python代码

python\main_script.py

from multiprocessing import Process
import subprocessdef call_script(args):# 创建一个新的进程来运行script_to_call.pyprocess = Process(target=run_script, args=(args[0], args[1]))process.start()process2 = Process(target=run_script, args=(args[0], args[1]))process2.start()process.join()  # 等待进程完成process2.join()  # 等待进程完成def run_script(arg1, arg2):subprocess.run(["python", "script_to_call.py", str(arg1), str(arg2)], check=True)if __name__ == "__main__":args = [4,6,7]call_script(args)

python\script_to_call.py

import time
import sysdef main(arg1, arg2):print(f"Arguments received: {arg1}, {arg2}")# 模拟一些工作time.sleep(2)print("Work done in script_to_call.py")return 0if __name__ == "__main__":# 从命令行参数获取输入（当直接运行此脚本时）# 但在这个例子中，我们不会直接运行它，而是通过多进程调用它# 因此，这里的代码主要是为了演示如何接收参数arg1 = int(sys.argv[1])arg2 = int(sys.argv[2])result = main(arg1, arg2)print(f"Result from script_to_call.py: {result}")

神经网络--回归
import numpy as np
import matplotlib.pyplot as pltinput_data=np.arange(0,np.pi*2,0.1)
correct_data=np.sin(input_data)input_data=(input_data-np.pi)/np.pi
n_data=len(correct_data)epoch=2001
eta=0.1 #学习率
interval=100n_in=1
n_mid=3
n_out=1wb_width=0.01 #权重和偏置的初始宽度
class MiddleLayer:def __init__(self,n_upper,n):self.w=wb_width*np.random.randn(n_upper,n)self.b=wb_width*np.random.randn(n)def forward(self,x):self.x=xu=np.dot(x,self.w)+self.bself.y=1/(1+np.exp(-u)) #sigmoid函数def backward(self,grad_y):delta=grad_y*(1-self.y)*self.y #sigmoid函数的导数/self.grad_w=np.dot(self.x.T,delta)self.grad_b=np.sum(delta,axis=0)self.grad_x=np.dot(delta,self.w.T)def update(self,eta): #更新权重和偏置self.w-=eta*self.grad_wself.b-=eta*self.grad_bclass OutputLayer:def __init__(self,n_upper,n):self.w=wb_width*np.random.randn(n_upper,n)self.b=wb_width*np.random.randn(n)def forward(self,x):self.x=xu=np.dot(x,self.w)+self.bself.y=udef backward(self,t):delta=self.y-tself.grad_w=np.dot(self.x.T,delta)self.grad_b=np.sum(delta,axis=0)self.grad_x=np.dot(delta,self.w.T)def update(self,eta):self.w-=eta*self.grad_wself.b-=eta*self.grad_b       middle_layer=MiddleLayer(n_in,n_mid)
output_layer=OutputLayer(n_mid,n_out)for i in range(epoch):#随机打乱索引值index_random=np.arange(n_data)np.random.shuffle(index_random)#用于结果的显示total_error=0plt_x=[]plt_y=[]for idx in index_random:x=input_data[idx:idx+1]t=correct_data[idx:idx+1]#前向传播middle_layer.forward(x.reshape(1,1))output_layer.forward(middle_layer.y)#反向传播output_layer.backward(t.reshape(1,1))middle_layer.backward(output_layer.grad_x)#更新权重和偏置middle_layer.update(eta)output_layer.update(eta)if i%interval==0:y=output_layer.y.reshape(-1) #将矩阵转换为向量total_error+=1.0/2.0*np.sum(np.square(y-t)) #平方和误差plt_x.append(x)plt_y.append(y)if i%interval==0:plt.plot(input_data,correct_data,linestyle="dashed")plt.scatter(plt_x,plt_y,marker="+")print(plt_y)plt.show()print("Epoch:"+str(i)+"/"+str(epoch),"Error:"+str(total_error/n_data))