如何使用Python plt像MATLAB一样绘图

1、 Python plt绘图

使用Python的绘图，制作训练的迭代次数与准确率、损失函数值的图像：

• 使用说明
• 代码

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使用说明

Introduction　简介：

我们在用MATLAB绘图时感觉很轻巧，那么在Python中，怎么使用plot呢？下面是一个简单的例子，使用linspace定义自变量的取值范围，文档中其说明为：
np.linspace(start, stop, num, endpoint, retstep, dtype)，
我们只需要了解前面3个，小标开始的数字、结束的数字、数字数字个数。好，直接showcode

代码块

代码，例如：

import numpy as npimport matplotlib.pyplot as pltfrom pylab import *mpl.rcParams['font.sans-serif']=['SimHei']tra_accuracy=[0.12,0.23,0.31,0.34,0.43,0.51,0.55,0.66,0.68,0.74,0.8,0.9]test_acc = [0.4,0.5,0.6,0.7,0.8,0.9]a=np.linspace(0,100,2)print(a)#正确率绘图fig1=plt.figure('fig1')plt.plot(np.linspace(0, 11, len(tra_accuracy)),tra_accuracy,'b-',label='训练的正确率')plt.plot(np.linspace(0, 10, len(test_acc)),test_acc,'k-.',label='测试的正确率')plt.title('训练、测试的正确率')plt.xlabel('迭代次数')plt.ylabel('准确率')plt.legend(loc='lower right')>>> plt.show(fig1)

2、绘制柱状图

# _*_coding:utf-8_*___author__ = 'Alex_XT'import numpy as npimport matplotlib.pyplot as pltx=[0,1,2,3,4,5,6,7,8,9]y=[12,23,4,10,22,33,44,23,35,7]plt.bar(x,y,align='center',alpha=0.5)plt.xticks(x,x)plt.ylabel('count')plt.title('Distribution')plt.show()

3、画准确率图

# _*_coding:utf-8_*___author__ = 'Alex_XT'from pylab import *mpl.rcParams['font.sans-serif']=['SimHei']#导入中文import numpy as npimport matplotlib.pyplot as plty=[1.0,0.982,0.97,0.95,0.92,0.620]y2=[0.989,0.967,0.43,0.44,0.48,0.3]plt.plot(np.linspace(40,50,6),y,'r-*',label='Swish')plt.plot(np.linspace(40,50,6),y2,'b-d',label='ReLu')plt.legend(loc='lower left')plt.ylabel('准确率')plt.xlabel('网络层数')plt.title('MNIST数据集中不同网络层数测试')plt.show()

4、等比数列的图，但xlabel却是等间距的

# _*_coding:utf-8_*___author__ = 'Alex_XT'from pylab import *mpl.rcParams['font.sans-serif']=['SimHei']#导入中文import numpy as npimport matplotlib.pyplot as pltx=np.linspace(1,5,5)xt=np.logspace(0,4,5,base=2)*128#等比print(xt)y=[92.4,92.22,92.2091,91.8,91.45]y2=[92.0,91.90,92.01,91.402,91.302]plt.plot(x,y,'r-*',label='Swish')plt.plot(x,y2,'b-d',label='ReLu')plt.legend(loc='upper right')plt.xticks(x,xt)plt.ylabel('准确率')plt.xlabel('Batch Size')plt.title('MNIST数据集中不同BatchSize大小测试')plt.show()

5、画Logistic的Sigmoid函数图

# _*_coding:utf-8_*___author__ = 'Alex_XT'# Python importsimport numpy as np # Matrix and vector computation packageimport matplotlib.pyplot as plt  # Plotting library# Define the logistic functiondef logistic(z):    return 1 / (1 + np.exp(-z))# Plot the logistic functionz = np.linspace(-6,6,100)plt.plot(z, logistic(z), 'b-')plt.xlabel('$z$', fontsize=15)plt.ylabel('$\sigma(z)$', fontsize=15)plt.title('logistic function')plt.grid()plt.show()

求导函数的图形：

# _*_coding:utf-8_*___author__ = 'Alex_XT'# Python importsimport numpy as np # Matrix and vector computation packageimport matplotlib.pyplot as plt  # Plotting library# Define the logistic functiondef logistic(z):    return 1 / (1 + np.exp(-z))# Define the logistic derivative functiondef logistic_derivative(z):    return logistic(z) * (1 - logistic(z))# Plot the derivative of the logistic functionz = np.linspace(-6,6,100)plt.plot(z, logistic_derivative(z), 'r-')plt.xlabel('$z$', fontsize=15)plt.ylabel('$\\frac{\\partial \\sigma(z)}{\\partial z}$', fontsize=15)plt.title('derivative of the logistic function')plt.grid()plt.show()

6、画Softmax三维图

# _*_coding:utf-8_*___author__ = 'Alex_XT'import numpy as np # Matrix and vector computation packageimport matplotlib.pyplot as plt  # Plotting libraryfrom matplotlib.colors import colorConverter, ListedColormap # some plotting functionsfrom mpl_toolkits.mplot3d import Axes3D  # 3D plotsfrom matplotlib import cm # Colormaps# Allow matplotlib to plot inside this notebook# Define the softmax functiondef softmax(z):    return np.exp(z) / np.sum(np.exp(z))# Plot the softmax output for 2 dimensions for both classes# Plot the output in function of the weights# Define a vector of weights for which we want to plot the ooutputnb_of_zs = 200zs = np.linspace(-10, 10, num=nb_of_zs) # inputzs_1, zs_2 = np.meshgrid(zs, zs) # generate gridy = np.zeros((nb_of_zs, nb_of_zs, 2)) # initialize output# Fill the output matrix for each combination of input z'sfor i in range(nb_of_zs):    for j in range(nb_of_zs):        y[i,j,:] = softmax(np.asarray([zs_1[i,j], zs_2[i,j]]))# Plot the cost function surfaces for both classesfig = plt.figure()# Plot the cost function surface for t=1ax = fig.gca(projection='3d')surf = ax.plot_surface(zs_1, zs_2, y[:,:,0], linewidth=0, cmap=cm.coolwarm)ax.view_init(elev=30, azim=70)cbar = fig.colorbar(surf)ax.set_xlabel('$z_1$', fontsize=15)ax.set_ylabel('$z_2$', fontsize=15)ax.set_zlabel('$y_1$', fontsize=15)ax.set_title ('$P(t=1|\mathbf{z})$')cbar.ax.set_ylabel('$P(t=1|\mathbf{z})$', fontsize=15)plt.grid()plt.show()

7、画ELU激活函数

# _*_coding:utf-8_*___author__ = 'Alex_XT'# Python importsimport numpy as np  # Matrix and vector computation packageimport matplotlib.pyplot as plt  # Plotting library# Define the ELU functiondef ELU(z):    new_z = []    for i in z:        if i > 0:            new_z.append(i)        else:            new_z.append(np.exp(i) - 1)    return new_z# Plot the ELU functionz = np.linspace(-10, 15, 100)plt.plot(z, ELU(z), 'r-')plt.xlabel('$x$', fontsize=15)plt.ylabel('$f(x)$', fontsize=15)plt.title('ELU')plt.grid()plt.show()