class Actor:
def __init__(self, game, layers=[], checkpoint=None, format='one_hot', optimizer='adam'):
self.game = game
self.format = format
self.layers = layers
self.optimizer = optimizer
self.network = Network(
[game.state_size(format)] + layers + [game.num_possible_moves()],
[],
minibatch_size=50,
steps=1,
loss_function='cross_entropy',
validation_fraction=0,
test_fraction=0,
learning_rate=0.001,
optimizer=optimizer,
output_functions=[tf.nn.softmax]
)
self.network.build()
if checkpoint:
self.load_checkpoint(checkpoint)
def select_move(self, state, stochastic=False):
possible_moves = self.game.get_moves(state)
formatted_state = self.game.format_for_nn(state, format=self.format)
predictions = self.network.predict([formatted_state])[0]
predictions = predictions[:len(possible_moves)]
if not stochastic:
move = np.argmax(predictions)
return possible_moves[move]
predictions = np.array(predictions)
ps = predictions.sum()
if predictions.sum() == 0:
move = np.random.choice(np.arange(0, len(predictions)))
else:
predictions = predictions / predictions.sum()
move = np.random.choice(np.arange(0, len(predictions)), p=predictions)
return possible_moves[move]
def save_checkpoint(self, checkpoint):
self.network.save(checkpoint)
def load_checkpoint(self, checkpoint):
self.network.load(checkpoint)
from data.dataset_generator import Generator, number_to_index
from nn.network import Network
numbers = [6, 5, 2]
layers = [784, 100, 3]
net = Network(layers, number_to_index(numbers))
# cria um dataset de 60000 imagens, sendo 50000 para treino e 10000 para teste.
# os números são limitados a 6, 5, 2, que são os correspondentes as 3 iniciais:
# generator = Generator(50000, 10000, numbers, 28)
# cria o dataset com 3 imagens de teste correspondentes as 3 iniciais, em numeros
generator = Generator(0, 3, numbers, 28, True)
dataset = generator.gen()
# para treinar:
# net.grad_descent(dataset[:50000], 200, 10, 3.0, test_data=dataset[50000:])
# carrega uma rede já treinada anteriormente:
net.load('pre-trained-data/biases.npy', 'pre-trained-data/weights.npy')
# porcentagem de acerto:
print((net.evaluate(dataset) / 3) * 100)