wehub-resource-sync
34 lines
809 B
Python
Executable File
34 lines
809 B
Python
Executable File
import dgl
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import numpy as np
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import scipy.sparse as sparse
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import torch
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import torch.nn as nn
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class NormalizationLayer(nn.Module):
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def __init__(self, mean, std):
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self.mean = mean
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self.std = std
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# Here we shall expect mean and std be scaler
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def normalize(self, x):
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return (x - self.mean) / self.std
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def denormalize(self, x):
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return x * self.std + self.mean
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def masked_mae_loss(y_pred, y_true):
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mask = (y_true != 0).float()
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mask /= mask.mean()
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loss = torch.abs(y_pred - y_true)
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loss = loss * mask
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# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
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loss[loss != loss] = 0
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return loss.mean()
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def get_learning_rate(optimizer):
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for param in optimizer.param_groups:
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return param["lr"]
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