Pownie/backgammon
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Added a verbosity flag, --verbose, which allows for printing of

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variables and such.
This commit is contained in:
Alexander Munch-Hansen 2018-05-10 10:39:22 +02:00
parent 6429e0732c
commit 9cfdd7e2b2
2 changed files with 25 additions and 9 deletions
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6
main.py
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@ -38,6 +38,8 @@ parser.add_argument('--board-rep', action='store', dest='board_rep',
help='name of board representation to use as input to neural network') help='name of board representation to use as input to neural network')
parser.add_argument('--use-baseline', action='store_true', parser.add_argument('--use-baseline', action='store_true',
help='use the baseline model, note, has size 28') help='use the baseline model, note, has size 28')
parser.add_argument('--verbose', action='store_true',
help='If set, a lot of stuff will be printed')
args = parser.parse_args() args = parser.parse_args()
@ -61,7 +63,9 @@ config = {
'board_representation': args.board_rep, 'board_representation': args.board_rep,
'force_creation': args.force_creation, 'force_creation': args.force_creation,
'use_baseline': args.use_baseline, 'use_baseline': args.use_baseline,
'global_step': 0 'global_step': 0,
'verbose': args.verbose
} }
# Create models folder # Create models folder

28
network.py
View File

@ -98,10 +98,20 @@ class Network:
def print_variables(self):
variables = self.model.variables
for k in variables:
print(k)
def eval_state(self, state): def eval_state(self, state):
return self.model(state.reshape(1,-1)) return self.model(state.reshape(1,-1))
def save_model(self, episode_count): def save_model(self, episode_count):
"""
:param episode_count:
:return:
"""
tfe.Saver(self.model.variables).save(os.path.join(self.checkpoint_path, 'model.ckpt')) tfe.Saver(self.model.variables).save(os.path.join(self.checkpoint_path, 'model.ckpt'))
#self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'), global_step=global_step) #self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'), global_step=global_step)
with open(os.path.join(self.checkpoint_path, "episodes_trained"), 'w+') as f: with open(os.path.join(self.checkpoint_path, "episodes_trained"), 'w+') as f:
@ -113,6 +123,8 @@ class Network:
print("[NETWK] ({name}) Saving global step to:".format(name=self.name), print("[NETWK] ({name}) Saving global step to:".format(name=self.name),
os.path.join(self.checkpoint_path, 'model.ckpt')) os.path.join(self.checkpoint_path, 'model.ckpt'))
f.write(str(self.global_step) + "\n") f.write(str(self.global_step) + "\n")
if self.config['verbose']:
self.print_variables()
def calc_vals(self, states): def calc_vals(self, states):
@ -150,6 +162,8 @@ class Network:
with open(global_step_path, 'r') as f: with open(global_step_path, 'r') as f:
self.config['global_step'] = int(f.read()) self.config['global_step'] = int(f.read())
if self.config['verbose']:
self.print_variables()
@ -489,19 +503,13 @@ class Network:
in self.config['eval_methods'] ] in self.config['eval_methods'] ]
return outcomes return outcomes
def train_model(self, episodes=1000, save_step_size=100, trained_eps=0): def train_model(self, episodes=1000, save_step_size=100, trained_eps=0):
with tf.Session() as sess: with tf.Session() as sess:
difference_in_vals = 0 difference_in_vals = 0
self.restore_model() self.restore_model()
#variables_names = [v.name for v in tf.trainable_variables()]
#values = sess.run(variables_names)
#for k, v in zip(variables_names, values):
# print("Variable: ", k)
# print("Shape: ", v.shape)
# print(v)
start_time = time.time() start_time = time.time()
def print_time_estimate(eps_completed): def print_time_estimate(eps_completed):
@ -537,9 +545,13 @@ class Network:
difference_in_vals += abs((cur_board_value - self.eval_state(self.board_trans_func(prev_board, player)))) difference_in_vals += abs((cur_board_value - self.eval_state(self.board_trans_func(prev_board, player))))
if self.config['verbose']:
print("Difference in values:", difference_in_vals)
print("Current board value :", cur_board_value)
print("Current board is :\n",cur_board)
# adjust weights # adjust weights
#print(cur_board)
if Board.outcome(cur_board) is None: if Board.outcome(cur_board) is None:
self.do_backprop(self.board_trans_func(prev_board, player), cur_board_value) self.do_backprop(self.board_trans_func(prev_board, player), cur_board_value)
player *= -1 player *= -1