Question

我正试图从头开始实现一个2层神经网络。但是有些不对劲。经过一些迭代后，我的损失变为nan。

'''
We are implementing a two layer neural network.
'''
import numpy as np

x,y = np.random.rand(64,1000),np.random.randn(64,10)
w1,w2 = np.random.rand(1000,100),np.random.rand(100,10)
learning_rate = 1e-4
x -= np.mean(x,axis=0) #Normalizing the Training Data Set

for t in range(2000):
  h = np.maximum(0,x.dot(w1))    # Applying Relu Non linearity
  ypred = h.dot(w2) #Output of Hidden layer

  loss = np.square(ypred - y).sum()
  print('Step',t,'\tLoss:- ',loss)

  #Gradient Descent

  grad_ypred = 2.0 * (ypred - y)
  gradw2 = (h.transpose()).dot(grad_ypred)
  grad_h = grad_ypred.dot(w2.transpose())
  gradw1 = (x.transpose()).dot(grad_h*h*(1-h))

  w1 -= learning_rate*gradw1
  w2 -= learning_rate*gradw2

我还使用Softmax分类器和多类SVM损失实现了线性回归。出现同样的问题。请告诉我如何解决这个问题。

输出：

D:\Study Material\Python 3 Tutorial\PythonScripts\Machine Learning>python TwoLayerNeuralNet.py
Step 0  Loss:-  19436393.79233052
Step 1  Loss:-  236820315509427.38
Step 2  Loss:-  1.3887002186558748e+47
Step 3  Loss:-  1.868219503527502e+189
Step 4  Loss:-  inf
TwoLayerNeuralNet.py:23: RuntimeWarning: invalid value encountered in multiply
  gradw1 = (x.transpose()).dot(grad_h*h*(1-h))
TwoLayerNeuralNet.py:12: RuntimeWarning: invalid value encountered in maximum
  h = np.maximum(0,x.dot(w1))    # Applying Relu Non linearity
Step 5  Loss:-  nan
Step 6  Loss:-  nan
Step 7  Loss:-  nan
Step 8  Loss:-  nan
Step 9  Loss:-  nan
Step 10         Loss:-  nan
Step 11         Loss:-  nan
Step 12         Loss:-  nan
Step 13         Loss:-  nan
Step 14         Loss:-  nan
Step 15         Loss:-  nan
Step 16         Loss:-  nan
Step 17         Loss:-  nan
Step 18         Loss:-  nan
Step 19         Loss:-  nan
Step 20         Loss:-  nan

Answer 1

因为你的损失太高了试试这个

loss = np.square(ypred - y).mean()

如果仍然无法工作，请尝试将学习率降低到1e-8 并观察损失是上升还是下降，如果损失正在减少那么好，如果损失增加是一个坏的迹象，您可能需要考虑使用更好的数据集并检查权重更新。

Python - 双层神经网络中的错误

1 个答案: