def train_from_directory(self, directory, learning_rate, max_epochs):
"""
Train a neural network based on a set of game logs located in a single directory
@param directory: the path to the directory containing the 2048 log files
@type directory: str
@param learning_rate: The learning rate (between 0 and 1)
@type learning_rate: float
@param max_epochs: The maximum number of epochs (cycles)
@type max_epochs: int
"""
all_x = None
all_y = None
for filename in os.listdir(directory):
if filename.endswith(".log"):
print("OK - File parsed: {}".format(
os.path.join(directory, filename)))
game = Game.load_game(os.path.join(directory, filename),
display_grid=False)
x, y = self.parse_inputs_outputs_for_neural_net(game)
if all_x is None:
all_x = x
all_y = y
else:
all_x = np.concatenate((all_x, x))
all_y = np.concatenate((all_y, y))
else:
print("NOK - File not parsed: {}".format(
os.path.join(directory, filename)))
self.train(all_x, all_y, learning_rate, max_epochs)
def replay_game(self):
"""
Method to visualize a 2048 game from log file
"""
self.mode = Modes.MODE_REPLAY
self.mode_text.set("REPLAY")
self.mode_label.configure(bg="red")
filepath = filedialog.askopenfilename(initialdir=".", title="Select file",
filetypes=(("2048 replay files", "*.log"), ("all files", "*.*")))
if filepath != '':
self.game = Game.load_game(filepath)
t_str_state = self.game.history.grid_history[0]
try:
self.grid.grid = Grid.from_string(t_str_state, self.nb_rows, self.nb_columns)
self.update_grid()
except ValueError:
print("Incorrect matrix dimensions!")
else:
self.start_new_game()