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Pytorch deep learning practice (3)
2022-04-23 22:02:00 【Know what you know and slowly understand what you don't know】
Random gradient descent method
import numpy as np
import matplotlib.pyplot as plt
x_data=[1.0,2.0,3.0]
y_data=[2.0,4.0,6.0]
w=1.0
def forward(x):
return x*w
def cost(xs,ys):
cost=0
for x,y in zip(xs,ys):
y_pred=forward(x)
cost+=(y_pred-y)**2
return cost/len(xs)
def gradient(xs,ys):
grad=0
for x,y in zip(xs,ys):
grad+=2*x*(x*w-y)
return grad/len(xs)
print('prdeict (before training)',4,forward(4))
for epoch in range(100):
cost_val=cost(x_data,y_data)
grad_val=gradient(x_data,y_data)
w-=0.01*grad_val
print('epoch:',epoch,'w=',w,'loss=',cost_val)
print('predict (after training',4,forward(4))
#plt.plot(cost)
plt.plot(x_data,y_data)
#epoch.append(epoch)
#cost.append(cost(x_data,y_data))
#plt.plot(epoch_list,cost_list)
plt.ylabel('cost')
plt.xlabel('epoch')
plt.show()
There is a problem with the last diagram
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