From cba0f67ae292b2078c2d2435ca39bd7383adb846 Mon Sep 17 00:00:00 2001
From: Alexander Munch-Hansen <alexmunchhansen@gmail.com>
Date: Thu, 19 Apr 2018 15:22:00 +0200
Subject: [PATCH] fixed *the* bug

---
 network.py | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/network.py b/network.py
index 467c7fc..4486790 100644
--- a/network.py
+++ b/network.py
@@ -7,7 +7,7 @@ import time
 import sys
 import random
 from eval import Eval
-
+import glob
 
 class Network:
     # board_features_quack has size 28
@@ -38,10 +38,10 @@ class Network:
         # Can't remember the best learning_rate, look this up
         self.max_learning_rate = 0.1
         self.min_learning_rate = 0.001
-        # self.learning_rate = 0.01
+        self.learning_rate = 0.01
 
         self.global_step = tf.Variable(0, trainable=False, name="global_step")
-        self.learning_rate = tf.maximum(self.min_learning_rate, tf.train.exponential_decay(self.max_learning_rate, self.global_step, 50000, 0.96, staircase=True), name="learning_rate")
+        # self.learning_rate = tf.maximum(self.min_learning_rate, tf.train.exponential_decay(self.max_learning_rate, self.global_step, 50000, 0.96, staircase=True), name="learning_rate")
 
         
         
@@ -88,7 +88,8 @@ class Network:
         apply_gradients = []
 
         global_step_op = self.global_step.assign_add(1)
-        
+
+
         with tf.variable_scope('apply_gradients'):
             for gradient, trainable_var in zip(gradients, trainable_vars):
                 # Hopefully this is Δw_t = α(V_t+1 - V_t)▿_wV_t.
@@ -96,6 +97,9 @@ class Network:
                 grad_apply = trainable_var.assign_add(backprop_calc)
                 apply_gradients.append(grad_apply)
 
+        
+        with tf.control_dependencies([global_step_op]):
+
             self.training_op = tf.group(*apply_gradients, name='training_op')
 
         self.saver = tf.train.Saver(max_to_keep=1)
@@ -145,7 +149,8 @@ class Network:
             f.write(str(episode_count) + "\n")
 
     def restore_model(self, sess):
-        if os.path.isfile(os.path.join(self.checkpoint_path, 'model.ckpt.index')):
+        if glob.glob(os.path.join(self.checkpoint_path, 'model.ckpt*.index')):
+        
             latest_checkpoint = tf.train.latest_checkpoint(self.checkpoint_path)
             print("[NETWK] ({name}) Restoring model from:".format(name=self.name),
                   str(latest_checkpoint))
@@ -162,6 +167,8 @@ class Network:
             if os.path.isfile(episode_count_path):
                 with open(episode_count_path, 'r') as f:
                     self.config['start_episode'] = int(f.read())
+        else:
+            assert False
 
     def make_move(self, sess, board, roll, player):
         # print(Board.pretty(board))
@@ -234,9 +241,6 @@ class Network:
                         board = Eval.make_pubeval_move(board, -1, roll)[0][0:26]
                         # print("post pubeval:", board, sep="\n")
 
-                    # print("*"*30)
-                    # print(board)
-                    # print("+"*30)
                     sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
                     outcomes.append(Board.outcome(board)[1])
                     sys.stderr.write("\n")
@@ -271,9 +275,6 @@ class Network:
                         board = Eval.make_dumbeval_move(board, -1, roll)[0][0:26]
                         # print("post pubeval:", board, sep="\n")
 
-                    # print("*"*30)
-                    # print(board)
-                    # print("+"*30)
                     sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
                     outcomes.append(Board.outcome(board)[1])
                     sys.stderr.write("\n")
@@ -405,7 +406,7 @@ class Network:
 
                 with tf.name_scope("final"):
                     merged = tf.summary.merge_all()
-                    summary, _, global_step = sess.run([merged, self.training_op, self.global_step],
+                    global_step, summary, _ = sess.run([self.global_step, merged, self.training_op],
                                           feed_dict={self.x: self.board_trans_func(prev_board, player),
                                                      self.value_next: scaled_final_score.reshape((1, 1))})
                     writer.add_summary(summary, episode + trained_eps)
@@ -420,7 +421,7 @@ class Network:
                     print_time_estimate(episode)
 
             sys.stderr.write("[TRAIN] Saving model for final episode...\n")
-            self.save_model(sess, episode+trained_eps, global_step=global_step)
+            self.save_model(sess, episode+trained_eps, global_step)
             
             writer.close()