Forward Propagate RNN using Pytorch

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I am trying to create an RNN forward pass method that can take a variable input, hidden, and output size and create the rnn cells needed. To me, it seems like I am passing the correct variables to self.rnn_cell -- the input values of x and the previous hidden layer. However, the error I receive is included below.

I have also tried using x[i] and x[:,i,i] (as suggested by my professor) to no avail. I am confused and just looking for guidance as to whether or not I am doing the right thing here. My prof suggested that since I keep receiving errors, I should restart the kernel in jupyter notebook and rerun code. I have, and I receive the same errors...

Please let me know if you need additional context.

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size

        self.rnn_cell = nn.RNNCell(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, x):
        """
        x: size [seq_length, 1, input_size]
        """
        h = torch.zeros(x.size(1), self.hidden_size)

        for i in range(x.size(0)):
            ### START YOUR CODE ###
            h = self.rnn_cell(x[:,:,i], h)
            ### END YOUR CODE ###

        ### START YOUR CODE ###
        # Hint: first call fc, then call softmax
        out = self.softmax(self.fc(self.hidden_size, h.size(0)))
        ### END YOUR CODE ###

        return out 
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

I am not an expert at RNNs but giving it a try.

class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
    super(RNN, self).__init__()
    self.hidden_size = hidden_size

    self.rnn_cell = nn.RNN(input_size, hidden_size)
    self.fc = nn.Linear(hidden_size, output_size)


def forward(self, x):
    """
    x: size [seq_length, 1, input_size]
    """
    h = torch.zeros(num_layers(hidden), x.size(0), self.hidden_size)


        ### START YOUR CODE ###
    out,hidden = self.rnn_cell(x, h)
        ### END YOUR CODE ###

    ### START YOUR CODE ###
    # Hint: first call fc, then call softmax
    out = out.contiguous().view(-1, self.hidden_dim) #You need to reshape the output to fit the FC layer
    out = self.fc(out)
    return F.softmax(out)
    ### END YOUR CODE ###

    return out 

Please try running this and let me know in case of errors or any doubts. (Cannot ask you details as I can't comment rn.)

If you got any idea from my answer, do support.

Recurrent Neural Network with Pytorch, https://www.kaggle.com/kanncaa1/long-short-term-memory-with-pytorch Clear gradients optimizer.zero_grad() # Forward propagation outputs = model(train)� If a torch.nn.utils.rnn.PackedSequence has been given as the input, the output will also be a packed sequence. For the unpacked case, the directions can be separated using output.view(seq_len, batch, num_directions, hidden_size), with forward and backward being direction 0 and 1 respectively. Similarly, the directions can be separated in the

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size

        self.rnn_cell = nn.RNNCell(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, x):
        """
        x: size [seq_length, 1, input_size]
        """
        h = torch.zeros(x.size(1), self.hidden_size)

        for i in range(x.size(0)):
            ### START YOUR CODE ###
            h = self.rnn_cell(x[i,:,:], h)
            ### END YOUR CODE ###

        ### START YOUR CODE ###
        # Hint: first call fc, then call softmax
        out = self.fc(h)
        out = self.softmax(out)
        ### END YOUR CODE ###

        return out

Beginner's Guide on Recurrent Neural Networks with PyTorch, A Beginner's Guide on Recurrent Neural Networks with PyTorch Overview of the feed-forward neural network and RNN structures Combined with the forward pass, back-propagation is looped over and again, allowing the� This means you can implement a RNN in a very “pure” way, as regular feed-forward layers. This RNN module (mostly copied from the PyTorch for Torch users tutorial) is just 2 linear layers which operate on an input and hidden state, with a LogSoftmax layer after the output.

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size

        self.rnn_cell = nn.RNNCell(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, x):
        """
        x: size [seq_length, 1, input_size]
        """
        h = torch.zeros(x.size(1), self.hidden_size)

        for i in range(x.size(0)):
            ### START YOUR CODE ###
            h = self.rnn_cell(x[i,:,:], h)
            ### END YOUR CODE ###

        ### START YOUR CODE ###
        # Hint: first call fc, then call softmax
        out = self.softmax(self.fc(h))
        ### END YOUR CODE ###

        return out

Recurrent Neural Networks (RNN), Example: An LSTM for Part-of-Speech Tagging. In this section, we will use an LSTM to get part of speech tags. We will not use Viterbi or Forward-Backward or� Recurrent Neural Network (RNN) - Forward Propagation The standard neural networks cannot take into account the sequence that come before or after a data point. For example, to identify a name in a sentence, we need knowledge of the other words surrounding it to identify it.

Sequence Models and Long-Short Term Memory Networks, Pruning Tutorial � (beta) Dynamic Quantization on an LSTM Word Language Model � (beta) Module contains layers, and a method forward(input) that returns the output . Propagate gradients back into the network's parameters; Update the weights of the You can use any of the Tensor operations in the forward function. Hi, This maybe a naive question to ask but i am a beginner in pytorch and i am unable to figure out how pytorch is doing the forward propagation. The code i am using is shown below- class MyModel(nn.Module): def _…

Neural Networks — PyTorch Tutorials 1.6.0 documentation, I assume you have at least installed PyTorch, know Python, and understand Before autograd, creating a recurrent neural network in Torch involved This means you can implement a RNN in a very “pure” way, as regular feed-forward layers. Compare final output to target; Back-propagate; Return the output and loss. For PyTorch, we have to individually compute all the steps including the loss function, optimizer, back-propagation and update each step in a for loop. TensorFlow In TensorFlow, the Fashion MNIST is an array with two dimensions, we have to reshape our training dataset to a tensor and convert the labels from integers to binary class matrix.

NLP From Scratch: Classifying Names with a Character-Level RNN , Get started with pytorch, how it works and learn how to build a neural network. CNN); Use Case 3: Sentiment Text Classification (Text Data, RNN); Use Forward Propagation: In this step, activations are calculated at every� im using google colab, which has the default version of pytorch 1.3, and CUDA 10.1

Comments
  • Please add more to the answer that explains why the OP should use your code and how it solves their issue. As it is now it is not really obvious what changes you made.
  • Please add more to the answer that explains why the OP should use your code and how it solves their issue. As it is now it is not really obvious what changes you made. Further it seems that your proposed solution is basically identical with the answer made by Dev Taylor.