_fname_pattern,
           crop=FLAGS.crop,

model.py

@@ -13,6 +13,12 @@
 def conv_out_size_same(size, stride):
   return int(math.ceil(float(size) / float(stride)))
 
+def gen_random(mode, size):
+    if mode=='normal01': return np.random.normal(0,1,size=size)
+    if mode=='uniform_signed': return np.random.uniform(-1,1,size=size)
+    if mode=='uniform_unsigned': return np.random.uniform(0,1,size=size)
+
+
 class DCGAN(object):
   def __init__(self, sess, input_height=108, input_width=108, crop=True,
          batch_size=64, sample_num = 64, output_height=64, output_width=64,
@@ -166,7 +172,7 @@ def train(self, config):
         [self.z_sum, self.d_sum, self.d_loss_real_sum, self.d_loss_sum])
     self.writer = SummaryWriter("./logs", self.sess.graph)
 
-    sample_z = np.random.uniform(-1, 1, size=(self.sample_num , self.z_dim))
+    sample_z = gen_random(config.z_dist, size=(self.sample_num , self.z_dim))
 
     if config.dataset == 'mnist':
       sample_inputs = self.data_X[0:self.sample_num]
@@ -223,7 +229,7 @@ def train(self, config):
           else:
             batch_images = np.array(batch).astype(np.float32)
 
-        batch_z = np.random.uniform(-1, 1, [config.batch_size, self.z_dim]) \
+        batch_z = gen_random(config.z_dist, size=[config.batch_size, self.z_dim]) \
               .astype(np.float32)
 
         if config.dataset == 'mnist':