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fix the bugs running in Python3 #26

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fix the bugs running in Python3 #26

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8 changes: 4 additions & 4 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,8 +29,8 @@ def evaluate(data, X, Y, model, evaluateL2, evaluateL1, batch_size):
test = torch.cat((test, Y));

scale = data.scale.expand(output.size(0), data.m)
total_loss += evaluateL2(output * scale, Y * scale).data[0]
total_loss_l1 += evaluateL1(output * scale, Y * scale).data[0]
total_loss += evaluateL2(output * scale, Y * scale).item()
total_loss_l1 += evaluateL1(output * scale, Y * scale).item()
n_samples += (output.size(0) * data.m);
rse = math.sqrt(total_loss / n_samples)/data.rse
rae = (total_loss_l1/n_samples)/data.rae
Expand All @@ -42,7 +42,7 @@ def evaluate(data, X, Y, model, evaluateL2, evaluateL1, batch_size):
mean_p = predict.mean(axis = 0)
mean_g = Ytest.mean(axis = 0)
index = (sigma_g!=0);
correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis = 0)/(sigma_p * sigma_g);
correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis = 0)/(sigma_p * sigma_g + 1e-15);
correlation = (correlation[index]).mean();
return rse, rae, correlation;

Expand All @@ -57,7 +57,7 @@ def train(data, X, Y, model, criterion, optim, batch_size):
loss = criterion(output * scale, Y * scale);
loss.backward();
grad_norm = optim.step();
total_loss += loss.data[0];
total_loss += loss.item();
n_samples += (output.size(0) * data.m);
return total_loss / n_samples

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2 changes: 1 addition & 1 deletion models/LSTNet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@ def __init__(self, args, data):
self.hidS = args.hidSkip;
self.Ck = args.CNN_kernel;
self.skip = args.skip;
self.pt = (self.P - self.Ck)/self.skip
self.pt = (self.P - self.Ck)//self.skip
self.hw = args.highway_window
self.conv1 = nn.Conv2d(1, self.hidC, kernel_size = (self.Ck, self.m));
self.GRU1 = nn.GRU(self.hidC, self.hidR);
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