def main():
ValidSampler = Sampler(utils.valid_file)
TestSampler = Sampler(utils.test_file)
networks = []
weights = []
for i in xrange(5):
if i == 0:
TrainSampler = Sampler(utils.train_file)
prev_ys = np.copy(TrainSampler.labels)
else:
TrainSampler = Sampler(utils.train_file, prev_ys)
network = Network()
network.train(TrainSampler)
cur_ys = network.predict(TrainSampler)
b1 = np.sum(np.multiply(cur_ys, prev_ys))
b2 = np.sum(np.multiply(cur_ys, cur_ys))
w = float(b1) / b2
prev_ys = np.subtract(prev_ys, w * cur_ys)
print i, 'done with weight', w
network.save('network_' + str(i) + '.ckpt')
weights.append(w)
networks.append(network)
validate_boost(ValidSampler, networks, weights)
validate_boost(TestSampler, networks, weights)
np.save('weights.npy', weights)
def main():
ValidSampler = Sampler(utils.valid_file)
TestSampler = Sampler(utils.test_file)
networks = []
weights = []
for i in xrange(5):
if i == 0:
TrainSampler = Sampler(utils.train_file)
prev_ys = np.copy(TrainSampler.labels)
else:
TrainSampler = Sampler(utils.train_file, prev_ys)
network = Network()
network.train(TrainSampler)
cur_ys = network.predict(TrainSampler)
b1 = np.sum(np.multiply(cur_ys, prev_ys))
b2 = np.sum(np.multiply(cur_ys, cur_ys))
w = float(b1) / b2
prev_ys = np.subtract(prev_ys, w * cur_ys)
print i, 'done with weight', w
network.save('network_' + str(i) + '.ckpt')
weights.append(w)
networks.append(network)
validate_boost(ValidSampler, networks, weights)
validate_boost(TestSampler, networks, weights)
np.save('weights.npy', weights)
def test(a, b):
white = Network(a)
black = Network(b)
print("training")
white.train()
print("playing")
board = chess.Board()
for i in range(10):
print(i)
board.reset()
while not board.is_game_over():
move = play(board, white, True)
if board.is_game_over():
break
board.push(move)
move = play(board, black, False)
board.push(move)
print(board.result())
activation='relu',
pooling=False,
name='conv3')
net.add_conv(patch_size=3,
in_depth=32,
out_depth=32,
activation='relu',
pooling=True,
name='conv4')
# 4 = 两次 pooling, 每一次缩小为 1/2
# 32 = conv4 out_depth
net.add_fc(in_num_nodes=(image_size // 4) * (image_size // 4) * 32,
out_num_nodes=128,
activation='relu',
name='fc1')
net.add_fc(in_num_nodes=128, out_num_nodes=10, activation=None, name='fc2')
net.define_model()
# net.run(train_samples, train_labels, test_samples, test_labels, train_data_iterator=train_data_iterator,
# iteration_steps=3000, test_data_iterator=test_data_iterator)
net.train(train_samples,
train_labels,
data_iterator=train_data_iterator,
iteration_steps=2000)
net.test(test_samples, test_labels, data_iterator=test_data_iterator)
else:
raise Exception(
'main.py: Should Not Be Imported!!! Must Run by "python main.py"')