SimpleZihao的博客

概述随机梯度下降，和批量梯度下降原理类似，区别在于求梯度时没有用所有样本的数据，而是仅仅选取一个样本j来求梯度，更新公式为：随机梯度下降法由于每次仅仅采用一个样本来迭代，训练速度很快# -*- coding:utf-8 _*-# @author: Fu zihao# @file: pre01.pyimport numpy as npimport random# 学习率ALPHA = 0.001# 允许的最大误差ERROR = 0.01X1 = np.array(

代码示例# -*- coding:utf-8 _*-# @author: Fu zihao# @file: actorCriticMethod.py# @time: 2021/10/21 15:47import gym, osfrom itertools import countimport paddleimport paddle.nn as nnimport paddle.optimizer as optimimport paddle.nn.functional as Ffro.

使用paddlepaddle训练手写体MNIST数据集import paddlefrom paddle.nn import Linearimport paddle.nn.functional as Fimport osimport numpy as npimport matplotlib.pyplot as plt# MNIST类class MNIST(paddle.nn.Layer): def __init__(self): super(MNIST, self).

使用飞桨训练cifar10数据集import paddleimport paddle.nn.functional as Ffrom paddle.vision.transforms import ToTensorimport numpy as npimport matplotlib.pyplot as plttransform = ToTensor()# 训练集cifar10_train = paddle.vision.datasets.Cifar10(mode='train',

# -*- coding:utf-8 _*-import numpy as npData = np.array([[2.5, 2.4], [0.5, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1, 3.0], [2.3, 2.7], .

# 晴天walkPsw = 0.6Prw = 0.1# 晴天shopPss = 0.3Prs = 0.4# 晴天clearPsc = 0.1Prc = 0.5# 晴天转晴天Ps_s = 0.6# 晴天转阴天Ps_r = 0.4# 阴天转晴天Pr_s = 0.3# 阴天转阴天Pr_r = 0.7# 第一天下雨def Rday1(): prob = 0.6 * Prw return prob# 第一天晴天def Sday1(): prob

import numpy as nplearning_rate = 0.001x1 = x2 = x3 = 1y1 = y2 = 1w14 = w15 = w16 = w24 = w25 = w26 = w34 = w35 = w36 = w17 = w27 = w37 = 1w48 = w49 = w58 = w59 = w68 = w69 = w78 = w79 = 1X = np.array([x1, x2, x3]).reshape(3, 1)W1 = np.array([[w1.

代码实现from matplotlib import pyplot as pltdef y(x, y): y = 1.1 * y - (0.2 * x) / y return float("%.2f" % y)def collection(x0, y0, h): x1 = x0 + h yp = y0 + h * y(x0, y0) yc = y0 + h * y(x1, yp) y1 = (1 / 2) * (yc + yp) ret.

#mermaid-svg-9kdIveOAARBgUNuK .label{font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family);fill:#333;color:#333}#mermaid-svg-9kdIveOAARBgUNuK .label text{fill:#333}#mermaid-svg-9kdIveOAARBgUNuK .node rect,#mermaid-svg-9kdIveOAA.

1. 欧拉公式例题之python求解问题描述：代码实现 # -*- coding:utf-8 _*- # @author: Fu zihao # @file: cal01.py # @time: 2021/9/14 18:48 from matplotlib import pyplot as plt # 计算结果 def iteration(x, y, times): print("x1:", x, "y1:", y) x_a = [] y_

# -*- coding:utf-8 _*-import randomimport numpy as npdef sigmod_Z(X, W_raw): # sigmod(Z) x_r = np.dot(X, W_raw.T) result = 1 / (1 + np.exp(-x_r)) return resultdef deta(sg, X, t): # Deta dt0 = (sg - t) * sg * (1 - sg) dt

import numpy as npimport randomALPHA = 0.01EPS = 10e-4X1 = np.array([[2104],[1600],[2400],[1416],[3000]])X2 = np.array([[3],[3],[3],[2],[4]])X = np.array([[1, 2104, 3],[1, 1600, 3],[1, 2400, 3],[1, 1416, 2],[1, 3000, 4]])Y = np.array([40