def __init__(self, training_set, topology, lr, epochs, batch_size, activation_functions):
self.training_set = training_set
self.topology = topology
self.lr = lr
self.epochs = epochs
self.batch_size = batch_size
self.activation_functions = activation_functions
self.train_boards = None
self.train_moves = None
# Different number of in nodes because of different test set
if self.training_set == 1:
self.input = 16
elif self.training_set == 2:
self.input = 16
elif self.training_set == 3:
self.input = 19
elif self.training_set == 4:
self.input = 22
elif self.training_set == 5:
self.input = 29
self.net = ANN(self.input, self.topology, activation_functions, 4, lr)
class NNplayer(object):
# Initialises values and takes parameters from user
def __init__(self, training_set, topology, lr, epochs, batch_size, activation_functions):
self.training_set = training_set
self.topology = topology
self.lr = lr
self.epochs = epochs
self.batch_size = batch_size
self.activation_functions = activation_functions
self.train_boards = None
self.train_moves = None
# Different number of in nodes because of different test set
if self.training_set == 1:
self.input = 16
elif self.training_set == 2:
self.input = 16
elif self.training_set == 3:
self.input = 19
elif self.training_set == 4:
self.input = 22
elif self.training_set == 5:
self.input = 29
self.net = ANN(self.input, self.topology, activation_functions, 4, lr)
# Loads the different test cases and scales the ones that have not been scaled when they were made
def load_test_cases(self):
if self.training_set == 1:
f = open("myfile1.txt", "r")
elif self.training_set == 2:
f = open("Gradient16dim.txt", "r")
elif self.training_set == 3:
f = open("Gradient19dim.txt", "r")
elif self.training_set == 4:
f = open("Gradient22dim.txt", "r")
elif self.training_set == 5:
f = open("Gradient30dim.txt", "r")
boards = []
moves = []
lines = f.readlines()
for line in lines:
case = line.split("$")
board = case[0]
move = case[1]
board = board.split(",")
move = move.strip("\n").split(",")
boards.append(board)
moves.append(move)
boards = np.asarray(boards, dtype=np.double)
if self.training_set < 3:
scale(boards)
moves = np.array(moves)
# Sets assignes the set to the objects variables
self.train_boards = boards
self.train_moves = moves
# Trains batches of size "minibatch_size" for x epochs.
def train_model(self, epochs, minibatch_size):
for epoch in range(epochs):
print("Training epoch number {}...".format(epoch))
cost = 0
for i in range(0, len(self.train_boards), minibatch_size):
board_batch = self.train_boards[i : i + minibatch_size]
move_batch = self.train_moves[i : i + minibatch_size]
cost += self.net.train(board_batch, move_batch)
print("Cost after epoch {}: {}".format(epoch, cost / len(self.train_boards)))
