I am implementing Stateful LSTM:
class StatefulFraudLSTM(nn.Module):
def init(self, num_features, hidden_size=100, hidden_size_lstm=100, num_layers_lstm=3, dropout_lstm=0, batch_size=128):
super(StatefulFraudLSTM, self).init()
# Parameters
self.num_features = num_features
self.hidden_size = hidden_size
self.hidden_size_lstm = hidden_size_lstm
self.num_layers_lstm = num_layers_lstm
self.batch_size = batch_size
# Representation learning part
self.lstm = nn.LSTM(num_features, hidden_size_lstm, num_layers_lstm, batch_first=True, dropout=dropout_lstm)
# Representation to hidden
self.fc1 = nn.Linear(hidden_size_lstm, hidden_size)
self.relu = nn.ReLU()
# Hidden to output
self.fc2 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
# Initialize hidden and cell states
self.hidden = self.init_hidden()
def init_hidden(self):
# Initialize hidden and cell states with zeros
h0 = torch.zeros(self.num_layers_lstm, self.batch_size, self.hidden_size_lstm).to(device)
c0 = torch.zeros(self.num_layers_lstm, self.batch_size, self.hidden_size_lstm).to(device)
return (h0, c0)
def forward(self, x):
representation, self.hidden = self.lstm(x.transpose(1, 2), self.hidden)
hidden_state = self.hidden[0][-1]
out = self.fc1(hidden_state)
out = self.relu(out)
out = self.fc2(out)
out = self.sigmoid(out)
return out
Getting below error:
RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed. Specify retain_graph=True when calling backward the first time.