test_stream = islice(data_stream, validation_count)
test_cost = test(test_stream)
print "Perplexity:", test_cost
if test_cost < improvement_threshold * best_cost:
best_cost = test_cost
P.save(output_file)
increase_count = 0
else:
increase_count += 1
if increase_count > patience:
break
# Run training
data_stream = data_io.randomise(data_stream, buffer_size=1024)
data_stream = data_io.sortify(data_stream, key=lambda x: len(x), buffer_size=512)
batch_data_stream = data_io.batch(data_stream, batch_size=batch_size)
batch_data_stream = data_io.randomise(batch_data_stream)
for batch in batch_data_stream:
avg_cost = train(batch)
if np.isnan(avg_cost):
pprint([''.join(id2char[c] for c in l[1:]) for l in batch])
exit(1)
print avg_cost,
seen += len(batch)
if seen > checkpoint:
print "Saving...",
P.save(tmp_file)
seen = 0
if errors < best_error * 0.9 :
P.save('model.pkl')
print "Wrote model."
best_error = errors
length_limit += 2
else:
# learning_rate = learning_rate / 2
# batch_size = max(1,batch_size//2)
# print "Learning rate:",learning_rate
P.save('tmp.model.pkl')
buffer_size = 256 / batch_size
train_group_answers = data_io.randomise(group_answers)
training_data = data_io.story_question_answer_idx(train_group_answers,vocab_in)
training_data = ( x for x in training_data if x[1].shape[0] <= length_limit )
training_data = data_io.sortify(training_data,key=lambda x:x[1].shape[0])
batched_training_data = data_io.batch(
training_data,
batch_size=batch_size,
criteria=lambda x,x_:abs(x[1].shape[0] - x_[1].shape[0]) <= 2
)
batched_training_data = data_io.randomise(batched_training_data,buffer_size=buffer_size)
group_count = 0
for batch in batched_training_data:
loss = 0
count = 0
for input_data,idxs,question_data,ans_w,ans_evds in batch:
print idxs.shape[0],
curr_loss = np.array(acc(input_data,idxs,question_data,ans_w,ans_evds))
if np.isnan(curr_loss).any():
print "Wrote model."
best_error = errors
length_limit += 2
else:
# learning_rate = learning_rate / 2
# batch_size = max(1,batch_size//2)
# print "Learning rate:",learning_rate
P.save('tmp.model.pkl')
buffer_size = 256 / batch_size
train_group_answers = data_io.randomise(group_answers)
training_data = data_io.story_question_answer_idx(
train_group_answers, vocab_in)
training_data = (x for x in training_data
if x[1].shape[0] <= length_limit)
training_data = data_io.sortify(training_data,
key=lambda x: x[1].shape[0])
batched_training_data = data_io.batch(
training_data,
batch_size=batch_size,
criteria=lambda x, x_: abs(x[1].shape[0] - x_[1].shape[0]) <= 2)
batched_training_data = data_io.randomise(batched_training_data,
buffer_size=buffer_size)
group_count = 0
for batch in batched_training_data:
loss = 0
count = 0
for input_data, idxs, question_data, ans_w, ans_evds in batch:
print idxs.shape[0],
curr_loss = np.array(
acc(input_data, idxs, question_data, ans_w, ans_evds))
