def train(self):
data_manager = DataManager(self.batch_size, logger=self.logger,
is_many_to_one=self.is_many_to_one,
data_file_count=self.data_file_count,
pretrained_file=self.pre_train)
if self.is_many_to_one:
net = RNN_M2O(len(data_manager.word_list), self.embedding_len,
self.hidden_size, self.learning_rate, self.num_hidden_layer,
self.drop_rate, use_adam=True, use_cuda=self.use_cuda,
pretrained_emb=data_manager.pretrained_embeddings())
else:
net = RNN_M2M(len(data_manager.word_list), self.embedding_len,
self.hidden_size, self.learning_rate, self.num_hidden_layer,
self.drop_rate, use_adam=True, use_cuda=self.use_cuda,
pretrained_emb=data_manager.pretrained_embeddings())
self._train(net, data_manager)
def test(self, id):
_, lr, hs, nh = re.search(r'M2(M|O)_([0-9]+)_([0-9]+)_([0-9]+)_?', id).groups()
lr, hs, nh = float('0.'+lr[1:]), int(hs), int(nh)
data_manager = DataManager(self.batch_size, logger=self.logger,
is_many_to_one=self.is_many_to_one,
data_file_count=self.data_file_count,
pretrained_file=self.pre_train, is_test=True)
if self.is_many_to_one:
model = RNN_M2O
else:
model = RNN_M2M
net = model(len(data_manager.word_list), self.embedding_len,
hs, lr, nh, self.drop_rate, use_adam=True, use_cuda=self.use_cuda,
pretrained_emb=data_manager.pretrained_embeddings())
status, _epoch_index, _perplexity_history, _min_perplexity = self._load(net, id)
if status:
loss_fn = net.get_loss()
# Testing
test_losses = 0.
test_acc = 0.
test_counter = 0
net.eval()
for data, label in data_manager.test_loader():
data = T.autograd.Variable(T.LongTensor(data))
label = T.autograd.Variable(T.LongTensor(label))
if self.use_cuda:
data = data.cuda()
label = label.cuda()
output, predicted = net(data)
test_losses += loss_fn(output.view(-1, len(data_manager.word_list)), label.view(-1)) \
.data.cpu()[0] * data.size(0)
test_acc += (label.squeeze() == predicted).float().mean().data * data.size(0)
test_counter += data.size(0)
mean_test_loss = test_losses/test_counter
mean_test_acc = test_acc/test_counter
perplexity = np.exp(mean_test_loss)
self.logger.i('Loss: %.4f, Acc: %.4f, Perp: %.4f'%(mean_test_loss, mean_test_acc, perplexity))
return mean_test_loss, mean_test_acc, perplexity
else:
raise AssertionError('Model file not found!')