我在fastai书中从头开始学习第12章关于RNN/LSTM的内容，但是在尝试从头开始训练一个定制的LSTM时遇到了困难。
这是样板位（遵循书中的示例）

from fastai.text.all import *
path = untar_data(URLs.HUMAN_NUMBERS)

lines = L()
with open(path/'train.txt') as f: lines += L(*f.readlines())
with open(path/'valid.txt') as f: lines += L(*f.readlines())

text = ' . '.join([l.strip() for l in lines])
tokens = text.split(' ')
vocab = L(*tokens).unique()
word2idx = {w:i for i,w in enumerate(vocab)}
nums = L(word2idx[i] for i in tokens)

def group_chunks(ds, bs):
    m = len(ds) // bs
    new_ds = L()
    for i in range(m): new_ds += L(ds[i + m*j] for j in range(bs))
    return new_ds

sl = 3
bs = 64
seqs = L((tensor(nums[i:i+sl]), nums[i+sl])
         for i in range(0,len(nums)-sl-1,sl))
cut = int(len(seqs) * 0.8)
dls = DataLoaders.from_dsets(group_chunks(seqs[:cut], bs),
                             group_chunks(seqs[cut:], bs),
                             bs=bs, drop_last=True, shuffle=False)

这是最重要的

class LSTMCell(Module):
    def __init__(self, ni, nh):
        self.forget_gate = nn.Linear(ni + nh, nh)
        self.input_gate  = nn.Linear(ni + nh, nh)
        self.cell_gate   = nn.Linear(ni + nh, nh)
        self.output_gate = nn.Linear(ni + nh, nh)
    
    def forward(self, input, state):
        h, c = state
        h = torch.cat([h, input], dim=1)  
        c = c * torch.sigmoid(self.forget_gate(h))
        c = c + torch.sigmoid(self.input_gate(h)) * torch.tanh(self.cell_gate(h))
        h = torch.sigmoid(self.output_gate(h)) * torch.tanh(c)
        return h, (h, c)    
    

class MyModel(Module):
    def __init__(self, vocab_sz, n_hidden):
        self.i_h = nn.Embedding(vocab_sz, n_hidden)
        self.cells = [LSTMCell(bs, n_hidden) for _ in range(sl)]
        self.h_o = nn.Linear(n_hidden, vocab_sz)
        
        self.h = torch.zeros(bs, n_hidden)
        self.c = torch.zeros(bs, n_hidden)
        
    def forward(self, x):
        x = self.i_h(x)
        h, c = self.h, self.c        
        for i, cell in enumerate(self.cells):
            res, (h, c) = cell(x[:, i, :], (h, c))
        self.h = h.detach()
        self.c = c.detach()
        return self.h_o(res)

    def reset(self): 
        self.h.zero_()
        self.c.zero_()

learn = Learner(dls, MyModel(len(vocab), 64), loss_func=CrossEntropyLossFlat(), metrics=accuracy, cbs=ModelResetter)
learn.fit_one_cycle(5, 1e-2)

培训输出如下所示

任何帮助都很感激