Я получаю эту ошибку при попытке построить мультиклассовую сеть классификации текста с использованием LSTM (RNN).Код, кажется, работает нормально для обучающей части кода, тогда как он выдает ошибку для части проверки.Ниже приведена сетевая архитектура и обучающий код.Цените любую помощь здесь.
Я попытался взять существующий код, который предсказывает настроение, используя RNN, и заменил сигмоид с функцией softmax в функции окончания и потери с BCE Loss на NLLLoss ()
def forward(self, x, hidden):
"""
Perform a forward pass of our model on some input and hidden state.
"""
batch_size = x.size(0)
embeds = self.embedding(x)
lstm_out,hidden= self.lstm(embeds,hidden)
# stack up lstm outputs
lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim)
# dropout and fully-connected layer
out = self.dropout(lstm_out)
out = self.fc(out)
# softmax function
soft_out = self.sof(out)
# reshape to be batch_size first
soft_out = soft_out.view(batch_size, -1)
# soft_out = soft_out[:, -1] # get last batch of labels
# return last sigmoid output and hidden state
return soft_out, hidden
def init_hidden(self, batch_size):
''' Initializes hidden state '''
# Create two new tensors with sizes n_layers x batch_size x hidden_dim,
# initialized to zero, for hidden state and cell state of LSTM
weight = next(self.parameters()).data
if (train_on_gpu):
hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda(),
weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda())
else:
hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(),
weight.new(self.n_layers, batch_size, self.hidden_dim).zero_())
return hidden
# Instantiate the model w/ hyperparams
vocab_size = len(vocab_to_int)+1
output_size = 44
embedding_dim = 100
hidden_dim = 256
n_layers = 2
net = ClassificationRNN(vocab_size, output_size, embedding_dim, hidden_dim, n_layers)
print(net)
# loss and optimization functions
lr=0.001
criterion = nn.NLLLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
# training params
epochs = 4 # 3-4 is approx where I noticed the validation loss stop decreasing
counter = 0
print_every = 100
clip=5 # gradient clipping
# move model to GPU, if available
if(train_on_gpu):
net.cuda()
net.train()
# train for some number of epochs
for e in range(epochs):
# initialize hidden state
h = net.init_hidden(batch_size)
# batch loop
for inputs, labels in train_loader:
counter += 1
if(train_on_gpu):
inputs, labels = inputs.cuda(), labels.cuda()
# Creating new variables for the hidden state, otherwise
# we'd backprop through the entire training history
h = tuple([each.data for each in h])
# zero accumulated gradients
net.zero_grad()
# get the output from the model
output, h = net(inputs, h)
# print('output:',output.squeeze())
# print('labels:',labels.float())
# calculate the loss and perform backprop
loss = criterion(output, labels)
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
nn.utils.clip_grad_norm_(net.parameters(), clip)
optimizer.step()
# loss stats
if counter % print_every == 0:
# Get validation loss
val_h = net.init_hidden(batch_size)
val_losses = []
net.eval()
for inputs, labels in valid_loader:
# Creating new variables for the hidden state, otherwise
# we'd backprop through the entire training history
val_h = tuple([each.data for each in val_h])
if(train_on_gpu):
inputs, labels = inputs.cuda(), labels.cuda()
output, val_h = net(inputs, val_h)
val_loss = criterion(output, labels)
val_losses.append(val_loss.item())
net.train()
print("Epoch: {}/{}...".format(e+1, epochs),
"Step: {}...".format(counter),
"Loss: {:.6f}...".format(loss.item()),
"Val Loss: {:.6f}".format(np.mean(val_losses)))
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-41-805ed880b453> in <module>()
58 inputs, labels = inputs.cuda(), labels.cuda()
59
---> 60 output, val_h = net(inputs, val_h)
61
62 val_loss = criterion(output, labels)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
<ipython-input-38-dbfb8d384231> in forward(self, x, hidden)
34 batch_size = x.size(0)
35 embeds = self.embedding(x)
---> 36 lstm_out,hidden= self.lstm(embeds,hidden)
37
38 # stack up lstm outputs