 # Loss function that uses the output to calculate index which is used to get value from an array and then loss is calculated

In my neural network (RNN), I am defining the loss function such that the output of the neural network is used to find the index (binary) and then the index is used to extract the required element from an array which in turn will be used to calculate MSELoss.

However, the program gives `parameter().grad = None` error which is mostly because the graph is breaking somewhere. What is the problem with the error function defined.

Framework: Pytorch

The codes are as follow:
Neural Network:

``````class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.hidden_size = 8
# self.input_size = 2
self.h2o = nn.Linear(self.hidden_size, 1)
self.h2h = nn.Linear(self.hidden_size, self.hidden_size)
self.sigmoid = nn.Sigmoid()
def forward(self,hidden):
output = self.h2o(hidden)
output = self.sigmoid(output)
hidden = self.h2h(hidden)
return output, hidden
def init_hidden(self):
``````

Loss Function, train step and training

``````rnn = RNN()
criterion = nn.MSELoss()

def loss_function(previous, output, index):
code = 2*(output > 0.5).long()
current = Q_m2[code:code+2, i]
return criterion(current, previous), current

def train_step():
hidden = rnn.init_hidden()
# Q_m2.create_graph = True
loss = 0
previous = Q_m[0:2, 0]
for i in range(1, samples):
output, hidden = rnn(hidden)
l, previous = loss_function(previous, output, i)
loss+=l
loss.backward()
for p in rnn.parameters():
return output, loss.item()/(samples - 1)

def training(epochs):
running_loss = 0
for i in range(epochs):
output, loss = train_step()
print(f'Epoch Number: {i+1}, Loss: {loss}')
running_loss +=loss
``````

Q_m2

``````Q_m = np.zeros((4, samples))
for i in range(samples):
Q_m[:,i] = q_x(U_m[:,i])
Q_m = torch.FloatTensor(Q_m)
Q_m2 = Q_m
Q_m2.create_graph = True
``````

Error:

``````<ipython-input-36-feefd257c97a> in train_step()
22   for p in rnn.parameters():
24   return output, loss.item()/(samples - 1)
25

AttributeError: 'NoneType' object has no attribute 'data'
``````

I haven’t looked at your code or error messages in any detail.

But, yes, your “graph is breaking.”

`code`, as a function of `output`, is not (usefully) differentiable, so,
regardless of what you subsequently do with `code`, you won’t be
able to back propagate through it.

I have no idea whether this would make sense for your use case,
but one possibility would run as follows:

As I read it, `code` is calculated to be either `0` or `2`. You could
instead interpret `output` (processed appropriately, as necessary)
to be the probability that `code` should be `0` vs. `2`, and then use
that probability to form a weighted average of the `0` and `2` entries
in your `Q_m2` array.

This will be differentiable and you will be able to backpropagate (but
I’m not saying that it would make sense …).

Best.

K. Frank

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