def gen(**kwargs):
for k, v in kwargs.items():
setattr(opt, k, v)
data_all = np.load(opt.pickle_path)
data = data_all['data']
word2ix = data_all['word2ix'].item()
ix2word = data_all['ix2word'].item()
model = PoetryModel(len(word2ix), 128, 256)
model.load_state_dict(t.load(opt.model_path, map_location=t.device('cpu')))
opt.device = t.device('cuda' if t.cuda.is_available() else 'cpu')
model.to(opt.device)
if opt.start_words.isprintable():
start_words = opt.start_words
prefix_words = opt.prefix_words if opt.prefix_words else None
else:
start_words = opt.start_words.encode('ascii',
'surrogateescape').decode('utf8')
prefix_words = opt.prefix_words.encode(
'ascii',
'surrogateescape').decode('utf8') if opt.prefix_words else None
start_words = start_words.replace(',', '，').replace('.',
'。').replace('?', '？')
gen_poetry = gen_acrostic if opt.acrostic else generate
result = gen_poetry(model, start_words, ix2word, word2ix, prefix_words)
print(''.join(result))
def run(self):
# 1 获取数据
data, char_to_ix, ix_to_chars = get_data(self.config)
vocab_size = len(char_to_ix)
print('样本数：%d' % len(data))
print('词典大小： %d' % vocab_size)
# 2 设置dataloader
data = torch.from_numpy(data)
data_loader = Data.DataLoader(data,
batch_size=self.config.batch_size,
shuffle=True,
num_workers=1)
# 3 创建模型
model = PoetryModel(vocab_size=vocab_size,
embedding_dim=self.config.embedding_dim,
hidden_dim=self.config.hidden_dim,
device=self.device,
layer_num=self.config.layer_num)
model.to(self.device)
# 4 创建优化器
optimizer = optim.Adam(model.parameters(),
lr=self.config.lr,
weight_decay=self.config.weight_decay)
# 5 创建损失函数,使用与logsoftmax的输出
criterion = nn.CrossEntropyLoss()
# 6.训练
self.train(data_loader, model, optimizer, criterion, char_to_ix,
ix_to_chars)
def userTest():
print("正在初始化......")
datas = np.load("data/tang.npz",allow_pickle=True)
data = datas['data']
ix2word = datas['ix2word'].item()
word2ix = datas['word2ix'].item()
model = PoetryModel(len(ix2word), Config.embedding_dim, Config.hidden_dim)
model.load_state_dict(t.load(Config.model_path, 'cpu'))
if Config.use_gpu:
model.to(t.device('cuda'))
print("初始化完成！\n")
while True:
print("欢迎使用李港唐诗生成器，\n"
"输入1 进入首句生成模式\n"
"输入2 进入藏头诗生成模式\n")
mode = int(input())
if mode == 1:
print("请输入您想要的诗歌首句，可以是五言或七言")
start_words = str(input())
gen_poetry = ''.join(generate(model, start_words, ix2word, word2ix))
print("生成的诗句如下：%s\n" % (gen_poetry))
elif mode == 2:
print("请输入您想要的诗歌藏头部分，不超过16个字，最好是偶数")
start_words = str(input())
gen_poetry = ''.join(gen_acrostic(model, start_words, ix2word, word2ix))
print("生成的诗句如下：%s\n" % (gen_poetry))
def gen():
"""生成相应的情话"""
data, word2ix, ix2word = get_data()
model = PoetryModel(len(word2ix), config.embedding_dim, config.hidden_dim,
config.num_layers)
state_dict = torch.load(config.model_path, map_location=lambda s, l: s)
model.load_state_dict(state_dict)
model.to(config.device)
# python2和python3 字符串兼容
if sys.version_info.major == 3:
if config.start_words.isprintable():
start_words = config.start_words
prefix_words = config.prefix_words if config.prefix_words else None
else:
start_words = config.start_words.encode(
'ascii', 'surrogateescape').decode('utf8')
prefix_words = config.prefix_words.encode(
'ascii', 'surrogateescape').decode(
'utf8') if config.prefix_words else None
else:
start_words = config.start_words.decode('utf8')
prefix_words = config.prefix_words.decode(
'utf8') if config.prefix_words else None
start_words = start_words.replace(',',
u'，').replace('.',
u'。').replace('?', u'？')
result = generate(model, start_words, ix2word, word2ix, prefix_words)
print(''.join(result))
def load_model(self):
model = PoetryModel(len(self.word_to_ix), self.config.embedding_dim,
self.config.hidden_dim, self.device,
self.config.layer_num)
map_location = lambda s, l: s
state_dict = torch.load(self.config.model_path,
map_location=map_location)
model.load_state_dict(state_dict)
model.to(self.device)
self.model = model
def gen(**kwargs):
"""
gen 提供命令行接口
"""
for k, v in kwargs.items():
setattr(opt, k, v)
data, word2ix, ix2word = get_data(opt)
model = PoetryModel(len(word2ix), opt.embedding_dim, opt.hidden_dim)
# todo : what is it???
map_location = lambda s, l: s
state_dict = t.load(opt.model_path, map_location=map_location)
model.load_state_dict(state_dict)
if opt.use_gpu:
model = model.to(device)
# 默认Python 3.X
if opt.start_words.isprintable:
start_words = opt.start_words
prefix_words = opt.prefix_words
else:
# todo : what is it???
start_words = opt.start_words.encode('ascii', 'surrogateescape').decode('utf8')
prefix_words = opt.prefix_words.encode('ascii', 'surrogateescape').decode('utf8') if opt.prefix_words else None
# 半角替换成全角
start_words = start_words.replace(',', u'，').replace('.', u'。').replace('?', u'？')
# 选择合适的生成函数
gen_poetry = gen_acrostic if opt.acrostic else generate
result = gen_poetry(model, start_words, ix2word, word2ix, prefix_words)
print(''.join(result))
def train():
datas = np.load("tang.npz")
data = datas['data']
ix2word = datas['ix2word'].item()
word2ix = datas['word2ix'].item()
data = torch.from_numpy(data)
dataloader = DataLoader(data[:5000],
batch_size=config.batch_size,
shuffle=True,
num_workers=2)
model = PoetryModel(len(word2ix),
embedding_dim=config.embedding_dim,
hidden_dim=config.hidden_dim)
optimizer = optim.Adam(model.parameters(), lr=config.lr)
criterion = nn.CrossEntropyLoss()
model.to(config.device)
f = open('result.txt', 'w')
loss_history = []
for epoch in range(config.epoch):
start_time = time.time()
temp_loss = 0
for step, batch_data in enumerate(dataloader):
batch_data = batch_data.long().transpose(1, 0).contiguous()
optimizer.zero_grad()
trn, target = batch_data[:-1, :], batch_data[1:, :]
output, _ = model(trn)
loss = criterion(output, target.view(-1))
loss.backward()
optimizer.step()
temp_loss += loss.item()
if step % config.print_freq == 0 or step == len(dataloader) - 1:
print("Train: [{:2d}/{}] Step: {:03d}/{:03d} Loss: {} ".format(
epoch + 1, config.epoch, step,
len(dataloader) - 1, loss.item()))
loss_history.append(temp_loss / len(len(dataloader)))
elapsed_time = time.time() - start_time
print("Epoch: %d" % epoch + " " + "Loss: %d" % loss_history[-1] +
" Epoch time: " +
time.strftime("%H: %M: %S", time.gmtime(elapsed_time)))
torch.save(model.state_dict(), config.model_path)
def train():
# 获取数据
data, word2ix, ix2word = get_data()
data = torch.from_numpy(data)
dataloader = DataLoader(data,
batch_size=config.batch_size,
shuffle=True,
num_workers=1)
# 模型定义
model = PoetryModel(len(word2ix), config.embedding_dim, config.hidden_dim,
config.num_layers)
optimizer = optim.Adam(model.parameters(),
lr=config.lr,
weight_decay=config.weight_decay)
scheduler = StepLR(optimizer,
step_size=config.lr_step,
gamma=config.lr_gamma)
criterion = nn.CrossEntropyLoss()
model.to(config.device)
for epoch in range(config.epoch):
total_loss = 0
for data_ in tqdm(dataloader):
# 训练
data_ = data_.long().transpose(1, 0).contiguous()
data_ = data_.to(config.device)
optimizer.zero_grad()
input_, target = data_[:-1, :], data_[1:, :]
output, _ = model(input_)
loss = criterion(output, target.view(-1))
loss.backward()
optimizer.step()
total_loss += loss.item()
scheduler.step()
print("epoch: ", epoch, "loss: ", total_loss / len(dataloader))
torch.save(model.state_dict(),
'%s_%s.pth' % (config.model_prefix, epoch))
# save the word dic for sample method
with open('wordDict', 'wb') as f:
p.dump(word_to_ix, f)
# save all available word
# wordList = open('wordList','w')
# for w in word_to_ix:
# wordList.write(w.encode('utf-8'))
# wordList.close()
# create model
print("create model...")
model = PoetryModel(vocab_size=len(word_to_ix),
embedding_dim=256,
hidden_dim=256)
model.to(device)
optimizer = optim.RMSprop(model.parameters(), lr=0.01, weight_decay=0.0001)
# use negative log likelihood loss.
criterion = nn.NLLLoss()
# one-hot vec representation of word
one_hot_var_target = {}
for w in word_to_ix:
one_hot_var_target.setdefault(w, make_one_hot_vec_target(w, word_to_ix))
# hyper params
epochs = 10
data_size = len(data)
batch_size = 1000
def train(**kwargs):
for k, v in kwargs.items():
setattr(config, k, v)
device = torch.device('cuda') if use_cuda else torch.device('cpu')
# 获取数据
data, vocab = get_data(config.filepath)
np.random.shuffle(data)
l = len(data)
dev_data = data[:l // 5 - 1]
data = data[l // 5:]
data = torch.from_numpy(data)
dev_data = torch.from_numpy(dev_data)
dataloader = D.DataLoader(data,
batch_size=config.batch_size,
shuffle=True,
num_workers=4)
dev_dataloader = D.DataLoader(dev_data,
batch_size=config.batch_size,
shuffle=True,
num_workers=4)
# 模型定义
model = PoetryModel(len(vocab.word2idx), 128, 256)
# if config.model_path:
# model.load_state_dict(torch.load(config.model_path))
model.to(device)
# SGD, SGD with momentum, Nesterov, Adagrad, Adadelta, Adam
# optimizer = torch.optim.SGD(model.parameters(), lr=config.lr)
# optimizer = torch.optim.SGD(model.parameters(), lr=config.lr, momentum=0.9)
# optimizer = torch.optim.SGD(model.parameters(), lr=config.lr, momentum=0.9, nesterov=True)
# optimizer = torch.optim.Adagrad(model.parameters(), lr=config.lr)
# optimizer = torch.optim.Adadelta(model.parameters())
optimizer = torch.optim.Adam(model.parameters(), lr=config.lr)
criterion = nn.CrossEntropyLoss()
pre_pp = 0
cnt = -1
loss_his = []
pp_his = []
for epoch in range(config.epoch):
for ii, data_ in enumerate(dataloader):
# 训练
data_ = data_.long().transpose(1, 0).contiguous()
data_ = data_.to(device)
optimizer.zero_grad()
input_, target = data_[:-1, :], data_[1:, :]
output, _ = model(input_)
loss = criterion(output, target.view(-1))
loss.backward()
optimizer.step()
print("epoch", epoch, "step", ii, "loss", loss.item())
loss_his.append(loss.item())
# 测试
if (1 + ii) % config.gen_every == 0:
# "'春江花月夜凉如水'"
word = "春"
gen_poetry = ''.join(generate(model, word, vocab))
print(gen_poetry)
if (1 + ii) % config.pp_every == 0:
pp = check_perplexity(model, dev_dataloader)
if pre_pp < pp:
cnt += 1
pre_pp = pp
print(pp.cpu().numpy())
pp_his.append(pp.cpu().numpy())
if cnt >= config.tolerance:
torch.save(model.state_dict(),
'%s_final.pth' % str(int(time.time())))
print("epoch", epoch, "step", ii, "final loss",
loss.item())
for word in ["日", "红", "山", "夜", "湖", "海", "月"]:
gen_poetry = ''.join(generate(model, word, vocab))
print(gen_poetry)
return loss_his, pp_his
if (epoch + 1) % config.save_every == 0 or epoch + 1 == config.epoch:
torch.save(model.state_dict(),
'%s_%s.pth' % (str(int(time.time())), str(epoch)))
return loss_his, pp_his
def train(**kwargs):
for k, v in kwargs.items():
setattr(opt, k, v)
opt.device = t.device('cuda') if opt.use_gpu else t.device('cpu')
device = opt.device
vis = Visualizer(env=opt.env)
# 获取数据
data, word2ix, ix2word = get_data(opt)
data = t.from_numpy(data)
dataloader = t.utils.data.DataLoader(data,
batch_size=opt.batch_size,
shuffle=True,
num_workers=1)
# 模型定义
model = PoetryModel(len(word2ix), 128, 256)
optimizer = t.optim.Adam(model.parameters(), lr=opt.lr)
criterion = nn.CrossEntropyLoss()
if opt.model_path:
model.load_state_dict(t.load(opt.model_path))
model.to(device)
loss_meter = meter.AverageValueMeter()
for epoch in range(opt.epoch):
loss_meter.reset()
for ii, data_ in tqdm.tqdm(enumerate(dataloader)):
# 训练
data_ = data_.long().transpose(1, 0).contiguous()
data_ = data_.to(device)
optimizer.zero_grad()
input_, target = data_[:-1, :], data_[1:, :]
output, _ = model(input_)
loss = criterion(output, target.view(-1))
loss.backward()
optimizer.step()
loss_meter.add(loss.item())
# 可视化
if (1 + ii) % opt.plot_every == 0:
if os.path.exists(opt.debug_file):
ipdb.set_trace()
vis.plot('loss', loss_meter.value()[0])
# 诗歌原文
poetrys = [[
ix2word[_word] for _word in data_[:, _iii].tolist()
] for _iii in range(data_.shape[1])][:16]
vis.text('</br>'.join([''.join(poetry) for poetry in poetrys]),
win=u'origin_poem')
gen_poetries = []
# 分别以这几个字作为诗歌的第一个字，生成8首诗
for word in list(u'春江花月夜凉如水'):
gen_poetry = ''.join(
generate(model, word, ix2word, word2ix))
gen_poetries.append(gen_poetry)
vis.text('</br>'.join(
[''.join(poetry) for poetry in gen_poetries]),
win=u'gen_poem')
t.save(model.state_dict(), '%s_%s.pth' % (opt.model_prefix, epoch))
def train(**kwargs):
for k, v in kwargs.items():
setattr(opt, k, v)
opt.device = t.device('cuda' if t.cuda.is_available() else 'cpu')
device = opt.device
vis = Visualizer(env=opt.env)
# 获取数据
data_all = np.load(opt.pickle_path)
data = data_all['data']
word2ix = data_all['word2ix'].item()
ix2word = data_all['ix2word'].item()
data = t.from_numpy(data)
dataloader = DataLoader(data,
batch_size=opt.batch_size,
shuffle=True,
num_workers=1)
# 模型定义
model = PoetryModel(len(word2ix), 128, 256)
optimizer = t.optim.Adam(model.parameters(), lr=opt.lr)
loss_func = nn.CrossEntropyLoss()
if opt.model_path:
model.load_state_dict(
t.load(opt.model_path, map_location=t.device('cpu')))
model.to(device)
loss_avg = 0
for epoch in range(opt.epoch):
for ii, data_ in tqdm(enumerate(dataloader)):
data_ = data_.long()
data_ = data_.to(device)
optimizer.zero_grad()
input_, target = data_[:, :-1], data_[:, 1:]
output, _ = model(input_)
loss = loss_func(output, target.reshape(-1))
loss.backward()
optimizer.step()
loss_avg += loss.item()
# 可视化
if (ii + 1) % opt.plot_every == 0:
vis.plot('loss', loss_avg / opt.plot_every)
loss_avg = 0
poetrys = [[ix2word[_word] for _word in data_[i].tolist()]
for i in range(data_.shape[0])][:16]
vis.text('</br>'.join([''.join(poetry) for poetry in poetrys]),
win='origin_poem')
gen_poetries = []
for word in list('春江花月夜凉如水'):
gen_poetry = ''.join(
generate(model, word, ix2word, word2ix))
gen_poetries.append(gen_poetry)
vis.text('</br>'.join(
[''.join(poetry) for poetry in gen_poetries]),
win='gen_poem')
t.save(model.state_dict(), '%s_%s.pth' % (opt.model_prefix, epoch))
def train(**kwargs):
for k, v in kwargs.items():
setattr(opt, k, v)
opt.device=t.device('cuda') if opt.use_gpu else t.device('cpu')
device = opt.device
vis = Visualizer(env=opt.env)
# 获取数据
data, word2ix, ix2word = get_data(opt)
data = t.from_numpy(data)
dataloader = t.utils.data.DataLoader(data,
batch_size=opt.batch_size,
shuffle=True,
num_workers=1)
# 模型定义
model = PoetryModel(len(word2ix), 128, 256)
optimizer = t.optim.Adam(model.parameters(), lr=opt.lr)
criterion = nn.CrossEntropyLoss()
if opt.model_path:
model.load_state_dict(t.load(opt.model_path))
model.to(device)
loss_meter = meter.AverageValueMeter()
for epoch in range(opt.epoch):
loss_meter.reset()
for ii, data_ in tqdm.tqdm(enumerate(dataloader)):
# 训练
data_ = data_.long().transpose(1, 0).contiguous()
data_ = data_.to(device)
optimizer.zero_grad()
input_, target = data_[:-1, :], data_[1:, :]
output, _ = model(input_)
loss = criterion(output, target.view(-1))
loss.backward()
optimizer.step()
loss_meter.add(loss.item())
# 可视化
if (1 + ii) % opt.plot_every == 0:
if os.path.exists(opt.debug_file):
ipdb.set_trace()
vis.plot('loss', loss_meter.value()[0])
# 诗歌原文
poetrys = [[ix2word[_word] for _word in data_[:, _iii].tolist()]
for _iii in range(data_.shape[1])][:16]
vis.text('</br>'.join([''.join(poetry) for poetry in poetrys]), win=u'origin_poem')
gen_poetries = []
# 分别以这几个字作为诗歌的第一个字，生成8首诗
for word in list(u'春江花月夜凉如水'):
gen_poetry = ''.join(generate(model, word, ix2word, word2ix))
gen_poetries.append(gen_poetry)
vis.text('</br>'.join([''.join(poetry) for poetry in gen_poetries]), win=u'gen_poem')
t.save(model.state_dict(), '%s_%s.pth' % (opt.model_prefix, epoch))
def train(**kwargs):
for k, v in kwargs.items():
setattr(opt, k, v)
vis = Visualizer(env=opt.env)
# 获取数据
data, word2ix, ix2word = get_data(opt)
data = t.from_numpy(data)
dataloader = t.utils.data.DataLoader(data, batch_size=opt.batch_size, shuffle=True, num_workers=2)
# 定义model
model = PoetryModel(len(word2ix), opt.embedding_dim, opt.hidden_dim)
# 优化器
optimizer = t.optim.Adam(model.parameters(), lr=opt.lr)
# Loss Function
criterion = nn.CrossEntropyLoss()
# 使用预训练的模型，为了可持续训练
if opt.model_path and os.path.exists(opt.model_path):
model.load_state_dict(t.load(opt.model_path))
# GPU related
if opt.use_gpu:
model = model.to(device)
criterion = criterion.to(device)
# loss 计量器
loss_meter = meter.AverageValueMeter()
# for loop
for epoch in range(opt.epoch):
loss_meter.reset()
# for : batching dataset
for i, data_ in tqdm.tqdm(enumerate(dataloader)):
# 训练
# data_
# size: [128, 125] 每次取128行，每行一首诗，长度为125
# type: Tensor
# dtype: torch.int32 应该转成long
# 这行代码信息量很大：
# 第一步：int32 to long
# 第二步：将行列互换，为了并行计算的需要
# 第三步：将数据放置在连续内存里，避免后续有些操作报错
data_ = data_.long().transpose(0, 1).contiguous()
# GPU related
if opt.use_gpu:
data_ = data_.to(device)
# 到这里 data_.dtype又变成了torch.int64
# print(data_.dtype)
# 清空梯度
optimizer.zero_grad()
# 错位训练，很容易理解
# 把前n-1行作为input，把后n-1行作为target : model的输入
# 这么做还是为了并行计算的需要
# input_ 加下划线是为了和built_in function input区分开
input_, target = data_[:-1, :], data_[1:, :]
# model的返回值 output和hidden
# 这里hidden没什么用
output, _ = model(input_)
# 计算loss
target = target.view(-1)
# 新的target.size() [15872] 124 * 128 = 15872
# output.size() [15872, 8293] 8293 是词汇量的大小
loss = criterion(output, target)
# 反向传播
loss.backward()
# optimizer梯度下降更新参数
optimizer.step()
loss_meter.add(loss.data[0])
# 可视化
if (1 + i) % opt.plot_every == 0:
if os.path.exists(opt.debug_file):
ipdb.set_trace()
vis.plot('loss', loss_meter.value()[0])
# 诗歌原文
poetrys = [[ix2word[_word.item()] for _word in data_[:, _iii]]
for _iii in range(data_.size(1))][:16]
vis.text('</br>'.join([''.join(poetry) for poetry in poetrys]), win=u'origin_poem')
gen_poetries = []
# 分别以这几个字作为诗歌的第一个字，生成8首诗
for word in list(u'春江花月夜凉如水'):
gen_poetry = ''.join(generate(model, word, ix2word, word2ix))
gen_poetries.append(gen_poetry)
vis.text('</br>'.join([''.join(poetry) for poetry in gen_poetries]), win=u'gen_poem')
# 迭代一次epoch，保存一下模型
t.save(model.state_dict(), '%s_%s.pth' % (opt.model_prefix, epoch))
def train(**kwargs):
for k, v in kwargs.items():
setattr(opt, k, v)
opt.device = t.device('cuda:0') if opt.use_gpu else t.device('cpu')
device = opt.device
#vis = Visualizer(env=opt.env)
# 获取数据
data, word2ix, ix2word = get_data(opt)
data = t.from_numpy(data) #[57580,125]
dataloader = t.utils.data.DataLoader(data,
batch_size=opt.batch_size,
shuffle=True,
num_workers=1)
# 模型定义
model = PoetryModel(len(word2ix), 128, 256)
optimizer = t.optim.Adam(model.parameters(), lr=opt.lr)
criterion = nn.CrossEntropyLoss()
if opt.model_path:
model.load_state_dict(t.load(opt.model_path))
model.to(device)
# AverageMeter类用来管理一些变量的更新
loss_meter = meter.AverageValueMeter()
for epoch in range(opt.epoch):
loss_meter.reset() # 在每一个epoch 都要进行reset一遍
for ii, data_ in tqdm.tqdm(enumerate(dataloader)):
# 训练
#contiguous：view只能用在contiguous的variable上。如果在view之前用了transpose, permute等，需要用contiguous()来返回一个contiguous copy。
#一种可能的解释是：
#有些tensor并不是占用一整块内存，而是由不同的数据块组成，而tensor的view()操作依赖于内存是整块的，这时只需要执行contiguous()这个函数，把tensor变成在内存中连续分布的形式。
# 也就是说使用contiguous()是为了能够使用view（）
data_ = data_.long().transpose(
1, 0).contiguous() #data_ shape:[seq_len,batch_size]
data_ = data_.to(device)
optimizer.zero_grad()
#input_ shape:[124,128] target shape:[124,128]
input_, target = data_[:-1, :], data_[1:, :]
output, _ = model(
input_
) # ouput shape:[seq_len * batch_size,vocab_size] 此处seq_len为124
loss = criterion(
output, target.view(-1)) # target需要规整成[seq_len * batch_size]
loss.backward()
optimizer.step()
# 更新loss_meter
loss_meter.add(loss.item())
'''
# 可视化
if (1 + ii) % opt.plot_every == 0:
if os.path.exists(opt.debug_file):
ipdb.set_trace()
vis.plot('loss', loss_meter.value()[0])
# 诗歌原文
poetrys = [[ix2word[_word] for _word in data_[:, _iii].tolist()]
for _iii in range(data_.shape[1])][:16]
vis.text('</br>'.join([''.join(poetry) for poetry in poetrys]), win=u'origin_poem')
gen_poetries = []
# 分别以这几个字作为诗歌的第一个字，生成8首诗
for word in list(u'春江花月夜凉如水'):
gen_poetry = ''.join(generate(model, word, ix2word, word2ix))
gen_poetries.append(gen_poetry)
vis.text('</br>'.join([''.join(poetry) for poetry in gen_poetries]), win=u'gen_poem')
'''
# 每一个epoch都打印下loss的值
print('epoch:%d, loss:%.3f' % (epoch, loss_meter.value()[0]))
#需要改进的地方是 得到模型在验证集的结果 根据结果只保存最好的模型
t.save(model.state_dict(), '%s_%s.pth' % (opt.model_prefix, epoch))
return ix2word, word2ix
.replace('?', u'？')
gen_poetry = gen_acrostic if opt.acrostic else generate
result = gen_poetry(model, start_words, ix2word, word2ix, prefix_words)
print(''.join(result))
if __name__ == '__main__':
# step1: 训练模型
ix2word, word2ix = train()
# step2: 加载训练好的模型
# data, word2ix, ix2word = get_data(opt)
# data, word2ix, ix2word = data['data'], data['word2ix'].item(), data['ix2word'].item()
model = PoetryModel(len(word2ix), 128, 256)
#opt.device = t.device('cuda:0') if opt.use_gpu else t.device('cpu')
model.to(opt.device)
model.load_state_dict(t.load('%s_%s.pth' % (opt.model_prefix, 19)))
#利用训练好的模型生成藏头诗
results = gen_acrostic(model,
start_words='深度学习',
ix2word=ix2word,
word2ix=word2ix,
prefix_words=None)
print(' '.join(results))
# 生成普通的诗
gen()