def train(self, train_data, dev_data):
hparams = self.hparams
sess = self.sess
assert len(train_data[0]) == len(
train_data[1]), "Size of features data must be equal to label"
for epoch in range(hparams.epoch):
info = {}
info['loss'] = []
info['norm'] = []
start_time = time.time()
for idx in range(len(train_data[0]) // hparams.batch_size + 3):
try:
if hparams.steps <= idx:
T = (time.time() - start_time)
self.eval(T, dev_data, hparams, sess)
break
except:
pass
if idx * hparams.batch_size >= len(train_data[0]):
T = (time.time() - start_time)
self.eval(T, dev_data, hparams, sess)
break
batch = train_data[0][idx * hparams.batch_size: \
min((idx + 1) * hparams.batch_size,
len(train_data[0]))]
batch = utils.hash_batch(batch, hparams)
label = train_data[1][idx * hparams.batch_size: \
min((idx + 1) * hparams.batch_size,
len(train_data[1]))]
loss, _, norm = sess.run(
[self.loss, self.update, self.grad_norm], feed_dict= \
{self.features: batch, self.label: label,
self.use_norm: True})
info['loss'].append(loss)
info['norm'].append(norm)
if (idx + 1) % hparams.num_display_steps == 0:
info['learning_rate'] = hparams.learning_rate
info["train_ppl"] = np.mean(info['loss'])
info["avg_grad_norm"] = np.mean(info['norm'])
utils.print_step_info(" ", epoch, idx + 1, info)
del info
info = {}
info['loss'] = []
info['norm'] = []
if (idx + 1) % hparams.num_eval_steps == 0 and dev_data:
T = (time.time() - start_time)
self.eval(T, dev_data, hparams, sess)
self.saver.restore(sess, 'model_tmp/model')
T = (time.time() - start_time)
self.eval(T, dev_data, hparams, sess)
os.system("rm -r model_tmp")
def infer(self, dev_data):
hparams = self.hparams
sess = self.sess
assert len(dev_data[0]) == len(
dev_data[1]), "Size of features data must be equal to label"
preds = []
total_loss = []
for idx in range(len(dev_data[0]) // hparams.batch_size + 1):
batch = dev_data[0][idx * hparams.batch_size: \
min((idx + 1) * hparams.batch_size,
len(dev_data[0]))]
if len(batch) == 0:
break
batch = utils.hash_batch(batch, hparams)
label = dev_data[1][idx * hparams.batch_size: \
min((idx + 1) * hparams.batch_size,
len(dev_data[1]))]
pred = sess.run(self.prob, feed_dict= \
{self.features: batch, self.label: label, self.use_norm: False})
preds.append(pred)
preds = np.concatenate(preds)
return preds
def get_embedding(self, dev_data):
hparams = self.hparams
sess = self.sess
assert len(dev_data[0]) == len(
dev_data[1]), "Size of features data must be equal to label"
embedding = []
total_loss = []
for idx in range(len(dev_data[0]) // hparams.batch_size + 1):
batch = dev_data[0][idx * hparams.batch_size: \
min((idx + 1) * hparams.batch_size,
len(dev_data[0]))]
if len(batch) == 0:
break
batch = utils.hash_batch(batch, hparams)
label = dev_data[1][idx * hparams.batch_size: \
min((idx + 1) * hparams.batch_size,
len(dev_data[1]))]
temp = sess.run(self.emb_inp_v2, \
feed_dict={self.features: batch, self.label: label})
embedding.append(temp)
embedding = np.concatenate(embedding, 0)
return embedding