def train(lr):
# Load data
print 'loading dataset...'
train_data = TextIterator(train_datafile, n_batch=n_batch, maxlen=maxlen)
valid_data = TextIterator(valid_datafile, n_batch=n_batch, maxlen=maxlen)
test_data = TextIterator(test_datafile, n_batch=n_batch, maxlen=maxlen)
print 'building model...'
model = RNNLM(n_input, n_hidden, vocabulary_size, rnn_cell, optimizer, p,
bptt)
print 'training start...'
start = time.time()
idx = 0
error = []
n_words = 0
for epoch in xrange(NEPOCH):
in_start = time.time()
for x, x_mask, y, y_mask in train_data:
idx += 1
beg_time = time.time()
#print x.shape
#print y.shape
cost, batch_nll = model.train(x, x_mask, y, y_mask, lr)
error.append(batch_nll)
n_words += np.sum(y_mask)
if np.isnan(cost) or np.isinf(cost):
print 'NaN Or Inf detected!'
return -1
if idx % disp_freq == 0:
error = np.asarray(error).flatten()
logger.info('epoch: %d idx: %d cost: %f ppl: %f' %
(epoch, idx, np.sum(error) / n_words,
np.exp(np.sum(error) / n_words)))
error = []
n_words = 0
if idx % save_freq == 0:
filename = './model/param_{}_bptt{}_{:.2f}.pkl'.format(
rnn_cell, bptt, (time.time() - start))
logger.info('dumping...' + filename)
save_model(filename, model)
if idx % valid_freq == 0:
logger.info('validing...')
valid_cost = evaluate_ppl(valid_data, model)
logger.info('validation cost: %f perplexity: %f' %
(valid_cost, np.exp(valid_cost)))
if idx % test_freq == 0:
logger.info('testing...')
test_cost = evaluate_ppl(test_data, model)
logger.info('test cost: %f perplexity: %f' %
(test_cost, np.exp(test_cost)))
print "Finished. Time = " + str(time.time() - start)
def test():
test_data = TextIterator(test_datafile, n_batch=n_batch)
valid_data = TextIterator(valid_datafile, n_batch=n_batch)
model = RNNLM(n_input, n_hidden, vocabulary_size, rnn_cell, optimizer, p)
if os.path.isfile(args.model_dir):
print 'loading pretrained model:', args.model_dir
model = load_model(args.model_dir, model)
else:
print args.model_dir, 'not found'
mean_cost = evaluate(valid_data, model)
print 'valid cost:', mean_cost, 'perplexity:', np.exp(
mean_cost) #,"word_error_rate:",mean_wer
mean_cost = evaluate(test_data, model)
print 'test cost:', mean_cost, 'perplexity:', np.exp(mean_cost)
def test():
test_data = TextIterator(test_datafile, n_batch=n_batch)
valid_data = TextIterator(valid_datafile, n_batch=n_batch)
model = RNNLM(n_input, n_hidden, vocabulary_size, rnn_cell, optimizer, p)
if os.path.isfile(args.model_dir):
print 'loading pretrained model:', args.model_dir
model = load_model(args.model_dir, model)
else:
print args.model_dir, 'not found'
valid_cost, wer = evaluate(valid_data, model)
logger.info('validation cost: %f perplexity: %f,word_error_rate:%f' %
(valid_cost, np.exp(valid_cost), wer))
test_cost, wer = evaluate(test_data, model)
logger.info('test cost: %f perplexity: %f,word_error_rate:%f' %
(test_cost, np.exp(test_cost), wer))
def train(lr):
print 'loading dataset...'
train_data = TextIterator(train_datafile, n_batch=n_batch, maxlen=maxlen)
valid_data = TextIterator(valid_datafile, n_batch=n_batch, maxlen=maxlen)
test_data = TextIterator(test_datafile, n_batch=n_batch, maxlen=maxlen)
print 'building model...'
model = RNNLM(n_input, n_hidden, vocabulary_size, rnn_cell, optimizer, p,
bptt)
if os.path.isfile(model_dir):
print 'loading checkpoint parameters....', model_dir
model = load_model(model_dir, model)
if goto_line > 0:
train_data.goto_line(goto_line)
print 'goto line:', goto_line
print 'training start...'
start = time.time()
idx = goto_line
for epoch in xrange(NEPOCH):
error = 0
for x, x_mask, y, y_mask in train_data:
idx += 1
cost = model.train(x, x_mask, y, y_mask, lr)
error += cost
if np.isnan(cost) or np.isinf(cost):
print 'NaN Or Inf detected!'
return -1
if idx % disp_freq == 0:
logger.info('epoch: %d idx: %d cost: %f ppl: %f' %
(epoch, idx, error / disp_freq,
np.exp(error / (1.0 * disp_freq))))
error = 0
if idx % save_freq == 0:
logger.info('dumping...')
save_model(
'./model/parameters_%.2f.pkl' % (time.time() - start),
model)
if idx % valid_freq == 0:
logger.info('validing...')
valid_cost, wer = evaluate(valid_data, model)
logger.info(
'validation cost: %f perplexity: %f,word_error_rate:%f' %
(valid_cost, np.exp(valid_cost), wer))
if idx % test_freq == 0:
logger.info('testing...')
test_cost, wer = evaluate(test_data, model)
logger.info('test cost: %f perplexity: %f,word_error_rate:%f' %
(test_cost, np.exp(test_cost), wer))
print "Finished. Time = " + str(time.time() - start)