def train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device,
corpus_indices, idx_to_char, char_to_idx,
num_epochs, num_steps, lr, clipping_theta,
batch_size, pred_period, pred_len, prefixes):
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
model.to(device)
state = None
for epoch in range(num_epochs):
l_sum, n, start = 0.0, 0, time.time()
data_iter = d2l.data_iter_consecutive(corpus_indices, batch_size,
num_steps, device) # 相邻采样
for X, Y in data_iter:
if state is not None:
# 使用detach函数从计算图分离隐藏状态, 这是为了
# 使模型参数的梯度计算只依赖一次迭代读取的小批量序列(防止梯度计算开销太大)
if isinstance(state, tuple): # LSTM, state:(h, c)
state = (state[0].detach(), state[1].detach())
else:
state = state.detach()
(output, state) = model(
X, state) # output: 形状为(num_steps * batch_size, vocab_size)
# Y的形状是(batch_size, num_steps)，转置后再变成长度为
# batch * num_steps 的向量，这样跟输出的行一一对应
y = torch.transpose(Y, 0, 1).contiguous().view(-1)
l = loss(output, y.long())
optimizer.zero_grad()
l.backward()
# 梯度裁剪
d2l.grad_clipping(model.parameters(), clipping_theta, device)
optimizer.step()
l_sum += l.item() * y.shape[0]
n += y.shape[0]
try:
perplexity = math.exp(l_sum / n)
except OverflowError:
perplexity = float('inf')
if (epoch + 1) % pred_period == 0:
print('epoch %d, perplexity %f, time %.2f sec' %
(epoch + 1, perplexity, time.time() - start))
for prefix in prefixes:
print(
' -',
predict_rnn_pytorch(prefix, pred_len, model,
vocab_size, device, idx_to_char,
char_to_idx))
def train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device,
corpus_indices, idx_to_char, char_to_idx,
num_epochs, num_steps, lr, clipping_theta,
batch_size, pred_period, pred_len, prefixes):
#pred_period每多少周期打印一次
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
model.to(device)
state = None
for epoch in range(num_epochs):
l_sum, n, start = 0.0, 0, time.time()
data_iter = d2l.data_iter_consecutive(corpus_indices, batch_size,
num_steps, device)
for X, Y in data_iter:
if state is not None:
if isinstance(state, tuple):
state = (state[0].detach(), state[1].detach())
else:
state = state.detach()
(output, state) = model(X, state)
##我总感觉是transpose(Y,1,0),试验了困惑度还小一些,想法正确！！！
y = torch.transpose(Y, 0, 1).contiguous().view(-1)
l = loss(output, y.long())
optimizer.zero_grad()
l.backward()
d2l.grad_clipping(model.parameters(), clipping_theta, device)
optimizer.step()
l_sum += l.item() * y.shape[0]
n += y.shape[0]
try:
perplexity = math.exp(l_sum / n)
except OverflowError:
perplexity = float('inf')
if (epoch + 1) % pred_period == 0:
print('epoch %d,perplexity %f,time %.2f sec' %
(epoch + 1, perplexity, time.time() - start))
for prefix in prefixes:
print(
'-',
predict_rnn_pytorch(prefix, pred_len, model, vocab_size,
device, idx_to_char, char_to_idx))