RuntimeError: Expected hidden[0] size (2, 20, 256), got (2, 50, 256)

I’m building a LSTM model to classify test into multiple classes and I get the following error when training the model. I’ve used a softmax activation as the final layer. There are 44 possible classes for the texts. Below is the code snippet and network architecture. any help will be greatly appreciated. I’m using CPU to train the model for now, post which I’ll move to GPU.

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)))

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)

ClassificationRNN(
(embedding): Embedding(5865, 100)
(lstm): LSTM(100, 256, num_layers=2, batch_first=True, dropout=0.5)
(fc): Linear(in_features=256, out_features=44, bias=True)
(sof): LogSoftmax()
(dropout): Dropout(p=0.3)
)

you can print the shape of the hidden[0] at each step.
then, carefully check it.

@DoubtWang Don’t see a provision to include that for debugging purposes as I believe this happens within the LSTM package.

Can you give detailed code?
Besides, explain the shape of inputs and label?

Input is a tensor from the data loader which has a batch size of 50 and sequence length of 20. Labels is uni dimensional tensor of possbile values ranging from 0 to 43 (44 values. Pls find below detailed code.

import torch.nn as nn

class ClassificationRNN(nn.Module):
“”"
The RNN model that will be used to perform Sentiment analysis.
“”"

def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5):
    """
    Initialize the model by setting up the layers.
    """
    super(ClassificationRNN, self).__init__()

    self.output_size = output_size
    self.n_layers = n_layers
    self.hidden_dim = hidden_dim
    
    # define all layers
    # embedding and LSTM layers
    self.embedding = nn.Embedding(vocab_size,embedding_dim)
    self.lstm = nn.LSTM(embedding_dim,hidden_dim,n_layers,dropout=drop_prob, batch_first=True)
    
    #full connected layer & softmax
    self.fc = nn.Linear(hidden_dim,output_size)
    self.sof = nn.LogSoftmax(dim=1)
    
    #dropout layer
    self.dropout = nn.Dropout(0.3)

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 shape',output.shape)

print(‘labels:’,labels.float())

    # calculate the loss and perform backprop
    loss = criterion(output.squeeze(), 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.squeeze(), 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)))

I have run your code, but I do not find any bug.
Give you some advice:

  1. If you initialized hidden state to zero, no operation required.

If (h_0, c_0) is not provided, both h_0 and c_0 default to zero.

  1. If you set batch_first=True, and the shape of inputs is (batch_size, len)(50, 20)

So, the hidden[0] should be (2, 50, 256) instead of (2, 20, 256)

h_0 of shape (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch.

  1. For NLLLoss function,

Input: (N, C) where C = number of classes, and Target: (N) where each value is 0≤targets[i]≤C−1
So, the code should be:

        # reshape to be batch_size first
        soft_out = soft_out.view(batch_size, -1, self.output_size)
        
        soft_out = soft_out[:, -1, :] # get last batch of labels

4.If you use the lstm to complete the classification task, you can use the finally state or self-attention based on my experience.

Thanks @DoubtWang. Your suggestions were very useful. I found the problem. One of the batches in the validation data loader has incorrect dimension. Pls refer attached image. But, I dont know how to remove that batch. Could you pls suggest a solution for that?

You can try and see if it works.

def init_hidden(self):
    if (train_on_gpu):
        hidden = (weight.new(self.n_layers, 1, self.hidden_dim).zero_().cuda(),
                         weight.new(self.n_layers, 1, self.hidden_dim).zero_().cuda())
    else:
        hidden = (weight.new(self.n_layers, 1, self.hidden_dim).zero_(),
                       weight.new(self.n_layers, 1, self.hidden_dim).zero_())
    return hidden
batch_size = inputs.size(0)
h = tuple([each.repeat(1, batch_size, 1).data for each in h])
batch_size = inputs.size(0)
val_h = tuple([each.repeat(1, batch_size, 1).data for each in val_h])

I think this is a method which can solve this problem.
Can you understand that?

Thanks @DoubtWang, your solution as I understand it, is trying to fix the hidden layer, but I believe the issue I’m facing is with the input dimension of few bad batches in the Validation dataloader. If I can fix the dataloader, I believe this issue would be resolved.

I worked around the problem by skipping the training & validation for those batches which were not of the shape (50,20). Below is a simple if condition that helped me to solve the problem.

if( (inputs.shape[0],inputs.shape[1]) != (batch_size,seq_length)):
print(‘Validation - Input Shape Issue:’,inputs.shape)
continue

1 Like

This was so helpful! Thank you!