fix 'logits' naming in A3C (fix #197)

examples/A3C-Gym/run-atari.py

@@ -64,7 +64,7 @@ class Model(ModelDesc):
     def _build_graph(self, inputs):
         state, action, futurereward = inputs
         policy = self._get_NN_prediction(state)
-        self.logits = tf.nn.softmax(policy, name='logits')
+        policy = tf.nn.softmax(policy, name='policy')
 
 
 def run_submission(cfg, output, nr):
@@ -105,5 +105,5 @@ if __name__ == '__main__':
         model=Model(),
         session_init=SaverRestore(args.load),
         input_names=['state'],
-        output_names=['logits'])
+        output_names=['policy'])
     run_submission(cfg, args.output, args.episode)

examples/A3C-Gym/train-atari.py

@@ -96,30 +96,30 @@ class Model(ModelDesc):
 
         l = FullyConnected('fc0', l, 512, nl=tf.identity)
         l = PReLU('prelu', l)
-        policy = FullyConnected('fc-pi', l, out_dim=NUM_ACTIONS, nl=tf.identity)
+        logits = FullyConnected('fc-pi', l, out_dim=NUM_ACTIONS, nl=tf.identity)    # unnormalized policy
         value = FullyConnected('fc-v', l, 1, nl=tf.identity)
-        return policy, value
+        return logits, value
 
     def _build_graph(self, inputs):
         state, action, futurereward = inputs
-        policy, self.value = self._get_NN_prediction(state)
+        logits, self.value = self._get_NN_prediction(state)
         self.value = tf.squeeze(self.value, [1], name='pred_value')  # (B,)
-        self.logits = tf.nn.softmax(policy, name='logits')
+        self.policy = tf.nn.softmax(logits, name='policy')
 
         expf = tf.get_variable('explore_factor', shape=[],
                                initializer=tf.constant_initializer(1), trainable=False)
-        logitsT = tf.nn.softmax(policy * expf, name='logitsT')
+        policy_explore = tf.nn.softmax(logits * expf, name='policy_explore')
         is_training = get_current_tower_context().is_training
         if not is_training:
             return
-        log_probs = tf.log(self.logits + 1e-6)
+        log_probs = tf.log(self.policy + 1e-6)
 
         log_pi_a_given_s = tf.reduce_sum(
             log_probs * tf.one_hot(action, NUM_ACTIONS), 1)
         advantage = tf.subtract(tf.stop_gradient(self.value), futurereward, name='advantage')
         policy_loss = tf.reduce_sum(log_pi_a_given_s * advantage, name='policy_loss')
         xentropy_loss = tf.reduce_sum(
-            self.logits * log_probs, name='xentropy_loss')
+            self.policy * log_probs, name='xentropy_loss')
         value_loss = tf.nn.l2_loss(self.value - futurereward, name='value_loss')
 
         pred_reward = tf.reduce_mean(self.value, name='predict_reward')
@@ -151,7 +151,7 @@ class MySimulatorMaster(SimulatorMaster, Callback):
 
     def _setup_graph(self):
         self.async_predictor = MultiThreadAsyncPredictor(
-            self.trainer.get_predictors(['state'], ['logitsT', 'pred_value'],
+            self.trainer.get_predictors(['state'], ['policy_explore', 'pred_value'],
                                         PREDICTOR_THREAD), batch_size=15)
 
     def _before_train(self):
@@ -220,7 +220,7 @@ def get_config():
                                       [(80, 2), (100, 3), (120, 4), (140, 5)]),
             master,
             StartProcOrThread(master),
-            PeriodicCallback(Evaluator(EVAL_EPISODE, ['state'], ['logits']), 2),
+            PeriodicCallback(Evaluator(EVAL_EPISODE, ['state'], ['policy']), 2),
         ],
         session_creator=sesscreate.NewSessionCreator(
             config=get_default_sess_config(0.5)),
@@ -254,7 +254,7 @@ if __name__ == '__main__':
             model=Model(),
             session_init=SaverRestore(args.load),
             input_names=['state'],
-            output_names=['logits'])
+            output_names=['policy'])
         if args.task == 'play':
             play_model(cfg)
         elif args.task == 'eval':

examples/GAN/GAN.py

@@ -43,15 +43,15 @@ class GANModelDesc(ModelDesc):
                 d_pos_acc = tf.reduce_mean(tf.cast(score_real > 0.5, tf.float32), name='accuracy_real')
                 d_neg_acc = tf.reduce_mean(tf.cast(score_fake < 0.5, tf.float32), name='accuracy_fake')
 
-                self.d_accuracy = tf.add(.5 * d_pos_acc, .5 * d_neg_acc, name='accuracy')
+                d_accuracy = tf.add(.5 * d_pos_acc, .5 * d_neg_acc, name='accuracy')
                 self.d_loss = tf.add(.5 * d_loss_pos, .5 * d_loss_neg, name='loss')
 
             with tf.name_scope("gen"):
                 self.g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
                     logits=logits_fake, labels=tf.ones_like(logits_fake)), name='loss')
-                self.g_accuracy = tf.reduce_mean(tf.cast(score_fake > 0.5, tf.float32), name='accuracy')
+                g_accuracy = tf.reduce_mean(tf.cast(score_fake > 0.5, tf.float32), name='accuracy')
 
-            add_moving_summary(self.g_loss, self.d_loss, self.d_accuracy, self.g_accuracy)
+            add_moving_summary(self.g_loss, self.d_loss, d_accuracy, g_accuracy)
 
 
 class GANTrainer(FeedfreeTrainerBase):