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

Square Model II — The Quickening
This model is pretty much the same as the original "Square Model," except for one
difference: Its recurrent neural network is not a plain RNN anymore, but a GRU.
Everything else stays exactly the same.
Model Configuration
1 class SquareModelGRU(nn.Module):
2 def __init__(self, n_features, hidden_dim, n_outputs):
3 super(SquareModelGRU, self).__init__()
4 self.hidden_dim = hidden_dim
5 self.n_features = n_features
6 self.n_outputs = n_outputs
7 self.hidden = None
8 # Simple GRU
9 self.basic_rnn = nn.GRU(self.n_features,
10 self.hidden_dim,
11 batch_first=True) 1
12 # Classifier to produce as many logits as outputs
13 self.classifier = nn.Linear(self.hidden_dim,
14 self.n_outputs)
15
16 def forward(self, X):
17 # X is batch first (N, L, F)
18 # output is (N, L, H)
19 # final hidden state is (1, N, H)
20 batch_first_output, self.hidden = self.basic_rnn(X)
21
22 # only last item in sequence (N, 1, H)
23 last_output = batch_first_output[:, -1]
24 # classifier will output (N, 1, n_outputs)
25 out = self.classifier(last_output)
26
27 # final output is (N, n_outputs)
28 return out.view(-1, self.n_outputs)
1 The ONLY change in the code: from nn.RNN to nn.GRU
We’ll be using the same data loaders again, so we’re going directly to the model
configuration and training.
Gated Recurrent Units (GRUs) | 635