import torch
import numpy as np
from sklearn.metrics import confusion_matrix
def get_grad_hook(name):
def hook(m, grad_in, grad_out):
print((name, grad_out[0].data.abs().mean(), grad_in[0].data.abs().mean()))
print((grad_out[0].size()))
print((grad_in[0].size()))
print((grad_out[0]))
print((grad_in[0]))
return hook
def softmax(scores):
es = np.exp(scores - scores.max(axis=-1)[..., None])
return es / es.sum(axis=-1)[..., None]
def log_add(log_a, log_b):
return log_a + np.log(1 + np.exp(log_b - log_a))
def class_accuracy(prediction, label):
cf = confusion_matrix(prediction, label)
cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)
cls_acc = cls_hit / cls_cnt.astype(float)
mean_cls_acc = cls_acc.mean()
return cls_acc, mean_cls_acc
