代码如下: 1importnumpy as np2importmatplotlib.pyplot as plt3fromnumpyimportarange4frommatplotlib.font_managerimportFontProperties5plt.ion()678#函数 f(x)=x^29deff(x):returnx ** 2101112#一阶导数:dy/dx=2*x13deffd(x):return2 *x141516defGD(x_start, df, epochs, lr):17xs = np.zeros(epoch...
import matplotlib.pyplot as plt import numpy as np # 初始算法开始之前的坐标 # cur_x 和 cur_y cur_x = 6 cur_y = (cur_x-1)**2 + 1 # 设置学习率 eta 为 0.05 eta = 0.05 # 变量 iter 用于存储迭代次数 # 这次我们迭代 1000 次 # 所以给它赋值 1000 iter = 1000 # 变量 cur_df 用...
# initialize parametersw_init =0b_init =0# some gradient descent settingsiterations =10000tmp_alpha =1.0e-2# run gradient descentw_final, b_final, J_hist, p_hist = gradient_descent(x_train ,y_train, w_init, b_init, tmp_alpha, iterations, compute_cost, compute_gradient)print(f"(w,...
随机梯度下降(Stochastic Gradient Descent,SGD)作为一种优化算法,在机器学习和优化领域中显得尤为重要,并被广泛运用于模型训练和参数优化的过程中。 梯度下降是一种优化算法,通过迭代沿着由梯度定义的最陡下降方向,以最小化函数。类似于图中的场景,可以将其比喻为站在山巅,希望找到通往山脚最低点的最佳路径。梯度下降...
代码实现如下:(下载链接:https:///Airuio/Implementing-Stochastic-gradient-descent-by-using-Python-) import numpy as np from numpy.random import seed class AdalineSGD(object): def __init__(self,eta=0.01,n_iter=10,shuffle=True,random_state=None): ...
1import numpy as np 2 3def gradient_descent( 4 gradient, x, y, start, learn_rate=0.1, n_iter=50, tolerance=1e-06, 5 dtype="float64" 6): 7 # Checking if the gradient is callable 8 if not callable(gradient): 9 raise TypeError("'gradient' must be callable") 10 11 # Setting ...
Gradient descent 1: Introduction To The Data We have a datasetpga.csvcontaining professional golfers' driving statistics in two columns,accuracyanddistance. Accuracy is measured as the percentage of fairways hit over many drives. Distances is measured as the average drive distance, in yards. Our ...
梯度下降 Gradient Descent 1.梯度 在微积分里面,对多元函数的参数求∂偏导数,把求得的各个参数的偏导数以向量的形式写出来,就是梯度。比如函数f(x,y), 分别对x,y求偏导数,求得的梯度向量就是(∂f/∂x, ∂f/∂y)T,简称grad f(x,y)或者▽f(x,y)。对于在点(x0,y0)的具体梯度向量就是(...
We have values on the X-axis and f(x) on the y-axis. Now let’s define how to use gradient descent to find the minimum. Use the below code for the same. We will first define the starting point, learning rate, and the parameter to stop it like iterations or if the value does...
Instead of using the Sum of Squared Errors (SSE), we will be using the Mean Squared Error (MSE). When using larger datasets, summing up all the weight steps can lead to very large updates that make the gradient descent diverge. To compensate for this, it would be necessary to use a ...