fix typos

bicycle-gan.py

@@ -80,6 +80,8 @@ def run(args):
     logger.info('Events directory: %s', logdir)
     summary_writer = tf.summary.FileWriter(logdir)
 
+    makedirs('./results')
+
     def init_fn(sess):
         logger.info('Initializing all parameters.')
         sess.run(init_all_op)

model.py

@@ -27,7 +27,7 @@ class BicycleGAN(object):
 
         self.is_train = tf.placeholder(tf.bool, name='is_train')
         self.lr = tf.placeholder(tf.float32, name='lr')
-        self.global_step = tf.contrib.framework.get_or_create_global_step(graph=None)
+        self.global_step = tf.train.get_or_create_global_step(graph=None)
 
         image_a = self.image_a = \
             tf.placeholder(tf.float32, [self._batch_size] + self._image_shape, name='image_a')
@@ -90,10 +90,10 @@ class BicycleGAN(object):
         loss_latent_cycle = tf.reduce_mean(tf.abs(z - z_recon))
 
         loss_kl = -0.5 * tf.reduce_mean(1 + 2 * z_encoded_log_sigma - z_encoded_mu ** 2 -
-                                       tf.exp(2 * z_encoded_log_sigma), 1)
+                                       tf.exp(2 * z_encoded_log_sigma))
 
-        loss = loss_vae_gan + self._coeff_reconstruct * loss_image_cycle + \
-            loss_gan + self._coeff_latent * loss_latent_cycle + \
+        loss = loss_vae_gan - self._coeff_reconstruct * loss_image_cycle + \
+            loss_gan - self._coeff_latent * loss_latent_cycle - \
             self._coeff_kl * loss_kl
 
         # Optimizer
@@ -105,18 +105,18 @@ class BicycleGAN(object):
                             .minimize(-loss, var_list=E.var_list)
 
         # Summaries
-        self.loss_image_reconstruct = loss_image_reconstruct
+        self.loss_vae_gan = loss_vae_gan
         self.loss_image_cycle = loss_image_cycle
         self.loss_latent_cycle = loss_latent_cycle
         self.loss_gan = loss_gan
-        self.loss_z_kl = loss_z_kl
+        self.loss_kl = loss_kl
         self.loss = loss
 
-        tf.summary.scalar('loss/image_reconstruct', loss_image_reconstruct)
+        tf.summary.scalar('loss/vae_gan', loss_vae_gan)
         tf.summary.scalar('loss/image_cycle', loss_image_cycle)
         tf.summary.scalar('loss/latent_cycle', loss_latent_cycle)
         tf.summary.scalar('loss/gan', loss_gan)
-        tf.summary.scalar('loss/Dz', loss_z_kl)
+        tf.summary.scalar('loss/kl', loss_kl)
         tf.summary.scalar('loss/total', loss)
         tf.summary.scalar('model/D_real', tf.reduce_mean(D_real))
         tf.summary.scalar('model/D_fake', tf.reduce_mean(D_fake))
@@ -167,8 +167,7 @@ class BicycleGAN(object):
             #sample_z = np.random.uniform(-1, 1, size=(self._batch_size, self._latent_dim))
             sample_z = np.random.normal(size=(self._batch_size, self._latent_dim))
 
-            fetches = [self.loss,
-                       self.optimizer_D, self.optimizer_Dz,
+            fetches = [self.loss, self.optimizer_D,
                        self.optimizer_G, self.optimizer_E]
             if step % self._log_step == 0:
                 fetches += [self.summary_op]