def train(config):
train_data, word2index, tag2index, intent2index = preprocessing(
config.file_path, config.max_length)
if train_data == None:
print("Please check your data or its path")
return
encoder = Encoder(len(word2index), config.embedding_size,
config.hidden_size)
decoder = Decoder(len(tag2index), len(intent2index),
len(tag2index) // 3, config.hidden_size * 2)
if USE_CUDA:
encoder = encoder.cuda()
decoder = decoder.cuda()
#print("來到這裏了！1！")
encoder.init_weights()
decoder.init_weights()
#print("來到這裏了！2！")
loss_function_1 = nn.CrossEntropyLoss(ignore_index=0)
loss_function_2 = nn.CrossEntropyLoss()
enc_optim = optim.Adam(encoder.parameters(), lr=config.learning_rate)
dec_optim = optim.Adam(decoder.parameters(), lr=config.learning_rate)
#print("來到這裏了！3！")
for step in range(config.step_size):
losses = []
for i, batch in enumerate(getBatch(config.batch_size, train_data)):
x, y_1, y_2 = zip(*batch)
x = torch.cat(x)
tag_target = torch.cat(y_1)
intent_target = torch.cat(y_2)
# print("來到這裏了！4！")
x_mask = torch.cat([
Variable(torch.ByteTensor(tuple(map(lambda s: s == 0,
t.data)))).cuda()
if USE_CUDA else Variable(
torch.ByteTensor(tuple(map(lambda s: s == 0, t.data))))
for t in x
]).view(config.batch_size, -1)
y_1_mask = torch.cat([
Variable(torch.ByteTensor(tuple(map(lambda s: s == 0,
t.data)))).cuda()
if USE_CUDA else Variable(
torch.ByteTensor(tuple(map(lambda s: s == 0, t.data))))
for t in tag_target
]).view(config.batch_size, -1)
# print("來到這裏了！5！")
encoder.zero_grad()
decoder.zero_grad()
# print("來到這裏了！6！")
output, hidden_c = encoder(x, x_mask)
# print("來到這裏了！7！")
start_decode = Variable(
torch.LongTensor([
[word2index['<SOS>']] * config.batch_size
])).cuda().transpose(1, 0) if USE_CUDA else Variable(
torch.LongTensor([[word2index['<SOS>']] *
config.batch_size])).transpose(1, 0)
# print("來到這裏了！8！")
tag_score, intent_score = decoder(start_decode, hidden_c, output,
x_mask)
#print("來到這裏了！9！")
loss_1 = loss_function_1(tag_score, tag_target.view(-1))
# print("來到這裏了！10！")
loss_2 = loss_function_2(intent_score, intent_target)
#print("來到這裏了！11！")
loss = loss_1 + loss_2
losses.append(
loss.data.cpu().numpy() if USE_CUDA else loss.data.numpy())
#print("來到這裏了！12！")
loss.backward()
# print("來到這裏了！13！")
torch.nn.utils.clip_grad_norm(encoder.parameters(), 5.0)
torch.nn.utils.clip_grad_norm(decoder.parameters(), 5.0)
enc_optim.step()
dec_optim.step()
if i % 100 == 0:
with open("result.txt", "a+") as f:
#print("Step",step," epoch",i," : ",np.mean(losses))
print(f"Step是{step},epoch是{i} ：均值为{np.mean(losses)}")
f.write(f"Step是{step},epoch是{i} ：均值为{np.mean(losses)}")
f.write("\n")
losses = []
if not os.path.exists(config.model_dir):
os.makedirs(config.model_dir)
#print("來到這裏了！5！")
torch.save(decoder.state_dict(),
os.path.join(config.model_dir, 'jointnlu-decoder.pkl'))
torch.save(encoder.state_dict(),
os.path.join(config.model_dir, 'jointnlu-encoder.pkl'))
print("Train Complete!")
def train(config, encoder_in = None, decoder_in = None):
train_data, word2index, tag2index, intent2index = preprocessing(config.file_path,config.max_length)
if train_data==None:
print("Please check your data or its path")
return
if encoder_in != None:
encoder = encoder_in
decoder = decoder_in
else:
encoder = Encoder(len(word2index),config.embedding_size,config.hidden_size)
decoder = Decoder(len(tag2index),len(intent2index),len(tag2index)//3,config.hidden_size*2)
if USE_CUDA:
encoder = encoder.cuda()
decoder = decoder.cuda()
encoder.init_weights()
decoder.init_weights()
loss_function_1 = nn.CrossEntropyLoss(ignore_index=0)
loss_function_2 = nn.CrossEntropyLoss()
enc_optim= optim.Adam(encoder.parameters(), lr=config.learning_rate)
dec_optim = optim.Adam(decoder.parameters(),lr=config.learning_rate)
for step in range(config.step_size):
losses=[]
for i, batch in enumerate(getBatch(config.batch_size,train_data)):
x,y_1,y_2 = zip(*batch) # sin,sout,intent
x = torch.cat(x)
tag_target = torch.cat(y_1)
intent_target = torch.cat(y_2)
x_mask = torch.cat([Variable(torch.ByteTensor(tuple(map(lambda s: s ==0, t.data)))).cuda() if USE_CUDA else Variable(torch.ByteTensor(tuple(map(lambda s: s ==0, t.data)))) for t in x]).view(config.batch_size,-1)
y_1_mask = torch.cat([Variable(torch.ByteTensor(tuple(map(lambda s: s ==0, t.data)))).cuda() if USE_CUDA else Variable(torch.ByteTensor(tuple(map(lambda s: s ==0, t.data)))) for t in tag_target]).view(config.batch_size,-1)
encoder.zero_grad()
decoder.zero_grad()
output, hidden_c = encoder(x,x_mask)
start_decode = Variable(torch.LongTensor([[word2index['<SOS>']]*config.batch_size])).cuda().transpose(1,0) if USE_CUDA else Variable(torch.LongTensor([[word2index['<SOS>']]*config.batch_size])).transpose(1,0)
tag_score, intent_score = decoder(start_decode,hidden_c,output,x_mask)
loss_1 = loss_function_1(tag_score,tag_target.view(-1))
loss_2 = loss_function_2(intent_score,intent_target)
loss = loss_1+loss_2
losses.append(loss.data.cpu().numpy() if USE_CUDA else loss.data.numpy())
loss.backward()
torch.nn.utils.clip_grad_norm(encoder.parameters(), 5.0)
torch.nn.utils.clip_grad_norm(decoder.parameters(), 5.0)
enc_optim.step()
dec_optim.step()
if i % 100==0:
print("Step",step," epoch",i," : ",np.mean(losses))
losses=[]
t = Check()
t.test(encoder,decoder)
count = t.test_error_count
rate = t.test_error_rate
if not os.path.exists(config.model_dir):
os.makedirs(config.model_dir)
torch.save(decoder, os.path.join(config.model_dir, str(count)+'_'+str(rate)+'_'+'decoder.pkl'))
torch.save(encoder, os.path.join(config.model_dir, str(count)+'_'+str(rate)+'_'+'encoder.pkl'))
# if not os.path.exists(config.model_dir):
# os.makedirs(config.model_dir)
# torch.save(decoder.state_dict(),os.path.join(config.model_dir,'jointnlu-decoder.pkl'))
# torch.save(encoder.state_dict(),os.path.join(config.model_dir, 'jointnlu-encoder.pkl'))
# torch.save(decoder,os.path.join(config.model_dir,'jointnlu-decoder.pkl'))
# torch.save(encoder,os.path.join(config.model_dir, 'jointnlu-encoder.pkl'))
print("Train Complete!")
def train(config):
data_loader = DataLoader(config.file_path, config.max_length,
config.batch_size)
train_data, word2index, tag2index, intent2index = data_loader.load_train()
if train_data is None:
print("Please check your data or its path")
return
encoder = Encoder(len(word2index), config.embedding_size,
config.hidden_size)
decoder = Decoder(len(tag2index), len(intent2index),
config.hidden_size * 2)
if USE_CUDA:
encoder = encoder.cuda()
decoder = decoder.cuda()
encoder.init_weights()
decoder.init_weights()
loss_function_1 = nn.CrossEntropyLoss(ignore_index=0)
loss_function_2 = nn.CrossEntropyLoss()
enc_optim = optim.Adam(encoder.parameters(), lr=config.learning_rate)
dec_optim = optim.Adam(decoder.parameters(), lr=config.learning_rate)
for step in range(config.step_size):
losses = []
for i, batch in enumerate(data_loader.get_batch(train_data)):
x, embedding_x, y_1, y_2 = zip(*batch)
x = torch.cat(x)
embedding_x = torch.cat(embedding_x)
tag_target = torch.cat(y_1)
intent_target = torch.cat(y_2)
x_mask = torch.cat([
Variable(torch.ByteTensor(tuple(map(lambda s: s == 0,
t.data)))).cuda()
if USE_CUDA else Variable(
torch.ByteTensor(tuple(map(lambda s: s == 0, t.data))))
for t in x
]).view(len(batch), -1)
encoder.zero_grad()
decoder.zero_grad()
output, hidden_c = encoder(x, embedding_x, x_mask)
start_decode = Variable(
torch.LongTensor(
[[word2index['<SOS>']] * len(batch)])).cuda().transpose(
1, 0) if USE_CUDA else Variable(
torch.LongTensor([[word2index['<SOS>']] *
len(batch)])).transpose(1, 0)
tag_score, intent_score = decoder(start_decode, hidden_c, output,
x_mask)
loss_1 = loss_function_1(tag_score, tag_target.view(-1))
loss_2 = loss_function_2(intent_score, intent_target)
loss = loss_1 + loss_2
losses.append(
loss.data.cpu().numpy() if USE_CUDA else loss.data.numpy())
loss.backward()
torch.nn.utils.clip_grad_norm(encoder.parameters(), 5.0)
torch.nn.utils.clip_grad_norm(decoder.parameters(), 5.0)
enc_optim.step()
dec_optim.step()
if i % 100 == 0:
print("Step", step, " : ", np.mean(losses))
losses = []
if not os.path.exists(config.model_dir):
os.makedirs(config.model_dir)
torch.save(encoder, os.path.join(config.model_dir, 'jointnlu-encoder.pt'))
torch.save(decoder, os.path.join(config.model_dir, 'jointnlu-decoder.pt'))
print("Training Complete!")
