PyTorch LSTM用于多类分类：TypeError：“ Example”和“ Example”的实例之间不支持“ <”

我试图修改本教程中的代码以使其适应多类数据（我有55个不同的类）。触发了错误，我不确定根本原因。我对本教程所做的更改已在同一行注释中进行了注释。

两种解决方案之一可以满足以下问题：

（A）帮助确定错误的根本原因，或者

（B）使用PyTorch LSTM进行多类分类的样板脚本

import spacy
import torchtext
from torchtext import data
import re

TEXT = data.Field(tokenize = 'spacy', include_lengths = True)
LABEL = data.LabelField(dtype = torch.float)
fields = [(None,None),('text', TEXT), ('wage_label', LABEL)]

train_torch, test_torch = data.TabularDataset.splits(path='/Users/jdmoore7/Desktop/Python Projects/560_capstone/', 
                                            format='csv', 
                                            train='train_text_target.csv', 
                                            test='test_text_target.csv', 
                                            fields=fields,
                                            skip_header=True)


import random
train_data, valid_data = train_torch.split(random_state = random.seed(SEED)) 

MAX_VOCAB_SIZE = 25_000

TEXT.build_vocab(train_data, 
                 max_size = MAX_VOCAB_SIZE, 
                 vectors = "glove.6B.100d", 
                 unk_init = torch.Tensor.normal_)

LABEL.build_vocab(train_data)

BATCH_SIZE = 64

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
    (train_data, valid_data, test_torch), 
    batch_size = BATCH_SIZE,
    sort_within_batch = True,
    device = device)

import torch.nn as nn

class RNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, 
                 bidirectional, dropout, pad_idx):

        super().__init__()

        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)

        self.rnn = nn.LSTM(embedding_dim, 
                           hidden_dim, 
                           num_layers=n_layers, 
                           bidirectional=bidirectional, 
                           dropout=dropout)

        self.fc = nn.Linear(hidden_dim * 2, output_dim)

        self.dropout = nn.Dropout(dropout)

    def forward(self, text, text_lengths):

        #text = [sent len, batch size]

        embedded = self.dropout(self.embedding(text))

        #embedded = [sent len, batch size, emb dim]

        #pack sequence
        packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths)

        packed_output, (hidden, cell) = self.rnn(packed_embedded)

        #unpack sequence
        output, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_output)

        #output = [sent len, batch size, hid dim * num directions]
        #output over padding tokens are zero tensors

        #hidden = [num layers * num directions, batch size, hid dim]
        #cell = [num layers * num directions, batch size, hid dim]

        #concat the final forward (hidden[-2,:,:]) and backward (hidden[-1,:,:]) hidden layers
        #and apply dropout

        hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1))

        #hidden = [batch size, hid dim * num directions]

        return self.fc(hidden)    

INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
HIDDEN_DIM = 256
OUTPUT_DIM = len(LABEL.vocab) ### changed from previous value (1)
N_LAYERS = 2
BIDIRECTIONAL = True
DROPOUT = 0.5
PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]

model = RNN(INPUT_DIM, 
            EMBEDDING_DIM, 
            HIDDEN_DIM, 
            OUTPUT_DIM, 
            N_LAYERS, 
            BIDIRECTIONAL, 
            DROPOUT, 
            PAD_IDX)

import torch.optim as optim
optimizer = optim.Adam(model.parameters())


criterion = nn.CrossEntropyLoss() # Previously: criterion = nn.BCEWithLogitsLoss()
model = model.to(device)
criterion = criterion.to(device)

def binary_accuracy(preds, y):
    """
    Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8
    """

    #round predictions to the closest integer
    rounded_preds = torch.round(torch.sigmoid(preds))
    correct = (rounded_preds == y).float() #convert into float for division 
    acc = correct.sum() / len(correct)
    return acc
def train(model, iterator, optimizer, criterion):

    epoch_loss = 0
    epoch_acc = 0

    model.train()

    for batch in iterator:

        optimizer.zero_grad()

        text, text_lengths = batch.text

        predictions = model(text, text_lengths).squeeze(1)

        loss = criterion(predictions, batch.label)

        acc = binary_accuracy(predictions, batch.label)

        loss.backward()

        optimizer.step()

        epoch_loss += loss.item()
        epoch_acc += acc.item()

    return epoch_loss / len(iterator), epoch_acc / len(iterator)

def evaluate(model, iterator, criterion):

    epoch_loss = 0
    epoch_acc = 0

    model.eval()

    with torch.no_grad():

        for batch in iterator:

            text, text_lengths = batch.text

            predictions = model(text, text_lengths).squeeze(1)

            loss = criterion(predictions, batch.label)

            acc = binary_accuracy(predictions, batch.label)

            epoch_loss += loss.item()
            epoch_acc += acc.item()

    return epoch_loss / len(iterator), epoch_acc / len(iterator)

import time

def epoch_time(start_time, end_time):
    elapsed_time = end_time - start_time
    elapsed_mins = int(elapsed_time / 60)
    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
    return elapsed_mins, elapsed_secs

上面的所有代码运行顺利，这是触发错误的下一个代码块：

N_EPOCHS = 5

best_valid_loss = float('inf')

for epoch in range(N_EPOCHS):

    start_time = time.time()

    train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
    valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)

    end_time = time.time()

    epoch_mins, epoch_secs = epoch_time(start_time, end_time)

    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'tut2-model.pt')

    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')
    print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
    print(f'\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-888-c1b298b1eeea> in <module>
      7     start_time = time.time()
      8 
----> 9     train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
     10     valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
     11 

<ipython-input-885-9a57198441ec> in train(model, iterator, optimizer, criterion)
      6     model.train()
      7 
----> 8     for batch in iterator:
      9 
     10         optimizer.zero_grad()

~/opt/anaconda3/lib/python3.7/site-packages/torchtext/data/iterator.py in __iter__(self)
    140         while True:
    141             self.init_epoch()
--> 142             for idx, minibatch in enumerate(self.batches):
    143                 # fast-forward if loaded from state
    144                 if self._iterations_this_epoch > idx:

~/opt/anaconda3/lib/python3.7/site-packages/torchtext/data/iterator.py in pool(data, batch_size, key, batch_size_fn, random_shuffler, shuffle, sort_within_batch)
    284     for p in batch(data, batch_size * 100, batch_size_fn):
    285         p_batch = batch(sorted(p, key=key), batch_size, batch_size_fn) \
--> 286             if sort_within_batch \
    287             else batch(p, batch_size, batch_size_fn)
    288         if shuffle:

TypeError: '<' not supported between instances of 'Example' and 'Example'

最后，PyTorch论坛对此错误开放了一个问题，但是，产生该错误的代码并不相似，因此我认为这是一个单独的问题。