예제 #1
0
def init_model():
    model = VRAE(args.rnn_size,
                 args.rnn_size,
                 args.n_features,
                 args.latent_size,
                 num_drivers,
                 batch_size=args.batch_size)
    model.create_gradientfunctions(x_train, t_train, x_valid, t_valid)
    return model
예제 #2
0
layers['recog_mean'] = F.Linear(n_hidden_recog[-1], n_z)
layers['recog_log_sigma'] = F.Linear(n_hidden_recog[-1], n_z)

# Generating model.
gen_layer_sizes = [(n_z, n_hidden_gen[0])]
gen_layer_sizes += zip(n_hidden_gen[:-1], n_hidden_gen[1:])
gen_layer_sizes += [(n_hidden_gen[-1], train_x.shape[1])]

layers['z'] = F.Linear(n_z, n_hidden_gen[0])
layers['gen_in_h'] = F.Linear(train_x.shape[1], n_hidden_gen[0], nobias=True)
layers['gen_h_h']  = F.Linear(n_hidden_gen[0], n_hidden_gen[0])

layers['output']   = F.Linear(n_hidden_gen[-1], train_x.shape[1])

if args.init_from == "":
    model = VRAE(**layers)
else:
    model = pickle.load(open(args.init_from))

# state pattern
state_pattern = ['recog_h', 'gen_h']

if args.gpu >= 0:
    cuda.init(args.gpu)
    model.to_gpu()


# use Adam
optimizer = optimizers.Adam()
optimizer.setup(model.collect_parameters())
layers['recog_mean'] = F.Linear(n_hidden_recog[-1], n_z)
layers['recog_log_sigma'] = F.Linear(n_hidden_recog[-1], n_z)

# Generating model.
gen_layer_sizes = [(n_z, n_hidden_gen[0])]
gen_layer_sizes += zip(n_hidden_gen[:-1], n_hidden_gen[1:])
gen_layer_sizes += [(n_hidden_gen[-1], train_x.shape[1])]

layers['z'] = F.Linear(n_z, n_hidden_gen[0])
layers['gen_in_h'] = F.Linear(train_x.shape[1], n_hidden_gen[0], nobias=True)
layers['gen_h_h'] = F.Linear(n_hidden_gen[0], n_hidden_gen[0])

layers['output'] = F.Linear(n_hidden_gen[-1], train_x.shape[1])

if args.init_from == "":
    model = VRAE(**layers)
else:
    model = pickle.load(open(args.init_from))

# state pattern
state_pattern = ['recog_h', 'gen_h']

if args.gpu >= 0:
    cuda.init(args.gpu)
    model.to_gpu()

# use Adam
optimizer = optimizers.Adam()
optimizer.setup(model.collect_parameters())

total_losses = np.zeros(n_epochs, dtype=np.float32)
예제 #4
0
    # Retrieved from: http://deeplearning.net/data/mnist/mnist.pkl.gz
    f = gzip.open('mnist.pkl.gz', 'rb')
    (x_train, t_train), (x_valid, t_valid), (x_test, t_test) = cPickle.load(f)
    f.close()
"""

path = "./"

print "instantiating model"
b1 = 0.05
b2 = 0.001
lr = 0.001
batch_size = 100
sigma_init = 0.01
num_inputs = 32000
model = VRAE(hu_encoder, hu_decoder, x_train, n_latent, b1, b2, lr, sigma_init, batch_size)


batch_order = np.arange(int(model.N / model.batch_size))
epoch = 0
LB_list = []

if os.path.isfile(path + "params.pkl"):
    print "Restarting from earlier saved parameters!"
    model.load_parameters(path)
    LB_list = np.load(path + "LB_list.npy")
    epoch = len(LB_list)

if __name__ == "__main__":
    print "iterating"
    while epoch < n_epochs:
예제 #5
0
if __name__ == "__main__":
    args = parse_args()
    save_path = os.path.join(
        "saved_weights",
        datetime.datetime.fromtimestamp(
            time.time()).strftime("%Y-%m-%d_%H:%M:%S"))
    os.makedirs(save_path)
    with open(os.path.join(save_path, 'args.pkl'), 'w') as f:
        pickle.dump(args, f)

    x_train, t_train, r_train, x_valid, t_valid, r_valid = load_data(args)
    num_drivers = np.max(t_train) + 1
    model = VRAE(args.rnn_size,
                 args.rnn_size,
                 args.n_features,
                 args.latent_size,
                 num_drivers,
                 batch_size=args.batch_size,
                 lamda1=args.lamda1,
                 lamda2=args.lamda2)

    batch_order = np.arange(x_train.shape[0] // model.batch_size + 1)
    val_batch_order = np.arange(x_valid.shape[0] // model.batch_size + 1)
    epoch = 0
    LB_list = []

    model.create_gradientfunctions(x_train, t_train, r_train, x_valid, t_valid,
                                   r_valid)

    print("iterating")
    while epoch < args.num_epochs:
        epoch += 1
예제 #6
0
    # Retrieved from: http://deeplearning.net/data/mnist/mnist.pkl.gz
    f = gzip.open('mnist.pkl.gz', 'rb')
    (x_train, t_train), (x_valid, t_valid), (x_test, t_test) = cPickle.load(f)
    f.close()
"""

path = "./"

print "instantiating model"
b1 = 0.05
b2 = 0.001
lr = 0.001
batch_size = 100
sigma_init = 0.01
num_inputs = 32000
model = VRAE(hu_encoder, hu_decoder, x_train, n_latent, b1, b2, lr, sigma_init,
             batch_size)

batch_order = np.arange(int(model.N / model.batch_size))
epoch = 0
LB_list = []

if os.path.isfile(path + "params.pkl"):
    print "Restarting from earlier saved parameters!"
    model.load_parameters(path)
    LB_list = np.load(path + "LB_list.npy")
    epoch = len(LB_list)

if __name__ == "__main__":
    print "iterating"
    while epoch < n_epochs:
        epoch += 1
예제 #7
0
파일: train.py 프로젝트: hjmr/VRAE_Kinect
def train_model():
    args = parse_arg()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    data_set = init_data(args.data_files, device)
    n_input = max([d.data.shape[1] for d in data_set])
    num_data = len(data_set)

    args.input_dims = n_input
    with open('{}/args.pickle'.format(args.output_dir), 'wb') as f:
        pickle.dump(args, f)

    print('# GPU: {}'.format(device))
    print('# dataset num: {}'.format(num_data))
    print('# input dimensions: {}'.format(args.input_dims))
    print('# latent dimensions: {}'.format(args.latent_dims))
    print('# minibatch-size: {}'.format(args.batch_size))
    print('# epoch: {}'.format(args.epoch))
    print('')

    if args.load_model is not None:
        model = torch.load(args.load_model)
        model.eval()
    else:
        model = VRAE(args.input_dims, args.enc_states, args.latent_dims,
                     args.dec_states, args.enc_layers, args.dec_layers,
                     args.dropout_rate).to(device)

    optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)

    num_train = int(num_data * 0.8)
    num_test = num_data - num_train
    train_dat, test_dat = tud.random_split(data_set, [num_train, num_test])

    train_iter = tud.BatchSampler(tud.RandomSampler(range(len(train_dat))),
                                  batch_size=args.batch_size,
                                  drop_last=False)
    test_iter = tud.BatchSampler(tud.SequentialSampler(range(len(test_dat))),
                                 batch_size=args.batch_size,
                                 drop_last=False)

    if args.resume_from_checkpoint is not None:
        checkpoint = torch.load(args.resume_from_checkpoint)
        model.load_state_dict(checkpoint['model_state_dict'])
        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])

    for epoch in range(args.epoch):
        train_loss = 0
        for indices in train_iter:
            x_data, x_len = make_padded_sequence(
                [train_dat[idx] for idx in indices], device)
            optimizer.zero_grad()
            loss = model.loss(x_data, x_len, k=1)
            loss.backward()
            optimizer.step()
            train_loss += loss

        # evaluation
        test_loss = 0
        with torch.no_grad():
            for indices in test_iter:
                x_data, x_len = make_padded_sequence(
                    [test_dat[idx] for idx in indices], device)
                test_loss += model.loss(x_data, x_len, k=10)

        output_log(epoch, train_loss / len(train_iter),
                   test_loss / len(test_iter))

        if (epoch + 1) % args.save_interval == 0:
            checkpoint = {
                'epoch': epoch,
                'model_state_dict': model.state_dict(),
                'optimizer_state_dict': optimizer.state_dict(),
                'train_loss': train_loss,
                'test_loss': test_loss
            }
            checkpoint_path = '{}/{}_{}.checkpoint'.format(
                args.output_dir, args.base_file_name, epoch)
            torch.save(checkpoint, checkpoint_path)

    model_path = '{}/{}_final.model'.format(args.output_dir,
                                            args.base_file_name)
    torch.save(model, model_path)