def prepare_data(data_path, w2v_path, vocab_path, make_vocab=True,
elmo_w2v_path=None,
elmo_pca=False):
[anchor,
check,
label,
anchor_len,
check_len] = data_utils.read_data(data_path,
"train",
cut_tool,
data_clearner_api,
"tab")
if make_vocab:
dic = data_utils.make_dic(anchor+check)
if not elmo_w2v_path:
data_utils.read_pretrained_embedding(w2v_path, dic, vocab_path, min_freq=3)
else:
data_utils.read_pretrained_elmo_embedding(w2v_path, dic,
vocab_path, min_freq=3,
elmo_embedding_path=elmo_w2v_path,
elmo_pca=elmo_pca)
if sys.version_info < (3, ):
embedding_info = pkl.load(open(os.path.join(vocab_path), "rb"))
else:
embedding_info = pkl.load(open(os.path.join(vocab_path), "rb"),
encoding="iso-8859-1")
return [anchor, check, label, anchor_len, check_len, embedding_info]
def test(args, vocab_size):
device = t.device('cuda') if args.use_gpu else t.device('cpu')
beam_size = args.beam_size
topk = args.topk
rev_model = args.load_model_path
# print(rev_model)
model = Seq2Seq(embed_size=args.embed_size,
enc_dec_output_size=args.enc_dec_output_size,
attn_size=args.attn_size,
num_layers=args.num_layers,
bidirectional=args.bidirectional,
use_gpu=args.use_gpu,
vocab_size=vocab_size).to(device)
assert rev_model is not None
# 读取已经保存的模型
rev_path = os.path.join(model_dir, rev_model)
if os.path.exists(rev_path):
print('read in model from', rev_path)
model.load(load_path=rev_path)
batch_size = args.batch_size
test_set = Set(read_data(args.test_data_root))
test_loader = Loader(test_set,
batch_size,
shuffle=False,
use_gpu=args.use_gpu,
num_workers=args.num_workers).loader
model.eval()
with t.no_grad():
recorder.epoch_start(0, 'test', len(test_set))
for batch_id, batch in enumerate(test_loader):
encoder_inputs, seq_len, decoder_inputs, weights = batch
encoder_inputs = encoder_inputs.to(device)
seq_len = seq_len.to(device)
decoder_inputs = decoder_inputs.to(device)
weights = weights.to(device)
encoder_inputs.to(device)
logits, output_symbols = model(
encoder_inputs,
seq_len,
decoder_inputs[:, :-1],
mode='test',
max_len=args.max_len,
beam_search=False if args.beam_size == 1 else True,
beam_size=args.beam_size,
topk=args.topk)
nll_loss = compute_loss(logits, decoder_inputs[:, 1:], weights)
ppl = perplexity(nll_loss)
recorder.batch_end(batch_id, batch_size, nll_loss, ppl)
recorder.log_text(encoder_inputs.tolist(),
decoder_inputs[:, 1:].tolist(),
output_symbols.tolist())
recorder.epoch_end()
def main(argv):
# random.seed(21) # So we have same parition every time.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Read in data from .pickle as a list of (features, label) tuples
# each representing a zipcode datapoint.
data_and_labels = data_utils.read_data()
need = np.sum([1 if label[1] == 0 else 0 for label in data_and_labels])
print('need', need)
# Oversample
# data_and_labels = data_utils.oversample(data_and_labels)
# data_and_labels = data_utils.undersample(data_and_labels)
# Standardize the data.
x_data = [x[0] for x in data_and_labels]
y_data = [x[1] for x in data_and_labels]
scaler = preprocessing.StandardScaler()
x_data = scaler.fit_transform(x_data)
# New dataset that is standardized.
data = [(x_data[i], y_data[i]) for i in range(len(x_data))]
# Separate 80/10/10 as train/val/test partition.
data_size = len(data)
random.shuffle(data)
train_data = data[:(data_size // 10) * 8]
val_data = data[(data_size // 10) * 8 : (data_size // 10) * 9]
test_data = data[(data_size // 10) * 9 :]
train_data = data_utils.oversample_train(train_data)
print(len(train_data), 'training points.')
print(len(val_data), 'validation points.')
print(len(test_data), 'testing points.')
input_dim = len(data[0][0]) # number of features
output_dim = 2 # two classes: food desert and not food desert
hidden_dim_list = [16, 36, 36, 24]
model_nn = FoodDesertClassifier(input_dim, hidden_dim_list,
output_dim).to(device)
loss = optimize_nn(model_nn, train_data, val_data, test_data)
eval_model_nn(model_nn, loss, test_data, "Testing")
def prepare_data(data_path, w2v_path, vocab_path):
import time
[anchor, check, label, anchor_len,
check_len] = data_utils.read_data(data_path, "train", cut_tool,
data_clearner_api, "tab")
dic = data_utils.make_dic(anchor + check)
data_utils.read_pretrained_embedding(w2v_path, dic, vocab_path, min_freq=3)
if sys.version_info < (3, ):
embedding_info = pkl.load(open(os.path.join(vocab_path), "rb"))
else:
embedding_info = pkl.load(open(os.path.join(vocab_path), "rb"),
encoding="iso-8859-1")
return [anchor, check, label, anchor_len, check_len, embedding_info]
def train(args, vocab_size):
# opt._parse(kwarg)
print('enter train func')
device = t.device('cuda') if args.use_gpu else t.device('cpu')
model = Seq2Seq(embed_size=args.embed_size,
enc_dec_output_size=args.enc_dec_output_size,
attn_size=args.attn_size,
num_layers=args.num_layers,
bidirectional=args.bidirectional,
use_gpu=args.use_gpu,
vocab_size=vocab_size).to(device)
print('Model structure')
print(model)
print('The model has %d parameters' % count_parameters(model))
if args.load_model_path is not None:
rev_path = os.path.join(model_dir, args.load_model_path)
if os.path.exists(rev_path):
print('read in model from', rev_path)
last_epoch = model.load(load_path=rev_path,
return_list=['epoch'])[0]
start_epoch = last_epoch + 1
else:
start_epoch = 1
last_epoch = -1
optimizer = Adam(model.parameters(), lr=args.lr)
if args.scheduler_type == 'exponential':
scheduler = lr_scheduler.ExponentialLR(optimizer,
gamma=args.exponential_lr_decay,
last_epoch=last_epoch)
elif args.scheduler_type == 'step':
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.step_size,
gamma=args.step_lr_decay)
print('read in data')
# 读取数据
batch_size = args.batch_size
train_set = Set(read_data(args.train_data_root))
valid_set = Set(read_data(args.valid_data_root))
# 构造dataloader
train_loader = Loader(train_set,
batch_size,
shuffle=True,
use_gpu=args.use_gpu,
num_workers=args.num_workers).loader
valid_loader = Loader(valid_set,
batch_size,
shuffle=False,
use_gpu=args.use_gpu,
num_workers=args.num_workers).loader
# 统计数据量
print('data scale:')
print('train data:', len(train_set), "batch_nums:", len(train_loader))
print('valid data:', len(valid_set), "batch_nums:", len(valid_loader))
# train
print('start training...')
epochs = args.max_epoch
for epoch in range(start_epoch, epochs + 1):
model.train()
# epoch开始前记录
recorder.epoch_start(epoch, 'train', len(train_set))
if args.scheduler_type is not None:
print(epoch, 'lr={:.10f}'.format(scheduler.get_lr()[0]))
for batch_id, batch in enumerate(train_loader):
encoder_inputs, seq_len, decoder_inputs, weights = batch
encoder_inputs = encoder_inputs.to(device)
seq_len = seq_len.to(device)
decoder_inputs = decoder_inputs.to(device)
weights = weights.to(device)
encoder_inputs.to(device)
optimizer.zero_grad()
# 第三个参数， 最长的句子最后一个token为EOS_I，不需要作为输入，这样可以减少一些计算
logits, output_symbols = model(
encoder_inputs,
seq_len,
decoder_inputs[:, :-1],
mode='train',
max_len=None,
teacher_forcing_ratio=args.teacher_forcing_ratio)
# print('train out',output_symbols)
# 计算损失
nll_loss = compute_loss(logits, decoder_inputs[:, 1:], weights)
# 计算困惑度
ppl = perplexity(nll_loss)
# print(nll_loss.item(), ppl.item())
# 反向传播，更新参数
nll_loss.backward()
# 减轻梯度爆炸 小trick
nn.utils.clip_grad_norm_(model.parameters(),
args.max_gradient_norm)
optimizer.step()
recorder.batch_end(batch_id, batch_size, nll_loss, ppl)
if args.scheduler_type is not None:
scheduler.step()
recorder.epoch_end()
# 保存模型
if epoch % 5 == 0:
model.save(os.path.join(
model_dir,
f'{args.project}_{datetime.datetime.now().strftime("%y_%m_%d_%H:%M:%S")}_{nll_loss.item()}_{ppl.item()}'
),
epoch=epoch)
# 训练一轮后，在验证集上计算loss, ppl
model.eval()
with t.no_grad():
recorder.epoch_start(epoch, 'eval', len(valid_set))
for batch_id, batch in enumerate(valid_loader):
encoder_inputs, seq_len, decoder_inputs, weights = batch
encoder_inputs = encoder_inputs.to(device)
seq_len = seq_len.to(device)
decoder_inputs = decoder_inputs.to(device)
weights = weights.to(device)
encoder_inputs.to(device)
logits, output_symbols = model(
encoder_inputs,
seq_len,
decoder_inputs[:, :-1],
mode='eval',
max_len=args.max_len,
beam_search=False if args.beam_size == 1 else True,
beam_size=args.beam_size,
topk=args.topk)
# print('eval out: ', output_symbols)
nll_loss = compute_loss(logits, decoder_inputs[:, 1:], weights)
ppl = perplexity(nll_loss)
recorder.batch_end(batch_id, batch_size, nll_loss, ppl)
recorder.log_text(encoder_inputs.tolist(),
decoder_inputs[:, 1:].tolist(),
output_symbols.tolist())
recorder.epoch_end()
parser.add_argument("--z_dim", type=int, default=32)
parser.add_argument("--seq_len", type=int, default=10)
parser.add_argument("--epochs", type=int, default=100)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--prt_evry", type=int, default=1)
parser.add_argument("--save_evry", type=int, default=10)
parser.add_argument("--lr", type=float, default=1e-3)
config = parser.parse_args()
device = th.device('cuda' if th.cuda.is_available() else 'cpu')
dir_name = mk_dir(config.data + 'experiment')
print(config, "DEVICE", device)
data = read_data('data/pianorolls/{}.pkl'.format(config.data))
train_data, test_data = data2seq(data=data,
split='train',
seq_len=config.seq_len)
if config.model == "VRNN":
model = VRNN(config, device)
else:
print("NotImplementedERROR")
model.to(device)
epoch = 0
while (epoch < config.epochs):
w2v_path = "/data/xuht/Chinese_w2v/sgns.merge.char/sgns.merge.char.pkl"
# vocab_path = "/data/xuht/duplicate_sentence/ChineseSTSCorpus/emb_mat.pkl"
vocab_path = "/data/xuht/duplicate_sentence/LCQMC/emb_mat.pkl"
data_clearner_api = data_clean.DataCleaner({})
cut_tool = data_utils.cut_tool_api()
import time
[train_anchor,
train_check,
train_label,
train_anchor_len,
train_check_len] = data_utils.read_data(train_data_path,
"train",
cut_tool,
data_clearner_api,
"tab")
dic = data_utils.make_dic(train_anchor+train_check)
data_utils.read_pretrained_embedding(w2v_path, dic, vocab_path, min_freq=3)
if sys.version_info < (3, ):
embedding_info = pkl.load(open(os.path.join(vocab_path), "rb"))
else:
embedding_info = pkl.load(open(os.path.join(vocab_path), "rb"),
encoding="iso-8859-1")
token2id = embedding_info["token2id"]
id2token = embedding_info["id2token"]
embedding_mat = embedding_info["embedding_matrix"]