def eval(tag_path, corpus_path):
correct = 0
total = 0
acc_list = []
model_name = MODEL_NAME
embedding_dim = EMBEDDING_DIM
hidden_dim = HIDDEN_DIM
word_to_ix = WORD_TO_IX
model = BiLSTM(len(word_to_ix), 5, embedding_dim, hidden_dim)
checkpoint = torch.load(model_name)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
tag_to_ix = {'1': 0, '2': 1, '3': 2, '4': 3, '5': 4}
sentences, tags = load_train_data(tag_path, corpus_path)
labels = torch.tensor([[tag_to_ix[tag]] for tag in tags[:]])
with torch.no_grad():
for i, sen in enumerate(tqdm(sentences[:])):
input = prepare_sequence(sen, word_to_ix)
output = model(input)
_, predicted = torch.max(output.data, 1)
label = labels[i]
total += label.size(0)
correct += (predicted == label).sum().item()
acc = round(100 * correct / total, 2)
acc_list.append(acc)
assert len(acc_list) == len(sentences)
final_acc = acc
plt.plot(list(range(len(tags))), acc_list)
plt.xlabel('pred_num')
plt.ylabel('accuracy / %')
plt.show()
return final_acc
def train_bilstm(self):
# Load Data
pre_processor = PreProcessor(file_path=TEST_DATA_PATH)
sentences, entities = pre_processor.run()
n_test_data = len(sentences)
test_generator = generate_data_by_batch(
x=sentences,
y=entities,
n_classes=pre_processor.n_entities + 1,
entity_to_index=pre_processor.entity_to_index,
batch_size=BATCH_SIZE
)
bilstm = BiLSTM(n_class=pre_processor.n_entities + 1)
bilstm.load()
# Saving model with `model.save()` doesn't store custom loss or metrics function. Model has to be stored
# separately into "config" and "weight" file and loaded from both. This causes an essential step of compiling
# before evaluating. I think this issue exist from keras 2.0.
# https://github.com/keras-team/keras/issues/5916
bilstm.model.compile(
optimizer="nadam",
loss="categorical_crossentropy",
metrics=["accuracy", custom_f1, custom_precision, custom_recall]
)
bilstm.model.evaluate_generator(
test_generator,
steps=n_test_data//BATCH_SIZE,
verbose=1,
)
def initialize_model(gpu, vocab_size, v_vec, emb_requires_grad, args):
emb_dim = args.emb_dim
h_dim = None
class_num = 2
is_gpu = True
if gpu == -1:
is_gpu = False
if args.emb_type == 'ELMo' or args.emb_type == 'ELMoForManyLangs':
bilstm = BiLSTM(emb_dim,
h_dim,
class_num,
vocab_size,
is_gpu,
v_vec,
emb_type=args.emb_type,
elmo_model_dir=args.emb_path)
elif args.emb_type == 'None':
bilstm = BiLSTM(emb_dim,
h_dim,
class_num,
vocab_size,
is_gpu,
v_vec,
emb_type=args.emb_type)
else:
bilstm = BiLSTM(emb_dim,
h_dim,
class_num,
vocab_size,
is_gpu,
v_vec,
emb_type=args.emb_type)
if is_gpu:
bilstm = bilstm.cuda()
for m in bilstm.modules():
print(m.__class__.__name__)
weights_init(m)
if args.emb_type != 'ELMo' and args.emb_type != 'ELMoForManyLangs' and args.emb_type != 'None':
for param in bilstm.word_embed.parameters():
param.requires_grad = emb_requires_grad
return bilstm
def __init__(self, config):
self.config = config
self.output_path = os.path.join(self.config.BASE_DIR,
self.config.output_path)
self.w2ix, self.ix2t = self.load_vocab() # 加载索引字典
self.vocab_size = len(self.w2ix)
self.sequence_length = self.config.sequence_length
self.model = BiLSTM(self.config, self.vocab_size)
self.load_graph()
def __init__(self, trainer_params, args):
self.args = args
self.trainer_params = trainer_params
random.seed(trainer_params.random_seed)
torch.manual_seed(trainer_params.random_seed)
if args.cuda:
torch.cuda.manual_seed_all(trainer_params.random_seed)
self.train_data = seq_mnist_train(trainer_params)
self.val_data = seq_mnist_val(trainer_params)
self.train_loader = DataLoader(self.train_data, batch_size=trainer_params.batch_size, \
shuffle=True, num_workers=trainer_params.num_workers)
self.val_loader = DataLoader(self.val_data, batch_size=trainer_params.test_batch_size, \
shuffle=False, num_workers=trainer_params.num_workers)
self.starting_epoch = 1
self.prev_loss = 10000
self.model = BiLSTM(trainer_params)
self.criterion = wp.CTCLoss(size_average=False)
self.labels = [i for i in range(trainer_params.num_classes - 1)]
self.decoder = seq_mnist_decoder(labels=self.labels)
self.optimizer = optim.Adam(self.model.parameters(),
lr=trainer_params.lr)
if args.cuda:
torch.cuda.set_device(args.gpus)
self.model = self.model.cuda()
self.criterion = self.criterion.cuda()
if args.resume or args.eval or args.export:
print("Loading model from {}".format(args.resume))
package = torch.load(args.resume,
map_location=lambda storage, loc: storage)
self.model.load_state_dict(package['state_dict'])
self.optimizer.load_state_dict(package['optim_dict'])
self.starting_epoch = package['starting_epoch']
self.prev_loss = package['prev_loss']
if args.cuda:
for state in self.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
if args.init_bn_fc_fusion:
if not trainer_params.prefused_bn_fc:
self.model.batch_norm_fc.init_fusion()
self.trainer_params.prefused_bn_fc = True
else:
raise Exception("BN and FC are already fused.")
def predict(sentence):
sentence = sentence.split()
model_name = BEST_NAME
embedding_dim = EMBEDDING_DIM
hidden_dim = HIDDEN_DIM
word_to_ix = WORD_TO_IX
model = BiLSTM(len(word_to_ix), 5, embedding_dim, hidden_dim)
checkpoint = torch.load(model_name)
model.load_state_dict(checkpoint['model_state_dict'])
input = prepare_sequence(sentence, word_to_ix)
with torch.no_grad():
output = model(input)
print(output)
_, predicted = torch.max(output.data, 1)
print(predicted)
def get_time_to_score(tsv_path, thing, model_path):
time_to_count = {}
time_to_scoresum = {}
if thing == 'hair_dryer':
id = '732252283'
elif thing == 'microwave':
id = '423421857'
else:
id = '246038397'
with open('train_' + thing + '_word_to_ix.json', 'r') as j:
word_to_ix = json.load(j)
embedding_dim = EMBEDDING_DIM
hidden_dim = HIDDEN_DIM
model = BiLSTM(len(word_to_ix), 5, embedding_dim, hidden_dim)
checkpoints = torch.load(model_path)
model.load_state_dict(checkpoints['model_state_dict'])
model.eval()
with open(tsv_path, 'r') as f:
reader = csv.reader(f, delimiter='\t')
for i, r in enumerate(reader):
if i == 0 or r[4] != id:
continue
month, _, year = r[14].split('/')
if year not in {'2014', '2015'}:
continue
time = get_idx_by_year_month(int(year), int(month))
if time < 8:
continue
sen = (r[12] + ' ' + r[13]).lower()
sen = re.sub(r'[^A-Za-z0-9,.!]+', ' ', sen)
input = prepare_sequence(sen.split(), word_to_ix)
with torch.no_grad():
output = model(input)
_, predicted = torch.max(output.data, 1)
pred_score = predicted.item()
if time not in time_to_count:
time_to_count[time] = 0
time_to_scoresum[time] = 0.
time_to_count[time] += 1
time_to_scoresum[time] += pred_score
time_to_scoremean = {}
for time in time_to_count.keys():
time_to_scoremean[time] = time_to_scoresum[time] / time_to_count[time]
print(time_to_count)
return time_to_scoremean
def main():
X_train, Y_train, X_valid, Y_valid, timestamp, close_prices = load_data(
'data.csv', TIME_WINDOW)
[X_train, Y_train, X_valid, Y_valid] = [
torch.from_numpy(i.astype(np.float32))
for i in [X_train, Y_train, X_valid, Y_valid]
]
model = BiLSTM(feature_num=FEATURE_NUM, time_window=TIME_WINDOW - 1)
dataset_train = torch.utils.data.TensorDataset(X_train, Y_train)
dataset_valid = torch.utils.data.TensorDataset(X_valid, Y_valid)
train_dataloader = torch.utils.data.DataLoader(dataset=dataset_train,
batch_size=BATCH_SIZE,
shuffle=False)
valid_dataloader = torch.utils.data.DataLoader(dataset=dataset_valid,
batch_size=BATCH_SIZE,
shuffle=False)
min_loss = train(model, train_dataloader, valid_dataloader)
print(f'Best trained model has a loss of {min_loss:.5f}.')
def model_load_test(test_df,
vocab_file,
embeddings_file,
pretrained_file,
test_prediction_dir,
test_prediction_name,
mode,
num_labels=2,
max_length=50,
gpu_index=0,
batch_size=128):
device = torch.device(
"cuda:{}".format(gpu_index) if torch.cuda.is_available() else "cpu")
print(20 * "=", " Preparing for testing ", 20 * "=")
if platform == "linux" or platform == "linux2":
checkpoint = torch.load(pretrained_file)
else:
checkpoint = torch.load(pretrained_file, map_location=device)
# Retrieving model parameters from checkpoint.
embeddings = load_embeddings(embeddings_file)
print("\t* Loading test data...")
test_data = My_Dataset(test_df, vocab_file, max_length, mode)
test_loader = DataLoader(test_data, shuffle=False, batch_size=batch_size)
print("\t* Building model...")
model = BiLSTM(embeddings,
num_labels=num_labels,
max_length=max_length,
device=device).to(device)
model.load_state_dict(checkpoint["model"])
print(20 * "=", " Testing BiLSTM model on device: {} ".format(device),
20 * "=")
batch_time, total_time, accuracy, predictions = test(model, test_loader)
print(
"\n-> Average batch processing time: {:.4f}s, total test time: {:.4f}s, accuracy: {:.4f}%\n"
.format(batch_time, total_time, (accuracy * 100)))
test_prediction = pd.DataFrame({'prediction': predictions})
if not os.path.exists(test_prediction_dir):
os.makedirs(test_prediction_dir)
test_prediction.to_csv(os.path.join(test_prediction_dir,
test_prediction_name),
index=False)
def initialize_model(gpu, vocab_size, v_vec, dropout_ratio, n_layers, model,
statistics_of_each_case_type):
is_gpu = True
if gpu == -1:
is_gpu = False
if model == 'Base' or model == 'FT':
bilstm = BiLSTM(vocab_size, v_vec, dropout_ratio, n_layers, gpu=is_gpu)
elif model == 'OneH':
bilstm = OneHot(vocab_size, v_vec, dropout_ratio, n_layers, gpu=is_gpu)
elif model == 'FA':
bilstm = FeatureAugmentation(vocab_size,
v_vec,
dropout_ratio,
n_layers,
gpu=is_gpu)
elif model == 'CPS':
bilstm = ClassProbabilityShift(
vocab_size,
v_vec,
dropout_ratio,
n_layers,
statistics_of_each_case_type=statistics_of_each_case_type,
gpu=is_gpu)
elif model == 'MIX':
bilstm = Mixture(
vocab_size,
v_vec,
dropout_ratio,
n_layers,
statistics_of_each_case_type=statistics_of_each_case_type,
gpu=is_gpu)
if is_gpu:
bilstm = bilstm.cuda()
for m in bilstm.modules():
print(m.__class__.__name__)
weights_init(m)
return bilstm
embeddings = gensim.models.KeyedVectors.load_word2vec_format('../embeddings/german.model', binary=True)
print("Done.")
# loop through each word in embeddings
for word in embeddings.vocab:
if word.lower() in words:
vector = embeddings.wv[word]
word_embeddings.append(vector)
word2Idx[word] = len(word2Idx)
word_embeddings = np.array(word_embeddings)
print(f"Found embeddings for {word_embeddings.shape[0]} of {len(words)} words.")
train_sentences = format_to_tensor(train_sentences, word2Idx, label2Idx)
model = BiLSTM(word_embeddings=torch.FloatTensor(word_embeddings), num_classes=len(labels))
model.train()
epochs = 50
learning_rate = 0.015
momentum = 0.9
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
def eval():
correct = 0
total = 0
for tokens, true_labels in train_sentences:
total += len(true_labels)
def train_model(args,
train_text=None,
train_labels=None,
eval_text=None,
eval_labels=None,
tokenizer=None):
textattack.shared.utils.set_seed(args.random_seed)
_make_directories(args.output_dir)
num_gpus = torch.cuda.device_count()
# Save logger writes to file
log_txt_path = os.path.join(args.output_dir, "log.txt")
fh = logging.FileHandler(log_txt_path)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
logger.info(f"Writing logs to {log_txt_path}.")
train_examples_len = len(train_text)
# label_id_len = len(train_labels)
label_set = set(train_labels)
args.num_labels = len(label_set)
logger.info(
f"Loaded dataset. Found: {args.num_labels} labels: {sorted(label_set)}"
)
if len(train_labels) != len(train_text):
raise ValueError(
f"Number of train examples ({len(train_text)}) does not match number of labels ({len(train_labels)})"
)
if len(eval_labels) != len(eval_text):
raise ValueError(
f"Number of teste xamples ({len(eval_text)}) does not match number of labels ({len(eval_labels)})"
)
if args.model == "gru":
textattack.shared.logger.info(
"Loading textattack model: GRUForClassification")
model = BiGRU()
model.to(device)
elif args.model == "lstm":
textattack.shared.logger.info(
"Loading textattack model: LSTMForClassification")
model = BiLSTM()
model.to(device)
# attack_class = attack_from_args(args)
# We are adversarial training if the user specified an attack along with
# the training args.
# adversarial_training = (attack_class is not None) and (not args.check_robustness)
# multi-gpu training
if num_gpus > 1:
model = torch.nn.DataParallel(model)
logger.info("Using torch.nn.DataParallel.")
logger.info(f"Training model across {num_gpus} GPUs")
num_train_optimization_steps = (
int(train_examples_len / args.batch_size / args.grad_accum_steps) *
args.num_train_epochs)
if args.model == "lstm" or args.model == "cnn" or args.model == "gru":
def need_grad(x):
return x.requires_grad
optimizer = torch.optim.Adam(filter(need_grad, model.parameters()),
lr=args.learning_rate)
scheduler = None
else:
param_optimizer = list(model.named_parameters())
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
"weight_decay":
0.01,
},
{
"params": [
p for n, p in param_optimizer
if any(nd in n for nd in no_decay)
],
"weight_decay":
0.0,
},
]
optimizer = transformers.optimization.AdamW(
optimizer_grouped_parameters, lr=args.learning_rate)
scheduler = transformers.optimization.get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_proportion,
num_training_steps=num_train_optimization_steps,
)
# Start Tensorboard and log hyperparams.
from torch.utils.tensorboard import SummaryWriter
tb_writer = SummaryWriter(args.output_dir)
# Use Weights & Biases, if enabled.
if args.enable_wandb:
global wandb
wandb = textattack.shared.utils.LazyLoader("wandb", globals(), "wandb")
wandb.init(sync_tensorboard=True)
# Save original args to file
args_save_path = os.path.join(args.output_dir, "train_args.json")
_save_args(args, args_save_path)
logger.info(f"Wrote original training args to {args_save_path}.")
tb_writer.add_hparams(
{k: v
for k, v in vars(args).items() if _is_writable_type(v)}, {})
# Start training
logger.info("***** Running training *****")
# if augmenter:
# logger.info(f"\tNum original examples = {train_examples_len}")
# logger.info(f"\tNum examples after augmentation = {len(train_text)}")
# else:
# logger.info(f"\tNum examples = {train_examples_len}")
logger.info(f"\tNum examples = {train_examples_len}")
logger.info(f"\tBatch size = {args.batch_size}")
logger.info(f"\tMax sequence length = {args.max_length}")
logger.info(f"\tNum steps = {num_train_optimization_steps}")
logger.info(f"\tNum epochs = {args.num_train_epochs}")
logger.info(f"\tLearning rate = {args.learning_rate}")
eval_dataloader = _make_dataloader(tokenizer, eval_text, eval_labels,
args.batch_size)
train_dataloader = _make_dataloader(tokenizer, train_text, train_labels,
args.batch_size)
global_step = 0
tr_loss = 0
model.train()
args.best_eval_score = 0
args.best_eval_score_epoch = 0
args.epochs_since_best_eval_score = 0
def loss_backward(loss):
if num_gpus > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
if args.grad_accum_steps > 1:
loss = loss / args.grad_accum_steps
loss.backward()
return loss
# if args.do_regression:
# # TODO integrate with textattack `metrics` package
# loss_fct = torch.nn.MSELoss()
# else:
# loss_fct = torch.nn.CrossEntropyLoss()
loss_fct = torch.nn.CrossEntropyLoss()
for epoch in tqdm.trange(int(args.num_train_epochs),
desc="Epoch",
position=0,
leave=True):
# if adversarial_training:
# if epoch >= args.num_clean_epochs:
# if (epoch - args.num_clean_epochs) % args.attack_period == 0:
# # only generate a new adversarial training set every args.attack_period epochs
# # after the clean epochs
# logger.info("Attacking model to generate new training set...")
# adv_attack_results = _generate_adversarial_examples(
# model_wrapper, attack_class, list(zip(train_text, train_labels))
# )
# adv_train_text = [r.perturbed_text() for r in adv_attack_results]
# train_dataloader = _make_dataloader(
# tokenizer, adv_train_text, train_labels, args.batch_size
# )
# else:
# logger.info(f"Running clean epoch {epoch+1}/{args.num_clean_epochs}")
prog_bar = tqdm.tqdm(train_dataloader,
desc="Iteration",
position=0,
leave=True)
# Use these variables to track training accuracy during classification.
correct_predictions = 0
total_predictions = 0
for step, batch in enumerate(prog_bar):
ids1, ids2, msk1, msk2, labels = batch
# input_ids, labels = batch
labels = labels.to(device)
# if isinstance(input_ids, dict):
# ## dataloader collates dict backwards. This is a workaround to get
# # ids in the right shape for HuggingFace models
# input_ids = {
# k: torch.stack(v).T.to(device) for k, v in input_ids.items()
# }
# logits = model(**input_ids)[0]
# else:
ids1 = ids1.to(device)
ids2 = ids2.to(device)
msk1 = msk1.to(device)
msk2 = msk2.to(device)
logits = model(ids1, ids2, msk1, msk2)
# if args.do_regression:
# # TODO integrate with textattack `metrics` package
# loss = loss_fct(logits.squeeze(), labels.squeeze())
# else:
loss = loss_fct(logits, labels)
pred_labels = logits.argmax(dim=-1)
correct_predictions += (pred_labels == labels).sum().item()
total_predictions += len(pred_labels)
loss = loss_backward(loss)
tr_loss += loss.item()
if global_step % args.tb_writer_step == 0:
tb_writer.add_scalar("loss", loss.item(), global_step)
if scheduler is not None:
tb_writer.add_scalar("lr",
scheduler.get_last_lr()[0],
global_step)
else:
tb_writer.add_scalar("lr", args.learning_rate, global_step)
if global_step > 0:
prog_bar.set_description(f"Loss {tr_loss/global_step}")
if (step + 1) % args.grad_accum_steps == 0:
optimizer.step()
if scheduler is not None:
scheduler.step()
optimizer.zero_grad()
# Save model checkpoint to file.
if (global_step > 0 and (args.checkpoint_steps > 0)
and (global_step % args.checkpoint_steps) == 0):
_save_model_checkpoint(model, args.output_dir, global_step)
# Inc step counter.
global_step += 1
# Print training accuracy, if we're tracking it.
if total_predictions > 0:
train_acc = correct_predictions / total_predictions
logger.info(f"Train accuracy: {train_acc*100}%")
tb_writer.add_scalar("epoch_train_score", train_acc, epoch)
# Check accuracy after each epoch.
# skip args.num_clean_epochs during adversarial training
# if (not adversarial_training) or (epoch >= args.num_clean_epochs):
if (epoch >= args.num_clean_epochs):
eval_score = _get_eval_score(model, eval_dataloader, False)
tb_writer.add_scalar("epoch_eval_score", eval_score, epoch)
if args.checkpoint_every_epoch:
_save_model_checkpoint(model, args.output_dir,
args.global_step)
logger.info(
f"Eval {'pearson correlation' if args.do_regression else 'accuracy'}: {eval_score*100}%"
)
if eval_score > args.best_eval_score:
args.best_eval_score = eval_score
args.best_eval_score_epoch = epoch
args.epochs_since_best_eval_score = 0
_save_model(model, args.output_dir, args.weights_name,
args.config_name)
logger.info(
f"Best acc found. Saved model to {args.output_dir}.")
_save_args(args, args_save_path)
logger.info(f"Saved updated args to {args_save_path}")
else:
args.epochs_since_best_eval_score += 1
if (args.early_stopping_epochs >
0) and (args.epochs_since_best_eval_score >
args.early_stopping_epochs):
logger.info(
f"Stopping early since it's been {args.early_stopping_epochs} steps since validation acc increased"
)
break
if args.check_robustness:
samples_to_attack = list(zip(eval_text, eval_labels))
samples_to_attack = random.sample(samples_to_attack, 1000)
adv_attack_results = _generate_adversarial_examples(
model_wrapper, attack_class, samples_to_attack)
attack_types = [r.__class__.__name__ for r in adv_attack_results]
attack_types = collections.Counter(attack_types)
adv_acc = 1 - (attack_types["SkippedAttackResult"] /
len(adv_attack_results))
total_attacks = (attack_types["SuccessfulAttackResult"] +
attack_types["FailedAttackResult"])
adv_succ_rate = attack_types[
"SuccessfulAttackResult"] / total_attacks
after_attack_acc = attack_types["FailedAttackResult"] / len(
adv_attack_results)
tb_writer.add_scalar("robustness_test_acc", adv_acc, global_step)
tb_writer.add_scalar("robustness_total_attacks", total_attacks,
global_step)
tb_writer.add_scalar("robustness_attack_succ_rate", adv_succ_rate,
global_step)
tb_writer.add_scalar("robustness_after_attack_acc",
after_attack_acc, global_step)
logger.info(f"Eval after-attack accuracy: {100*after_attack_acc}%")
# read the saved model and report its eval performance
logger.info(
"Finished training. Re-loading and evaluating model from disk.")
model_wrapper = model_from_args(args, args.num_labels)
model = model_wrapper.model
model.load_state_dict(
torch.load(os.path.join(args.output_dir, args.weights_name)))
eval_score = _get_eval_score(model, eval_dataloader, args.do_regression)
logger.info(
f"Saved model {'pearson correlation' if args.do_regression else 'accuracy'}: {eval_score*100}%"
)
if args.save_last:
_save_model(model, args.output_dir, args.weights_name,
args.config_name)
# end of training, save tokenizer
try:
tokenizer.save_pretrained(args.output_dir)
logger.info(f"Saved tokenizer {tokenizer} to {args.output_dir}.")
except AttributeError:
logger.warn(
f"Error: could not save tokenizer {tokenizer} to {args.output_dir}."
)
# Save a little readme with model info
write_readme(args, args.best_eval_score, args.best_eval_score_epoch)
_save_args(args, args_save_path)
tb_writer.close()
logger.info(f"Wrote final training args to {args_save_path}.")
with tf.Graph().as_default():
session = tf.Session()
with session.as_default():
# Define training procedure
with tf.variable_scope('embedding'):
embedding = tf.get_variable(
'embedding',
shape=word_embedding.shape,
dtype=tf.float32,
initializer=tf.constant_initializer(word_embedding),
trainable=True)
model = BiLSTM(FLAGS.seq_length, FLAGS.hidden_size, FLAGS.layer_num,
FLAGS.class_num, FLAGS.learning_rate,
FLAGS.l2_reg_lambda)
train_writer = tf.summary.FileWriter(FLAGS.log_path + '/train',
session.graph)
dev_writer = tf.summary.FileWriter(FLAGS.log_path + '/dev',
session.graph)
merged = tf.summary.merge_all()
saver = tf.train.Saver()
session.run(tf.global_variables_initializer())
session.run(tf.local_variables_initializer())
# training loop, for each batch
for step in range(FLAGS.epochs_num):
def main(args):
print "Running BiLSTM model"
print args
random.seed(args.seed)
trainset = []
devset = []
print >> sys.stderr, "Loading dataset.."
assert(os.path.isdir(args.datapath))
word_vocab = []
for fname in sorted(os.listdir(args.datapath)):
if os.path.isdir(fname):
continue
#if fname.endswith('train.ner.txt'):
if fname.endswith('.ppi.txt'):
print fname
dataset, vocab = load_dataset(os.path.join(args.datapath,fname))
word_vocab += vocab
trainset += dataset
print >> sys.stderr, "Loaded {} instances with a vocab size of {} from {}".format(len(dataset),len(vocab),fname)
print "Loaded {} instances from data set".format(len(trainset))
word_vocab = sorted(set(word_vocab))
vocab_cache = os.path.join(args.datapath,'word_vocab.ner.txt')
with open(vocab_cache,'w') as f:
print "Saved vocab to", vocab_cache
pickle.dump(word_vocab,f)
embeddings = load_embeddings(args.embeddings_path, word_vocab, 200)
labels = ['B-MISC','I-MISC','O']
model_name = 'saved_model_autumn'
if not os.path.exists('{}/scratch'.format(args.datapath)):
os.mkdir('{}/scratch'.format(args.datapath))
if os.path.exists('{}/{}'.format(args.datapath,model_name)):
os.rename('{}/{}'.format(args.datapath,model_name),
'{}/{}_{}'.format(args.datapath,model_name,int(time.time())))
os.mkdir('{}/{}'.format(args.datapath,model_name))
for j in range(num_ensembles):
m = BiLSTM(labels=labels,
word_vocab=word_vocab,
word_embeddings=embeddings,
optimizer=args.optimizer,
embedding_size=200,
char_embedding_size=32,
lstm_dim=200,
num_cores=args.num_cores,
embedding_factor=args.embedding_factor,
learning_rate=args.learning_rate,
decay_rate=args.decay_rate,
dropout_keep=args.keep_prob)
training_samples = random.sample(trainset,len(trainset)/2)
cut = int(0.8 * len(training_samples))
X_train, y_train = zip(*training_samples[:cut])
X_dev, y_dev = zip(*training_samples[cut:])
print "Training on {}, tuning on {}".format(len(X_train),len(X_dev))
m.fit(X_train, y_train, X_dev, y_dev,
num_iterations=args.num_iterations,
num_it_per_ckpt=args.num_it_per_ckpt,
batch_size=args.batch_size,
seed=j, fb2=True)
save_path = '{}/{}/model_{}'.format(args.datapath,model_name,j)
m.save(save_path)
print "Saved model {} to {}".format(j,save_path)
if FLAGS.mode != 'distil' :
#创建词表
word2idx, idx2word, vocab_path = create_vocabulary(FLAGS.vocab_size)
create_data_ids(word2idx)
else:
#创建词表（增强数据集）
word2idx, idx2word, vocab_path = create_vocabulary_distil(FLAGS.vocab_size)
create_data_ids_distil(word2idx)
if not tf.gfile.Exists(FLAGS.model_save_dir):
tf.gfile.MakeDirs(FLAGS.model_save_dir)
#创建模型对象
model = BiLSTM(vocab_size=FLAGS.vocab_size,
batch_size=FLAGS.batch_size,
embedding_size=FLAGS.num_embedding_units,
num_hidden_size=FLAGS.num_hidden_units,
maxlen=FLAGS.maxlen)
#创建训练对象
solver = Solver(model=model,
training_iter=FLAGS.train_step,
word2idx=word2idx,
idx2word=idx2word,
log_dir=FLAGS.log_dir,
model_save_dir=FLAGS.model_save_dir)
if FLAGS.mode == 'train':
solver.train()
elif FLAGS.mode == 'test':
solver.test()
elif FLAGS.mode=='distil':
model_name, 'epochs',
str(args.epochs), args.optimizer, 'lr',
str(args.lr), 'hidden',
str(args.hidden), 'layers',
str(args.layers)
]
model_name = '_'.join(model_name)
model_path = os.path.join(save_model_dir, model_name)
print('writer_path:', writer_path)
print('save_model_dir:', save_model_dir)
print('model_name:', model_name)
if args.crf:
model = LSTM_CRF(args.hidden, args.layers, args.dropout)
else:
model = BiLSTM(args.hidden, 8, args.dropout, args.layers)
criterion = nn.CrossEntropyLoss()
if args.load_model:
model.load_state_dict(torch.load(model_path))
if args.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
else:
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum)
lr_lambda = lambda epoch: 1 / (1 + (epoch + 1) * args.lr_decay)
scheduler = LambdaLR(optimizer, lr_lambda=lr_lambda)
if use_cuda:
model = model.cuda()
if not args.crf:
return epoch_loss / len(valid_it), epoch_acc / len(valid_it)
vocab_size = len(TEXT.vocab)
emb_dim = 50
hidden_dim = 50
out_dim = 1
lr = 1e-2
nlayers = 2
bidir = True
dropout = 0.3
model = BiLSTM(vocab_size,
hidden_dim,
emb_dim,
out_dim,
bsize,
nlayers,
bidir,
dropout,
gpu=gpu)
n_filters = 3
filter_sizes = [3, 4, 5]
modelc = CNN(vocab_size, emb_dim, n_filters, filter_sizes, out_dim, dropout)
optimizer = optim.Adam(model.parameters()) #no need to specify LR for adam
lossf = nn.BCEWithLogitsLoss()
ep = 5
modelatt = LSTMAttn(vocab_size, hidden_dim, emb_dim, out_dim, bsize, gpu=gpu)
def main(options):
use_cuda = (len(options.gpuid) >= 1)
if options.gpuid:
cuda.set_device(options.gpuid[0])
train, dev, test, vocab = torch.load(open(options.data_file, 'rb'),
pickle_module=dill)
batched_train, batched_train_mask, _ = utils.tensor.advanced_batchize(
train, options.batch_size, vocab.stoi["<pad>"])
batched_dev, batched_dev_mask, _ = utils.tensor.advanced_batchize(
dev, options.batch_size, vocab.stoi["<pad>"])
vocab_size = len(vocab)
if options.load_file:
rnnlm = torch.load(options.load_file)
else:
rnnlm = BiLSTM(vocab_size)
if use_cuda > 0:
rnnlm.cuda()
else:
rnnlm.cpu()
criterion = torch.nn.NLLLoss()
optimizer = eval("torch.optim." + options.optimizer)(rnnlm.parameters(),
options.learning_rate)
# main training loop
last_dev_avg_loss = float("inf")
rnnlm.train()
for epoch_i in range(options.epochs):
logging.info("At {0}-th epoch.".format(epoch_i))
# srange generates a lazy sequence of shuffled range
for i, batch_i in enumerate(utils.rand.srange(len(batched_train))):
train_batch = Variable(
batched_train[batch_i]) # of size (seq_len, batch_size)
train_mask = Variable(batched_train_mask[batch_i])
if use_cuda:
train_batch = train_batch.cuda()
train_mask = train_mask.cuda()
sys_out_batch = rnnlm(
train_batch
) # (seq_len, batch_size, vocab_size) # TODO: substitute this with your module
train_in_mask = train_mask.view(-1)
train_in_mask = train_in_mask.unsqueeze(1).expand(
len(train_in_mask), vocab_size)
train_out_mask = train_mask.view(-1)
sys_out_batch = sys_out_batch.view(-1, vocab_size)
train_out_batch = train_batch.view(-1)
sys_out_batch = sys_out_batch.masked_select(train_in_mask).view(
-1, vocab_size)
train_out_batch = train_out_batch.masked_select(train_out_mask)
loss = criterion(sys_out_batch, train_out_batch)
logging.debug("loss at batch {0}: {1}".format(i, loss.data[0]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# validation -- this is a crude esitmation because there might be some paddings at the end
dev_loss = 0.0
rnnlm.eval()
for batch_i in range(len(batched_dev)):
dev_batch = Variable(batched_dev[batch_i], volatile=True)
dev_mask = Variable(batched_dev_mask[batch_i], volatile=True)
if use_cuda:
dev_batch = dev_batch.cuda()
dev_mask = dev_mask.cuda()
sys_out_batch = rnnlm(dev_batch)
dev_in_mask = dev_mask.view(-1)
dev_in_mask = dev_in_mask.unsqueeze(1).expand(
len(dev_in_mask), vocab_size)
dev_out_mask = dev_mask.view(-1)
sys_out_batch = sys_out_batch.view(-1, vocab_size)
dev_out_batch = dev_batch.view(-1)
sys_out_batch = sys_out_batch.masked_select(dev_in_mask).view(
-1, vocab_size)
dev_out_batch = dev_out_batch.masked_select(dev_out_mask)
loss = criterion(sys_out_batch, dev_out_batch)
dev_loss += loss
dev_avg_loss = dev_loss / len(batched_dev)
logging.info(
"Average loss value per instance is {0} at the end of epoch {1}".
format(dev_avg_loss.data[0], epoch_i))
#if (last_dev_avg_loss - dev_avg_loss).data[0] < options.estop:
# logging.info("Early stopping triggered with threshold {0} (previous dev loss: {1}, current: {2})".format(epoch_i, last_dev_avg_loss.data[0], dev_avg_loss.data[0]))
# break
torch.save(
rnnlm,
open(
options.model_file +
".nll_{0:.2f}.epoch_{1}".format(dev_avg_loss.data[0], epoch_i),
'wb'),
pickle_module=dill)
last_dev_avg_loss = dev_avg_loss
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model',
type=str,
default='rnn',
help=
"Available models are: 'rnn', 'cnn', 'bilstm', 'fasttext', and 'distilbert'\nDefault is 'rnn'"
)
parser.add_argument('--train_data_path',
type=str,
default="./data/train_clean.csv",
help="Path to the training data")
parser.add_argument('--test_data_path',
type=str,
default="./data/dev_clean.csv",
help="Path to the test data")
parser.add_argument('--seed', type=int, default=1234)
parser.add_argument('--vectors',
type=str,
default='fasttext.simple.300d',
help="""
Pretrained vectors:
Visit
https://github.com/pytorch/text/blob/9ce7986ddeb5b47d9767a5299954195a1a5f9043/torchtext/vocab.py#L146
for more
""")
parser.add_argument('--max_vocab_size', type=int, default=750)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--bidirectional', type=bool, default=True)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--hidden_dim', type=int, default=64)
parser.add_argument('--output_dim', type=int, default=1)
parser.add_argument('--n_layers', type=int, default=2)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--n_epochs', type=int, default=5)
parser.add_argument('--n_filters', type=int, default=100)
parser.add_argument('--filter_sizes', type=list, default=[3, 4, 5])
args = parser.parse_args()
torch.manual_seed(args.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
########## BILSTM ##########
if args.model == "bilstm":
print('\nBiLSTM')
TEXT = Field(tokenize='spacy')
LABEL = LabelField(dtype=torch.float)
data_fields = [("text", TEXT), ("label", LABEL)]
train_data = TabularDataset(args.train_data_path,
format='csv',
fields=data_fields,
skip_header=True,
csv_reader_params={'delimiter': ","})
test_data = TabularDataset(args.test_data_path,
format='csv',
fields=data_fields,
skip_header=True,
csv_reader_params={'delimiter': ","})
train_data, val_data = train_data.split(split_ratio=0.8,
random_state=random.seed(
args.seed))
TEXT.build_vocab(train_data,
max_size=args.max_vocab_size,
vectors=args.vectors,
unk_init=torch.Tensor.normal_)
LABEL.build_vocab(train_data)
train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
(train_data, val_data, test_data),
batch_size=args.batch_size,
sort_key=lambda x: len(x.text),
device=device)
input_dim = len(TEXT.vocab)
embedding_dim = get_embedding_dim(args.vectors)
pad_idx = TEXT.vocab.stoi[TEXT.pad_token]
unk_idx = TEXT.vocab.stoi[TEXT.unk_token]
model = BiLSTM(input_dim, embedding_dim, args.hidden_dim,
args.output_dim, args.n_layers, args.bidirectional,
args.dropout, pad_idx)
pretrained_embeddings = TEXT.vocab.vectors
model.embedding.weight.data.copy_(pretrained_embeddings)
model.embedding.weight.data[unk_idx] = torch.zeros(embedding_dim)
model.embedding.weight.data[pad_idx] = torch.zeros(embedding_dim)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.BCEWithLogitsLoss()
model.to(device)
criterion.to(device)
best_valid_loss = float('inf')
print("\nTraining...")
print("===========")
for epoch in range(1, args.n_epochs + 1):
start_time = time.time()
train_loss, train_acc = train(model, train_iterator, optimizer,
criterion)
valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(),
'./checkpoints/{}-model.pt'.format(args.model))
print(
f'[Epoch: {epoch:02}] | Epoch Time: {epoch_mins}m {epoch_secs}s'
)
print(
f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%'
)
print(
f'\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%'
)
model.load_state_dict(
torch.load('./checkpoints/{}-model.pt'.format(args.model)))
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print('\nEvaluating...')
print("=============")
print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%'
) # Test Loss: 0.139, Test Acc: 95.27%
########## VANILLA RNN ##########
else:
print('\nVanilla RNN')
TEXT = Field(tokenize='spacy')
LABEL = LabelField(dtype=torch.float)
data_fields = [("text", TEXT), ("label", LABEL)]
train_data = TabularDataset(args.train_data_path,
format='csv',
fields=data_fields,
skip_header=True,
csv_reader_params={'delimiter': ","})
test_data = TabularDataset(args.test_data_path,
format='csv',
fields=data_fields,
skip_header=True,
csv_reader_params={'delimiter': ","})
train_data, val_data = train_data.split(split_ratio=0.8,
random_state=random.seed(
args.seed))
TEXT.build_vocab(train_data,
max_size=args.max_vocab_size,
vectors=args.vectors)
LABEL.build_vocab(train_data)
train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
(train_data, val_data, test_data),
batch_size=args.batch_size,
sort_key=lambda x: len(x.text),
device=device)
input_dim = len(TEXT.vocab)
embedding_dim = get_embedding_dim(args.vectors)
model = RNN(input_dim, embedding_dim, args.hidden_dim, args.output_dim)
pretrained_embeddings = TEXT.vocab.vectors
model.embedding.weight.data.copy_(pretrained_embeddings)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.BCEWithLogitsLoss()
model.to(device)
criterion.to(device)
best_valid_loss = float('inf')
print("\nTraining...")
print("===========")
for epoch in range(1, args.n_epochs + 1):
start_time = time.time()
train_loss, train_acc = train(model, train_iterator, optimizer,
criterion)
valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(),
'./checkpoints/{}-model.pt'.format(args.model))
print(
f'[Epoch: {epoch:02}] | Epoch Time: {epoch_mins}m {epoch_secs}s'
)
print(
f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%'
)
print(
f'\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%'
)
model.load_state_dict(
torch.load('./checkpoints/{}-model.pt'.format(args.model)))
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print('\nEvaluating...')
print("=============")
print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%'
) # Test Loss: 0.138, Test Acc: 95.05%
print(str(datetime.now()), "Generating vocab")
vocab = Vocab(train_colors,
min_count=min_count,
add_padding=True,
add_bos=True,
add_eos=True)
embeddings = nn.Embedding(len(vocab.index2token),
embedding_size,
padding_idx=vocab.PAD.hash)
model = BiLSTM(
embeddings=embeddings,
hidden_size=hidden_size,
num_labels=len(vocab), #num_labels,
bidirectional=bidirectional,
num_layers=num_layers,
color_representation_size=54) #54)
model_id = str(int(time.time())) + "w_fourier"
save_path = os.path.join(output_path, model_id)
if not os.path.isdir(save_path):
os.makedirs(save_path)
writer = SummaryWriter(save_path)
if cuda:
model.cuda()
print(model)
def model_train_validate_test(train_df,
dev_df,
test_df,
embeddings_file,
vocab_file,
target_dir,
mode,
num_labels=2,
max_length=50,
epochs=50,
batch_size=128,
lr=0.0005,
patience=5,
max_grad_norm=10.0,
gpu_index=0,
if_save_model=False,
checkpoint=None):
device = torch.device(
"cuda:{}".format(gpu_index) if torch.cuda.is_available() else "cpu")
print(20 * "=", " Preparing for training ", 20 * "=")
# 保存模型的路径
if not os.path.exists(target_dir):
os.makedirs(target_dir)
# -------------------- Data loading ------------------- #
print("\t* Loading training data...")
train_data = My_Dataset(train_df, vocab_file, max_length, mode)
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)
print("\t* Loading validation data...")
dev_data = My_Dataset(dev_df, vocab_file, max_length, mode)
dev_loader = DataLoader(dev_data, shuffle=True, batch_size=batch_size)
print("\t* Loading test data...")
test_data = My_Dataset(test_df, vocab_file, max_length, mode)
test_loader = DataLoader(test_data, shuffle=False, batch_size=batch_size)
# -------------------- Model definition ------------------- #
print("\t* Building model...")
if (embeddings_file is not None):
embeddings = load_embeddings(embeddings_file)
else:
embeddings = None
model = BiLSTM(embeddings, num_labels=num_labels, device=device).to(device)
total_params = sum(p.numel() for p in model.parameters())
print(f'{total_params:,} total parameters.')
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print(f'{total_trainable_params:,} training parameters.')
# -------------------- Preparation for training ------------------- #
criterion = nn.CrossEntropyLoss()
# 过滤出需要梯度更新的参数
parameters = filter(lambda p: p.requires_grad, model.parameters())
# optimizer = optim.Adadelta(parameters, params["LEARNING_RATE"])
optimizer = torch.optim.Adam(parameters, lr=lr)
# optimizer = torch.optim.Adam(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode="max",
factor=0.85,
patience=0)
best_score = 0.0
start_epoch = 1
# Data for loss curves plot
epochs_count = []
train_losses = []
valid_losses = []
# Continuing training from a checkpoint if one was given as argument
if checkpoint:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint["epoch"] + 1
best_score = checkpoint["best_score"]
print("\t* Training will continue on existing model from epoch {}...".
format(start_epoch))
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
epochs_count = checkpoint["epochs_count"]
train_losses = checkpoint["train_losses"]
valid_losses = checkpoint["valid_losses"]
# Compute loss and accuracy before starting (or resuming) training.
_, valid_loss, valid_accuracy, _, = validate(model, dev_loader, criterion)
print("\t* Validation loss before training: {:.4f}, accuracy: {:.4f}%".
format(valid_loss, (valid_accuracy * 100)))
# -------------------- Training epochs ------------------- #
print("\n", 20 * "=", "Training BiLSTM model on device: {}".format(device),
20 * "=")
patience_counter = 0
for epoch in range(start_epoch, epochs + 1):
epochs_count.append(epoch)
print("* Training epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy = train(model, train_loader,
optimizer, criterion,
epoch, max_grad_norm)
train_losses.append(epoch_loss)
print("-> Training time: {:.4f}s, loss = {:.4f}, accuracy: {:.4f}%".
format(epoch_time, epoch_loss, (epoch_accuracy * 100)))
print("* Validation for epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy, _, = validate(
model, dev_loader, criterion)
valid_losses.append(epoch_loss)
print("-> Valid. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n".
format(epoch_time, epoch_loss, (epoch_accuracy * 100)))
# Update the optimizer's learning rate with the scheduler.
scheduler.step(epoch_accuracy)
# Early stopping on validation accuracy.
if epoch_accuracy < best_score:
patience_counter += 1
else:
best_score = epoch_accuracy
patience_counter = 0
if (if_save_model):
torch.save(
{
"epoch": epoch,
"model": model.state_dict(),
"best_score": best_score,
"epochs_count": epochs_count,
"train_losses": train_losses,
"valid_losses": valid_losses
}, os.path.join(target_dir, "best.pth.tar"))
print("save model succesfully!\n")
print("* Test for epoch {}:".format(epoch))
_, _, test_accuracy, predictions = validate(
model, test_loader, criterion)
print("Test accuracy: {:.4f}%\n".format(test_accuracy))
test_prediction = pd.DataFrame({'prediction': predictions})
test_prediction.to_csv(os.path.join(target_dir,
"test_prediction.csv"),
index=False)
if patience_counter >= patience:
print("-> Early stopping: patience limit reached, stopping...")
break
import tensorflow as tf
from model import BiLSTM
vocab_size = 10
num_classes_A = 5
num_classes_B = 2
data = tf.placeholder(tf.int32, [None, None])
target_A = tf.placeholder(tf.float32, [None, None, num_classes_A])
target_B = tf.placeholder(tf.float32, [None, None, num_classes_B])
current_target = tf.placeholder(tf.string)
model = BiLSTM(data, target_A, target_B, vocab_size)
x = [[0, 4, 1, 5, 9], [3, 4, 6, 2, 7]]
y_A = [[[1, 0, 0, 0, 0], [0, 0, 1, 0, 0], [1, 0, 0, 0, 0], [0, 0, 1, 0, 0],
[0, 0, 0, 0, 1]],
[[0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 1, 0, 0, 0],
[0, 0, 0, 1, 0]]]
y_B = [[[0, 1], [0, 1], [0, 1], [1, 0], [1, 0]],
[[0, 1], [0, 1], [1, 0], [0, 1], [1, 0]]]
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(10):
print('-' * 20)
print(sess.run(model.cost_A, feed_dict={data: x, target_A: y_A}))
print(sess.run(model.cost_B, feed_dict={data: x, target_B: y_B}))
shuffle=False)
novel_iter = Iterator(novel,
batch_size=1,
device=-1,
sort=False,
sort_within_batch=False,
repeat=False,
shuffle=False)
vocab = TEXT.vocab
vocab_size = len(vocab)
emb_dim = 300
hidden_dim = 64
emb_matrix = vocab.vectors
model = BiLSTM(vocab_size, hidden_dim, emb_dim, emb_matrix)
print("Computing the deep features...")
'''
features = []
for x in tqdm(data_iter):
feature = model(x.comment_text)
features.append(feature)
feats = []
for f in features:
feats.append(f.detach().numpy())
feats = np.vstack(feats)
'''
novel_features = []
for x in tqdm(novel_iter):
if 'cuda' in args.device:
if torch.cuda.is_available():
device = torch.device(args.device)
else:
print("cuda not available...")
print("Using device {}".format(device))
print("loading datasets...")
n = None
train_data = DataSource("train", n=n)
print("loaded {} train data".format(len(train_data)))
dev_data = DataSource("dev", n=n)
print("loaded {} dev data".format(len(dev_data)))
test_data = DataSource("test", n=n)
print("loaded {} test data".format(len(test_data)))
model = BiLSTM(128, device)
print("allocated model")
if args.restore == "":
losses = train()
print("graphing")
graph_losses(losses)
else:
model.load_state_dict(torch.load(args.restore))
print("loaded weights from {}".format(args.restore))
confusion = evaluate()
print(confusion)
print("accuracy: {}".format(np.sum(np.diagonal(confusion))))
shuffle=True)
dev_batches, num_dev_batches, num_dev_samples = get_batch(cfg.data_npy_path, cfg.filename_x_dev,
cfg.filename_y_dev, cfg.epochs,
cfg.maxlen, cfg.len_wv, cfg.batch_size[0],
cfg.num_classes, str(fold),
shuffle=False)
# create a iterator of the correct shape and type
iter = tf.data.Iterator.from_structure(train_batches.output_types, train_batches.output_shapes)
xs, ys = iter.get_next()
train_init_opt = iter.make_initializer(train_batches)
dev_init_opt = iter.make_initializer(dev_batches)
# index+=1
model = BiLSTM(param)
# print('xs')
# print(xs)
# print('ys')
# print(ys)
loss,train_opt,pred_train,train_summaries,global_step,lstm_cell_fw,x_check = model.train(xs,ys)
logits_eval,probs_eval,pred_eval,ys = model.eval(xs,ys)
#Variables for early stop
dev_history = []
dev_best = 0
stop_times = 0
logging.info('# Session')
from model import BiLSTM
from utils import batch_iter, get_data
from vocab import Vocab
from seqeval.metrics import classification_report
from torch import optim
import numpy as np
import torch
x_train, x_valid, x_test, y_train, y_valid, y_test = get_data('time_delay')
train_data = list(zip(x_train, y_train))
vocab = Vocab.from_corpus(x_train)
tag_vocab = Vocab.from_corpus(y_train)
model = BiLSTM(vocab, tag_vocab, 100, 256)
torch.cuda.set_device(0)
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=0.01)
for epoch in range(3):
for sents, labels in batch_iter(train_data, 16):
model.zero_grad()
loss, acc = model(sents, labels)
print("epoch {}:".format(epoch), loss, acc)
loss.backward()
optimizer.step()
test_data = list(zip(x_test, y_test))
preds = []
for sent, labels in test_data:
pred = model.predict([sent])
preds.append(pred.tolist()[0])
preds = [[tag_vocab.id2word[i] for i in sent] for sent in preds]
def __init__(self, config):
self.config = config
self.load_data() # 加载数据集
self.model = BiLSTM(self.config, self.vocab_size,
self.word_vectors) # 初始化模型
mapping_file = ".\\dataset\\map_data.map"
mapping = {}
with open(mapping_file, 'rb') as f:
mapping = cPickle.load(f)
word_to_id = mapping['word_to_id']
tag_to_id = mapping['tag_to_id']
char_to_id = mapping['char_to_id']
word_embeds = mapping['word_embeds']
model = BiLSTM(voca_size=len(word_to_id),
word_emb_dim=100,
pre_word_emb=word_embeds,
char_emb_dim=25,
char_lstm_dim=25,
char_to_ix=char_to_id,
n_cap=4,
cap_emb_dim=8,
hidden_dim=200,
tag_to_ix=tag_to_id)
x = torch.load(model_path)
model.load_state_dict(x())
model.eval()
def test():
test_sentences = loader.load_data(test_path, zeros=False)
loader.update_tag_scheme(test_sentences, 'iob')
# ---- Build Vocabulary ------
w2v_map = data.load_map("resources/w2v_map_SQ.pkl")
w2v_map['<pad>'] = np.zeros(300)
word_to_ix = data.load_map("resources/word_to_ix_SQ.pkl")
label_to_ix = data.load_map("resources/rel_to_ix_SQ.pkl")
vocab_size = len(word_to_ix)
num_classes = len(label_to_ix)
max_sent_length = 36 # set from the paper
# ---- Define Model, Loss, Optim ------
config = args
config.d_out = num_classes
config.n_directions = 2 if config.birnn else 1
print(config)
model = BiLSTM(config)
loss_function = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# ---- Test Model ------
if args.test:
print("Test Mode: loading pre-trained model and testing on test set...")
# model = torch.load(args.resume_snapshot, map_location=lambda storage, location: storage.cuda(args.gpu))
model.load_state_dict(torch.load(args.resume_snapshot))
test_acc = evaluate_dataset_batch(test_set, max_sent_length, model,
w2v_map, label_to_ix)
print("Accuracy: {}".format(test_acc))
sys.exit(0)
# ---- Train Model ------
start = time.time()
import torchvision
from model import BiLSTM
from data import load_dataset
from config import model_name, device
if __name__ == "__main__":
# the string to test!
test_string = "<s> john can"
# ########################
# LOAD DATASET
# ########################
corpus, word_to_idx, idx_to_word, train_dataset = load_dataset()
# ########################
# TEST VARIABLES
# ########################
model = BiLSTM(len(corpus))
model.load_state_dict(torch.load(model_name))
model.eval()
sentence = test_string.split()
sentence = torch.tensor([[word_to_idx[w] for w in sentence]])
s = model.sample(sentence)
print(test_string.split() + s)
