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文章来源:https://www.toymoban.com/news/detail-764384.html
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)
# relu
y = F.relu(x_)
y.sum().backward()
activate_list.append([y, x_.grad, 'relu'])
# sigmoid
x_ = 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'])
# tanh
x_ = 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'])
# swish
x_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)
beta = 1
y = x_ * F.sigmoid(x_ * beta)
y.sum().backward()
activate_list.append([y, x_.grad, 'swish'])
# silu
x_ = torch.arange(-8.0, 8.0, 0.01, requires_grad=True)
beta = 1
threshold = 20
y = F.silu(x_)
y.sum().backward()
activate_list.append([y, x_.grad, 'silu'])
# mish
x_ = 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'])
# gelu
x_ = 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'])
# celu
x_ = 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'])
# elu
x_ = 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文章来源地址https://www.toymoban.com/news/detail-764384.html
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