Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Data Preparation
1 def pack_collate(batch):
2 X = [item[0] for item in batch]
3 y = [item[1] for item in batch]
4 X_pack = rnn_utils.pack_sequence(X, enforce_sorted=False)
5
6 return X_pack, torch.as_tensor(y).view(-1, 1)
7
8 train_var_loader = DataLoader(train_var_data,
9 batch_size=16,
10 shuffle=True,
11 collate_fn=pack_collate)
There Can Be Only ONE … Model
We’ve developed many models throughout this chapter, depending both on the
type of recurrent layer that was used (RNN, GRU, or LSTM) and on the type of
sequence (packed or not). The model below, though, is able to handle different
configurations:
• Its rnn_layer argument allows you to use whichever recurrent layer you
prefer.
• The **kwargs argument allows you to further configure the recurrent layer
(using num_layers and bidirectional arguments, for example).
• The output dimension of the recurrent layer is automatically computed to
build a matching linear layer.
• If the input is a packed sequence, it handles the unpacking and fancy indexing
to retrieve the actual last hidden state.
Model Configuration
1 class SquareModelOne(nn.Module):
2 def __init__(self, n_features, hidden_dim, n_outputs,
3 rnn_layer=nn.LSTM, **kwargs):
4 super(SquareModelOne, self).__init__()
5 self.hidden_dim = hidden_dim
6 self.n_features = n_features
7 self.n_outputs = n_outputs
8 self.hidden = None
Putting It All Together | 681