深度学习激活函数图像绘制（基于Python编程语言实现）

import math import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl mpl.rcParams['axes.unicode_minus'] = False def sigmoid(x): result = 1 / (1 + math.e ** (-x)) return result def tanh(x): # result = np.exp(x)-np.exp(-x)/np.exp(x)+np.exp(-x) result = (math.e ** (x) - math.e ** (-x)) / (math.e ** (x) + math.e ** (-x)) return result def relu(x): result = np.maximum(0, x) return result def elu(x, alpha=1): a = x[x > 0] b = alpha * (math.e ** (x[x < 0]) - 1) result = np.concatenate((b, a), axis=0) return result def leaky(x): a = x[x > 0] b = 0.1 * x[x < 0] result = np.concatenate((b, a), axis=0) return result yanse = 'teal' # =========================================== fig = plt.figure(figsize=(10, 8)) plt.rcParams['font.size'] = 11 plt.rc('font',family='Times New Roman') ax = fig.add_subplot(221) x = np.linspace(-10, 10) y = sigmoid(x) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.spines['bottom'].set_position(('data', 0)) ax.set_xticks([-10, -5, 0, 5, 10]) ax.yaxis.set_ticks_position('left') ax.spines['left'].set_position(('data', 0)) ax.set_yticks([-1, -0.5, 0.5, 1]) plt.plot(x, y, label="Sigmoid", linestyle='-', color=str(yanse)) plt.legend(loc='upper left',frameon=False,ncol=1) # ============================================ ax = fig.add_subplot(222) x = np.linspace(-10, 10) y = tanh(x) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.spines['bottom'].set_position(('data', 0)) ax.set_xticks([-10, -5, 0, 5, 10]) ax.yaxis.set_ticks_position('left') ax.spines['left'].set_position(('data', 0)) ax.set_yticks([-1, -0.5, 0.5, 1]) plt.plot(x, y, label="Tanh", linestyle='-', color=str(yanse)) plt.legend(loc='upper left',frameon=False,ncol=1) # ============================================ ax = fig.add_subplot(223) x = np.linspace(-10, 10) y = relu(x) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.spines['bottom'].set_position(('data', 0)) ax.set_xticks([-10, -5, 0, 5, 10]) ax.yaxis.set_ticks_position('left') ax.spines['left'].set_position(('data', 0)) ax.set_yticks([5, 10]) plt.plot(x, y, label="ReLU", linestyle='-', color=str(yanse)) plt.legend(loc='upper left',frameon=False,ncol=1) # ============================================ # ax = fig.add_subplot(234) # x = np.linspace(-10, 10) # y = elu(x) # # ax.spines['top'].set_color('none') # ax.spines['right'].set_color('none') # ax.xaxis.set_ticks_position('bottom') # ax.spines['bottom'].set_position(('data', 0)) # ax.set_xticks([-10, -5, 0, 5, 10]) # ax.yaxis.set_ticks_position('left') # ax.spines['left'].set_position(('data', 0)) # ax.set_yticks([5, 10]) # # plt.plot(x, y, label="ELU alpha=1", linestyle='-', color='red') # plt.legend() # ============================================ ax = fig.add_subplot(224) x = np.linspace(-10, 10) y = leaky(x) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.spines['bottom'].set_position(('data', 0)) ax.set_xticks([-10, -5, 0, 5, 10]) ax.yaxis.set_ticks_position('left') ax.spines['left'].set_position(('data', 0)) ax.set_yticks([5, 10]) plt.plot(x, y, label="Leaky Relu", linestyle='-', color=str(yanse)) plt.legend(loc='upper left', frameon=False, ncol=1) plt.savefig('../激活函数/' + str("jihuohanshu"), bbox_inches='tight', dpi=500) plt.show()