hidden_layer_topology = [int(raw) for raw in raw_topology.split(";")]
pba = PowerBoardAnn(51, hidden_layer_topology, 4)
pba.read_pickle()
error_rates = pba.do_training(no_of_epochs)
start_time = time.time()
ann_best_tiles = []
random_best_tiles = []
for i in range(50):
pb = PowerBoard((4, 4))
pb.add_random_tile()
while True:
if pb.is_game_over():
pb.print_to_console()
ann_best_tiles.append(max(pb.get_board()))
break
network_output = pba.evaluate_one_board(
convert_list_to_2d(pb.get_board()))
pb.move_and_add_random_tile(directions[network_output])
"""
possible_directions = pb.get_possible_move_directions()
for choice in network_output:
if directions[choice] in possible_directions:
pb.move_and_add_random_tile(directions[choice])
break
"""
for i in range(50):
pb = PowerBoard((4, 4))
hidden_layer_topology = [int(raw) for raw in raw_topology.split(";")]
pba = PowerBoardAnn(51, hidden_layer_topology, 4)
pba.read_pickle()
error_rates = pba.do_training(no_of_epochs)
start_time = time.time()
ann_best_tiles = []
random_best_tiles = []
for i in range(50):
pb = PowerBoard((4, 4))
pb.add_random_tile()
while True:
if pb.is_game_over():
pb.print_to_console()
ann_best_tiles.append(max(pb.get_board()))
break
network_output = pba.evaluate_one_board(convert_list_to_2d(pb.get_board()))
pb.move_and_add_random_tile(directions[network_output])
"""
possible_directions = pb.get_possible_move_directions()
for choice in network_output:
if directions[choice] in possible_directions:
pb.move_and_add_random_tile(directions[choice])
break
"""
for i in range(50):
pb = PowerBoard((4, 4))
pb.add_random_tile()
