human
I am working on the file "18-11-22-Deep-Learning-with-PyTorch/05-Recurrent Neural Networks/01 - Simple_RNN.ipynb".
The first task is:
Define the RNN
Next, we define an RNN in PyTorch. We'll use nn.RNN to create an RNN layer, then we'll add a last, fully-connected layer to get the output size that we want. An RNN takes in a number of parameters:
* input_size - the size of the input
* hidden_dim - the number of features in the RNN output and in the hidden state
* n_layers - the number of layers that make up the RNN, typically 1-3.
greater than 1 means that you'll create a stacked RNN
* batch_first - whether or not the input/output of the RNN will have the batch_size as the first dimension (batch_size, seq_length, hidden_dim)
Take a look at the RNN documentation to read more about recurrent layers.
Can you write Python program for it?
 What is a the Python program you requested?
assistant
#Here is the Python program you requested.

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

        # Define an RNN with specified parameters
        # Batch_first means that the first dim of the input and output will be the batch_size
        self.rnn = nn.RNN(input_size=input_size,
                          hidden_size=hidden_dim,
                          num_layers=n_layers,
                          batch_first=True)
        
        # last, fully-connected layer
        self.fc = nn.Linear(hidden_dim, output_size)

    def forward(self, x, hidden):
        # x (batch_size, seq_length, input_size)
        # hidden (n_layers, batch_size, hidden_dim)
        # r_out (batch_size, time_step, hidden_size)
        batch_size = x.size(0)
        
        # Get RNN outputs
        r_out, hidden = self.rnn(x, hidden)
        # Shape output to be (batch_size*seq_length, hidden_dim)
        r_out = r_out.view(-1, self.hidden_dim)  
        
        # Get final output 
        output = self.fc(r_out)
        
        return output, hidden