from HelperClass.NeuralNet_1_0 import * file_name = "../../data/ch04.npz" if __name__ == '__main__': sdr = DataReader_1_0(file_name) sdr.ReadData() hp = HyperParameters_1_0(1, 1, eta=0.5, max_epoch=1000, batch_size=-1, eps = 0.02) net = NeuralNet_1_0(hp) net.train(sdr)，怎么调整什么代码的学习率，批大小等参数

对于这个问题，你可以通过调整 HyperParameters_1_0 类的参数来改变学习率、批大小等参数。具体来说，可以通过修改 eta 和 batch_size 参数来改变学习率和批大小。例如，将 eta 设置为 0.1，将 batch_size 设置为 32，可以尝试加快训练速度。当然，这些参数的最佳值取决于具体的数据集和模型，需要通过实验来确定。

相关问题

怎么用python将下面代码中的dw,db改为私有属性import numpy as npimport matplotlib.pyplot as pltfrom HelperClass.DataReader_1_0 import *file_name = "../../data/ch04.npz"class NeuralNet_0_1(object): def __init__(self, eta): self.eta = eta self.w = 0 self.b = 0 def __forward(self, x): z = x * self.w + self.b return z def __backward(self, x,y,z): dz = z - y db = dz dw = x * dz return dw, db def __update(self, dw, db): self.w = self.w - self.eta * dw self.b = self.b - self.eta * db def train(self, dataReader): for i in range(dataReader.num_train): # get x and y value for one sample x,y = dataReader.GetSingleTrainSample(i) # get z from x,y z = self.__forward(x) # calculate gradient of w and b dw, db = self.__backward(x, y, z) # update w,b self.__update(dw, db) # end for def inference(self, x): return self.__forward(x)# end classdef ShowResult(net, dataReader): X,Y = dataReader.GetWholeTrainSamples() # draw sample data plt.plot(X, Y, "b.") # draw predication data PX = np.linspace(0,1,10) PZ = net.inference(PX) plt.plot(PX, PZ, "r") plt.title("Air Conditioner Power") plt.xlabel("Number of Servers(K)") plt.ylabel("Power of Air Conditioner(KW)") plt.show()if __name__ == '__main__': # read data sdr = DataReader_1_0(file_name) sdr.ReadData() # create net eta = 0.1 net = NeuralNet_0_1(eta) net.train(sdr) # result print("w=%f,b=%f" %(net.w, net.b)) # predication result = net.inference(1.346) print("result=", result) ShowResult(net, sdr)

将dw和db改为私有属性可以在它们的变量名前加上两个下划线"__"，即将dw改为__dw，将db改为__db。修改后的代码如下：

import numpy as np
import matplotlib.pyplot as plt
from HelperClass.DataReader_1_0 import *

file_name = "../../data/ch04.npz"

class NeuralNet_0_1(object):
def __init__(self, eta):
self.eta = eta
self.w = 0
self.b = 0
self.__dw = 0
self.__db = 0

def __forward(self, x):
z = x * self.w + self.b
return z

def __backward(self, x, y, z):
dz = z - y
self.__db = dz
self.__dw = x * dz
return self.__dw, self.__db

def __update(self):
self.w = self.w - self.eta * self.__dw
self.b = self.b - self.eta * self.__db

def train(self, dataReader):
for i in range(dataReader.num_train):
x, y = dataReader.GetBatchTrainSamples(1)
z = self.__forward(x)
dw, db = self.__backward(x, y, z)
self.__update()