Forecasting with a GRU using PyTorch

In this recipe, you will use the same train_model_pt function from the previous Forecasting with an RNN using PyTorch recipe. The function trains the model, captures loss function scores, evaluates the model, makes a forecast using the test set, and finally, produces plots for further evaluation.

You will still need to define a new class for the GRU model.

How to do it...

1. Create a GRU class that will inherit from Module class. The setup will be similar to the RNN class, but unlike the LSTM class, you only handle one state (the hidden state):

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