fix image summary

examples/GAN/DCGAN-CelebA.py

@@ -75,7 +75,7 @@ class Model(ModelDesc):
                 W_init=tf.truncated_normal_initializer(stddev=0.02)):
             with tf.variable_scope('gen'):
                 image_gen = self.generator(z)
-                tf.summary.image('gen', image_gen, max_images=30)
+                tf.summary.image('gen', image_gen, max_outputs=30)
             with tf.variable_scope('discrim'):
                 vecpos = self.discriminator(image_pos)
             with tf.variable_scope('discrim', reuse=True):
@@ -105,12 +105,11 @@ def get_config():
         optimizer=tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-3),
         callbacks=Callbacks([
             StatPrinter(), ModelSaver(),
-            ScheduledHyperParamSetter('learning_rate', [(200, 1e-4)])
         ]),
         session_config=get_default_sess_config(0.5),
         model=Model(),
         step_per_epoch=300,
-        max_epoch=300,
+        max_epoch=200,
     )
 
 def sample(model_path):

examples/GAN/Image2Image.py

@@ -117,7 +117,7 @@ class Model(ModelDesc):
             fake_output = tf.image.grayscale_to_rgb(fake_output)
         viz = (tf.concat(2, [input, output, fake_output]) + 1.0) * 128.0
         viz = tf.cast(tf.clip_by_value(viz, 0, 255), tf.uint8, name='viz')
-        tf.image_summary('gen', viz, max_images=max(30, BATCH))
+        tf.image_summary('gen', viz, max_outputs=max(30, BATCH))
 
         all_vars = tf.trainable_variables()
         self.g_vars = [v for v in all_vars if v.name.startswith('gen/')]

examples/GAN/InfoGAN-mnist.py

@@ -66,7 +66,7 @@ class Model(ModelDesc):
                 W_init=tf.truncated_normal_initializer(stddev=0.02)):
             with tf.variable_scope('gen'):
                 image_gen = self.generator(z)
-                tf.summary.image('gen', image_gen, max_images=30)
+                tf.summary.image('gen', image_gen, max_outputs=30)
             with tf.variable_scope('discrim'):
                 vecpos, _ = self.discriminator(image_pos)
             with tf.variable_scope('discrim', reuse=True):