使用torch中的激活函数，绘制多个激活函数多一个图中对比展示

引入依赖

import torch
from torch.nn import functional as F
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']

定义单个曲线图的绘制函数

def draw_single_plot(x, y, x_name='x', y_name='y', img_path=''):plt.figure(figsize=(5, 2.5))plt.plot(x, y)plt.xlabel(x_name)plt.ylabel(y_name)if img_path:plt.savefig(img_path)plt.grid()plt.show()

定义多个曲线图的绘制函数

def draw_multi_plot(value_list, x_name, y_name, title, img_path):""":param value_list: [x, y, name]:return:"""fig, ax = plt.subplots()  # 创建图实例for x, y, name in value_list:ax.plot(x, y, label=name)ax.set_xlabel(x_name)ax.set_ylabel(y_name)ax.set_title(title)ax.legend()plt.grid()# 是否保存图片if img_path:plt.savefig(img_path)print("成功保存图片")plt.show()print("success")

定义激活函数生成数据

def get_multi_activate_value():activate_list = []x_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)# reluy = F.relu(x_)y.sum().backward()activate_list.append([y, x_.grad, 'relu'])# sigmoidx_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)y = F.sigmoid(x_)y.sum().backward()activate_list.append([y, x_.grad, 'sigmoid'])# tanhx_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)y = F.tanh(x_)y.sum().backward()activate_list.append([y, x_.grad, 'tanh'])# swishx_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)beta = 1y = x_ * F.sigmoid(x_ * beta)y.sum().backward()activate_list.append([y, x_.grad, 'swish'])# silux_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)beta = 1threshold = 20y = F.silu(x_)y.sum().backward()activate_list.append([y, x_.grad, 'silu'])# mishx_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)y = x_ * F.tanh(F.softplus(x_, beta, threshold))y.sum().backward()activate_list.append([y, x_.grad, 'mish'])# gelux_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)y = F.gelu(x_)y.sum().backward()activate_list.append([y, x_.grad, 'gelu'])# celux_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)y = F.celu(x_)y.sum().backward()activate_list.append([y, x_.grad, 'celu'])# elux_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)y = F.elu(x_)y.sum().backward()activate_list.append([y, x_.grad, 'elu'])return x_, activate_list

多激活函数图绘图

	x_, activate_list = get_multi_activate_value()act_value_list = [[x_.data.numpy(), obj[0].data.numpy(), obj[2]] for obj in activate_list]draw_multi_plot(act_value_list, x_name='x', y_name="激活值", title="激活函数对比", img_path='./imgs/act_multi.png')

多激活函数梯度图绘图

	x_, activate_list = get_multi_activate_value()grad_value_list = [[x_.data.numpy(), obj[1].data.numpy(), obj[2]] for obj in activate_list]draw_multi_plot(grad_value_list, x_name='x', y_name="梯度值", title="激活函数梯度对比", img_path='./imgs/grad_multi.png')

单个激活函数曲线绘图

	x_, activate_list = get_multi_activate_value()# 单个激活函数绘图draw_single_plot(x_.data.numpy(), activate_list[0][0].data.numpy(), x_name='x', y_name='y', img_path='')

参考：
torch常见激活函数
常用的激活函数合集