HAJIC的博客

原创 2021-10-26

原创 神经网络及基础梯度下降

原创 x^2的梯层下降法

import matplotlib.pyplot as pltimport numpy as npdef fx(x):return x**2def gradient_descent():times = 10alpha = 0.1x =12x_axis = np.linspace(-12, 12)fig = plt.figure(1,figsize=(6,6))ax = fig.add_subplot(1,1,1)ax.set_xlabel(‘X’, fontsize=14)ax.se

原创 np.randm.randint() 生成离散均匀分布的整数值组成的矩阵

import numpy as npz = np.random.randint(2,9,(2,3))zarray([[2, 5, 6], [5, 7, 6]])m = np.random.randint(9,size = (2,3))marray([[8, 8, 0], [3, 3, 5]])

原创 产生随机浮点数

import numpy as npn = np.random.rand(3,4)narray([[0.29753678, 0.3563371 , 0.03046292, 0.08280118], [0.19488954, 0.53010199, 0.9870846 , 0.12542579], [0.79666487, 0.54572888, 0.01928849, 0.73992835]])

原创 np.full 用于形成元素全为某元素的矩阵

import numpy as npc = np.array([[1,2],[3,4]])carray([[1, 2], [3, 4]])c.astype(np.float32)array([[1., 2.], [3., 4.]], dtype=float32)

原创 np.hstack() 和 np.vstack() 用于堆叠矩阵

import numpy as npx = np.array([[3,4,5],[1,3,4]])y = np.array([[1,1,1],[2,2,2]])np.hstack((x,y))array([[3, 4, 5, 1, 1, 1], [1, 3, 4, 2, 2, 2]])np.vstack((x,y)) array([[3, 4, 5], [1, 3, 4], [1, 1, 1], [2, 2, 2]])

原创 111111取整

import numpy as npa = np.array([0.125,0.568,5.688])np.round(a)array([0., 1., 6.])np.round(a,decimals = 2) array([0.12, 0.57, 5.69])np.floor(a) array([0., 0., 5.])np.ceil(a) array([1., 1., 6.])

原创 np.newaxis 在特定位置增加一个维度

import numpy as npc = np.array([1,2,5,4])c[:,np.newaxis]array([[1], [2], [5], [4]])c[np.newaxis,:]array([[1, 2, 5, 4]])

原创 111广播机制

import numpy as npa = np.array([[1,2,3],[4,5,6]])a = np.array([[1,2,3,6],[4,5,6,6]])a1 = a.reshape((1,2,4))a1array([[[1, 2, 3, 6], [4, 5, 6, 6]]])b = np.array([[3,4,5,6],[1,2,3,4],[4,5,5,5]])barray([[3, 4, 5, 6], [1, 2, 3, 4],

原创 np.prod() 计算元素乘积

import numpy as npx = np.array([[1,2,3],[2,3,4]])np.prod(x)144np.prod(x,axis=1)array([ 6, 24])np.prod(x,axis=0)array([ 2, 6, 12])

原创 拉平操作 ravel()和faltten()及reshape(1,-1)的区别联系

import numpy as npx = np.array([[1,2,3],[4,5,6],[1,2,3]])x.flatten()array([1, 2, 3, 4, 5, 6, 1, 2, 3])x.ravel()array([1, 2, 3, 4, 5, 6, 1, 2, 3])x.ravel('F')array([1, 4, 1, 2, 5, 2, 3, 6, 3])x.flatten('F')array([1, 4, 1, 2, 5, 2, 3, 6, 3])

原创 星号的作用

import numpy as npy1 = np.linspace(-10.0,10.0)y1array([-10. , -9.59183673, -9.18367347, -8.7755102 , -8.36734694, -7.95918367, -7.55102041, -7.14285714, -6.73469388, -6.32653061, -5.91836735, -5.51020408, -5.1020

原创 np.argmax(a, axis=None, out=None)

import numpy as npa = np.array([[1,1,1],[2,2,2],[0,3,6]])aarray([[1, 1, 1], [2, 2, 2], [0, 3, 6]])b1 = np.argmax(a)b18b2 = np.argmax(a, axis=0)b2array([1, 2, 2], dtype=int64)b3 = np.argmax(a, axis=1)b3array([0, 0, 2], dtype

原创 np.random.choice(a, size, replace, p)

import numpy as npa1 = np.random.choice(7,5)a1array([6, 2, 3, 5, 2])a2 = np.random.choice([0,1,2,3,4,5,6],5)a2array([0, 6, 0, 6, 5])a3 = np.random.choice(np.array([0,1,2,3,4,5,6]),5)a3array([5, 5, 3, 3, 6])a4 = np.random.choice([0,1,2,3,

原创 append

import numpy as npmatrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]m1 = np.append(matrix,[[1,1,1,1]],axis=0)print('>>>>m1>>>>\n',m1)m2 = np.append(matrix,[[1],[1],[1]],axis=1)print('>>>>m2>>>>\n',m2)m3

原创 insert

import numpy as npmatrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]q1 = np.insert(matrix, 1, [1,1,1,1], 0)print('>>>>q1>>>>\n',q1)q2 = np.insert(matrix, 0, [1,1,1], 1)print('>>>>q2>>>>\n',q2)q3 = np.inser

原创 delete

import numpy as npmatrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]p1 = np.delete(matrix, 1, 0)print('>>>>p1>>>>\n',p1)p2 = np.delete(matrix, 1, 1)print('>>>>p2>>>>\n',p2)p3 = np.delete(matrix, 1)print('

原创 numpy基本加减和取行操作

import numpy as npa = np.array([1,1,1,1])b = np.array([[1],[1],[1],[1]])a+barray([[2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2]])c = np.array([[1,1,1,1]])c+barray([[2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2,