dmlc--dgl
Hongzhi (Steve), Chen
704bcaf6dd
Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
34 行
809 B
Python
可执行文件
34 行
809 B
Python
可执行文件
import dgl
|
|
import numpy as np
|
|
import scipy.sparse as sparse
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
|
|
class NormalizationLayer(nn.Module):
|
|
def __init__(self, mean, std):
|
|
self.mean = mean
|
|
self.std = std
|
|
|
|
# Here we shall expect mean and std be scaler
|
|
def normalize(self, x):
|
|
return (x - self.mean) / self.std
|
|
|
|
def denormalize(self, x):
|
|
return x * self.std + self.mean
|
|
|
|
|
|
def masked_mae_loss(y_pred, y_true):
|
|
mask = (y_true != 0).float()
|
|
mask /= mask.mean()
|
|
loss = torch.abs(y_pred - y_true)
|
|
loss = loss * mask
|
|
# trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
|
|
loss[loss != loss] = 0
|
|
return loss.mean()
|
|
|
|
|
|
def get_learning_rate(optimizer):
|
|
for param in optimizer.param_groups:
|
|
return param["lr"]
|