I am running the time sequence prediction example with some actual time-series data instead of sine waves and the loss function gets stuck.
The data in the example is (100,1000), my new data is (40,1000), and I don’t see anywhere in the code where this could be the issue.
class Sequence(nn.Module):
def init(self):
super(Sequence, self).init()
self.lstm1 = nn.LSTMCell(1,51)
self.lstm2 = nn.LSTMCell(51,1)
def forward(self, input, future=0): outputs = [] h_t = Variable(torch.zeros(input.size(0), 51).double(), requires_grad=False) c_t = Variable(torch.zeros(input.size(0), 51).double(), requires_grad=False) h_t2 = Variable(torch.zeros(input.size(0), 1).double(), requires_grad=False) c_t2 = Variable(torch.zeros(input.size(0), 1).double(), requires_grad=False)
for i, input_t in enumerate(input.chunk(input.size(1), dim=1)): # Split tensor into tuples of size input.size(0) h_t, c_t = self.lstm1(input_t, (h_t, c_t)) # Two layer LSTM (2 periods lookbehind) h_t2, c_t2 = self.lstm2(c_t, (h_t2, c_t2)) # Second period (t-2): input is lstm cell of t-1 outputs += [c_t2]
for i in range(future): # if we should predict the future h_t, c_t = self.lstm1(c_t2, (h_t, c_t)) h_t2, c_t2 = self.lstm2(c_t, (h_t2, c_t2)) outputs += [c_t2]
outputs = torch.stack(outputs, 1).squeeze(2) return outputs
if name == ‘main’:
# Set seed to 0 np.random.seed(0) torch.manual_seed(0)
# Load data and generate training set data = torch.load('traindata_EX1.pt') input = Variable(torch.from_numpy(data[3:,:-1]), requires_grad=False) target = Variable(torch.from_numpy(data[3:,1:]), requires_grad=False)
# Build the model seq = Sequence() seq.double() # Casts all parameters and buffers to double datatype. criterion = nn.MSELoss() # Set loss function to Mean Squared Error
# use LBFGS as optimizer since we can load the whole data to train optimizer = optim.LBFGS(seq.parameters())
# Begin to train for i in range(15): print('STEP:', i)
def closure(): optimizer.zero_grad() # Reset gradients each pass. out = seq(input) # Calculate the predicted value with the given parameters W* loss = criterion(out, target) # Calculate error. print('Loss:', loss.data.numpy()[0]) loss.backward() # Backpropagate return loss
optimizer.step(closure) # Move parameters to fastest changing gradient (?)
# Predict future = 1000 pred = seq(input[:3], future = future) y = pred.data.numpy()
# Draw the result plt.figure() #figsize=(30,10) plt.title('Predict future values for time sequences\n(Dashlines are predicted values)', fontsize=30) plt.xlabel('x', fontsize=20) plt.ylabel('y', fontsize=20) plt.xticks(fontsize=20) plt.yticks(fontsize=20) def draw(yi, color): plt.plot(np.arange(input.size(1)), yi[:input.size(1)], color, linewidth = 2.0) plt.plot(np.arange(input.size(1), input.size(1) + future), yi[input.size(1):], color + ':', linewidth = 2.0) draw(y[0], 'r') draw(y[1], 'g') draw(y[2], 'b') plt.savefig('plots/predict{}_V1.pdf'.format(i)) plt.close()
The file traindata_EX1.pt is here.
Any clues as to why my loss is getting stuck?