I am running the time sequence prediction example 22 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 7.
Any clues as to why my loss is getting stuck?