How to apply LSTM in the FC layer of CNN?

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()

        input 3 channel

        #self.fc1 = nn.Linear(17664, 1024)

        self.fc1 = nn.LSTM(      

        self.fc2 = nn.Linear(1024, 1)

    def forward(self, x):

        CNN forward

        x = x.view(-1, self.num_flat_features(x))
        x, _ = self.fc1(x, None)  
        x = self.fc2(x[:, -1, :])
        return x

 UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greately increasing memory usage. To compact weights again call flatten_parameters().
  x, _ = self.fc1(x, None)
Traceback (most recent call last):
  File "", line 444, in <module>
    train(Train_Loader, model, criterion, optimizer, Now_Epoch)
  File "", line 308, in train
    output = model(batch_input)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/modules/", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/", line 60, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/", line 70, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/", line 67, in parallel_apply
    raise output
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/", line 42, in _worker
    output = module(*input, **kwargs)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/modules/", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "", line 52, in forward
    x, _ = self.fc1(x, None)  
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/modules/", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/modules/", line 162, in forward
    output, hidden = func(input, self.all_weights, hx)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/", line 351, in forward
    return func(input, *fargs, **fkwargs)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/autograd/", line 284, in _do_forward
    flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/autograd/", line 306, in forward
    result = self.forward_extended(*nested_tensors)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/", line 293, in forward_extended
    cudnn.rnn.forward(self, input, hx, weight, output, hy)
  File "/home/leaf/anaconda3/lib/python3.6/site-packages/torch/backends/cudnn/", line 208, in forward
    'input must have 3 dimensions, got {}'.format(input.dim()))
RuntimeError: input must have 3 dimensions, got 2

The first Warning seems caused by nn.DataParallel() but I don’t know why.

I don’t know what’s wrong with the LSTM part ?

It doesn’t even make sense. Your input is not sequential data.

1 Like

@SimonW I have a series of images from the videos, so I want to use LSTM to model the time relation of these images.
I thought a batch of images can be the sequential data. Can you tell me what should I do ? Thank you !

You have a lot of examples from DQN implementations (for ex

In your case, you just use 1-layer LSTM, so I would suggest nn.LSTMCell instead of nn.LSTM.:

# in your constructor:
self.lstm = nn.LSTMCell(17664, 1024)
self.fc = nn.Linear(1024, 1)

# in your forward:
def forward(self, inputs):
    x, (hx, cx) = inputs
    # *CNN forward over x*
    x = x.view(-1, self.num_flat_features(x))
    hx, cx = self.lstm(x, (hx, cx))
    x = hx
    x = self.fc(x)
    return x, (hx, cx)
1 Like

If you want each image to be the input at a timestep, then (unless you flatten them) you need to write your own RNNCell that does convolutions :slight_smile:

1 Like

Hi Wang, I have no idea how to implement the convolutional RNN, is there any exmple? Thank you.

RNN input is 3 dimensions, but after flat it becomes 2D. How to handle this?