def __init__(self, layers, up_weights=None, down_weights=None):
'''Initializes a BN from the layers given'''
self.numlayers = len(layers)
down_layers = [layer.__class__.from_layer(layer) for layer in layers[::-1]]#Copy list so that upnet and downnet layers are different objects
self.upnet = NeuralNet(layers, up_weights)
self.downnet = NeuralNet(down_layers, down_weights)
def main():
parser = argparse.ArgumentParser(description='Deblur image')
parser.add_argument('-d','--data', help='Input data image')
parser.add_argument('-o','--output', help='Output image')
#START: Stage 0 load the image
args = parser.parse_args()
input_name = args.data
img = cv2.imread(input_name)
fig = plt.figure()
ax1 = fig.add_subplot(2,2,1)
ax1.imshow(img)
#Stage 1 add padding
img,y,x = add_padding(img)
#Stage 2 openCV optimization
#Stage 3 Machine Learning Deblur
model = NeuralNet()
img = model.predict(img)
#Stage 4 More openCV
#End
ax2 = fig.add_subplot(2,2,2)
img = remove_padding(img,x,y)
ax2.imshow(img)
plt.show()
def get_net(self):
net = NeuralNet(self.layer_spec, self.activation_function,
self.regularisation_coefficient)
if self.persistence_id:
persist = Persist(self.persistence_id)
if persist.exists():
persist.load(net)
print 'Loaded network from', persist.get_filename()
return net
from_type3 = type3[0:n_inputs]
from_type3_targets = type3_targets[0:n_inputs]
from_type4 = type4[0:n_inputs]
from_type4_targets = type4_targets[0:n_inputs]
inputs = torch.cat((from_type1, from_type2, from_type3, from_type4), 0 )
targets = torch.cat( (from_type1_targets,from_type2_targets,from_type3_targets,from_type4_targets) ,0)
args = dict()
args['n_inputs'] = n_inputs
args['n_neurons'] = n_neurons
# Model
net = NeuralNet(hidden_neurons=args['n_neurons'])
criterion = nn.MSELoss(reduction="mean")
optimizer = optim.SGD(net.parameters(), lr=learning_rate)
hold_loss=[]
EPOCHS = math.ceil(K /(n_inputs * 4))
# prog_bar = Bar('Training...', suffix='%(percent).1f%% - %(eta)ds - %(index)d / %(max)d', max=EPOCHS )
# Train loop
for epoch in range(0, EPOCHS):
running_loss = 0.0
# Batch gradient descent
optimizer.zero_grad()
output = net(inputs)
loss = criterion(output, targets)
def train_alphazero(lr, dropout, num_channels, epochs, batch_size,
replay_buffer_size, temp_decrese_moves, mcts_rollouts,
n_episodes_per_iteration, eval, model, test):
board = Game(player_turn=1)
network = NeuralNet(board, num_channels, lr, dropout, epochs, batch_size)
if model is not None:
print("Loading {}".format(model))
network.load(model)
if test:
while True:
while board.turn_player() == -1:
move = np.argmax(network(board, board.valid_moves())[0][0])
print("Board {}, move {}".format(board.board(), move))
board.move(move)
print("{}".format(board.board()))
import pdb
pdb.set_trace()
# set up the experiment
experiment = Experiment(
api_key=os.environ.get('ALPHAZERO_API_KEY'),
project_name=os.environ.get('ALPHAZERO_PROJECT_NAME'),
workspace=os.environ.get('ALPHAZERO_WORKSPACE'))
experiment.log_multiple_params({
'lr':
lr,
'dropout':
dropout,
'num_channels':
num_channels,
'epochs':
epochs,
'batch_size':
batch_size,
'replay_buffer_size':
replay_buffer_size,
'temp_decrese_moves':
temp_decrese_moves,
'mcts_rollouts':
mcts_rollouts,
'n_episodes_per_iteration':
n_episodes_per_iteration
})
buf = ReplayBuffer(replay_buffer_size, batch_size)
epoch = 0
while True:
epoch += 1
print("Epoch {}, {}".format(epoch, time.clock()))
for i in range(n_episodes_per_iteration):
winner = execute_episode(network, buf, experiment)
print("Finished episode {}, winner {}, time {}".format(
i, winner, time.clock()))
network.clone()
loss, entropy = train_network(network, buf, experiment)
print("Training loss: {}, entropy: {}".format(loss, entropy))
won_counter = evaluate_network(network, board, 10)
if won_counter >= 5:
print("Performance improved, {} games won".format(won_counter))
network.save()
else:
print("Performance decreased, {} games won".format(won_counter))
network.revert_network()
def main():
nn = NeuralNet(64,20,2)
nn.train(PATTERNS, iterations=1000)
print nn.show(TESTS)
