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Correcting to RNN #136

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Correcting to RNN #136

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6 changes: 3 additions & 3 deletions tutorials/02-intermediate/recurrent_neural_network/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)

def forward(self, x):
Expand All @@ -51,7 +51,7 @@ def forward(self, x):
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)

# Forward propagate LSTM
out, _ = self.lstm(x, (h0, c0)) # out: tensor of shape (batch_size, seq_length, hidden_size)
out, _ = self.rnn(x, (h0, c0)) # out: tensor of shape (batch_size, seq_length, hidden_size)

# Decode the hidden state of the last time step
out = self.fc(out[:, -1, :])
Expand Down Expand Up @@ -99,4 +99,4 @@ def forward(self, x):
print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')
torch.save(model.state_dict(), 'model.ckpt')