激活函数在神经网络中扮演着至关重要的角色，它们用于引入非线性特性，使神经网络能够学习和适应更复杂的数据模式。本篇列出几个常见的激活函数，简单形式比较常用的是 ReLU 函数和Leaky ReLU 函数。
1. Sigmoid 函数
PyTorch实现（类）：torch.nn.Sigmoid()
PyTorch实现（函数）：torch.nn.functional.sigmoid()
PyTorch文档：
- https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html
- https://pytorch.org/docs/stable/generated/torch.nn.functional.sigmoid.html
2. Tanh 函数
PyTorch实现（类）：torch.nn.Tanh()
PyTorch实现（函数）：torch.nn.functional.tanh()
PyTorch文档：
- https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html
- https://pytorch.org/docs/stable/generated/torch.nn.functional.tanh.html
3. ReLU 函数
全称：Rectified Linear Unit
PyTorch实现（类）：torch.nn.ReLU()
PyTorch实现（函数）：torch.nn.functional.relu()
PyTorch文档：
- https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html
- https://pytorch.org/docs/stable/generated/torch.nn.functional.relu.html
4. Leaky ReLU 函数
PyTorch实现（类）：torch.nn.LeakyReLU()
PyTorch实现（函数）：torch.nn.functional.leaky_relu()
PyTorch文档：
- https://pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html
- https://pytorch.org/docs/stable/generated/torch.nn.functional.leaky_relu.html
5. GELU 函数
PyTorch实现（类）：torch.nn.GELU()
PyTorch实现（函数）：torch.nn.functional.gelu()
PyTorch文档：
- https://pytorch.org/docs/stable/generated/torch.nn.GELU.html
- https://pytorch.org/docs/stable/generated/torch.nn.functional.gelu.html
6. Swish/SiLU 函数
PyTorch实现（类）：torch.nn.SiLU()
PyTorch实现（函数）：torch.nn.functional.silu()
PyTorch文档：
- https://pytorch.org/docs/stable/generated/torch.nn.SiLU.html
- https://pytorch.org/docs/stable/generated/torch.nn.functional.silu.html
还有其他的一些激活函数，这里暂时不列出。每种激活函数都有其适用的场景和局限性，通常需要根据具体的问题和数据特点来选择合适的激活函数。
7. 画图代码
"""
This code is supported by the website: https://www.guanjihuan.com
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/39029
"""
import torch
import numpy as np
import matplotlib.pyplot as plt
x_array = np.linspace(-6, 6, 100)
x_array_torch_tensor = torch.from_numpy(x_array)
y_array_torch_tensor = torch.nn.functional.sigmoid(x_array_torch_tensor)
plt.plot(x_array_torch_tensor, y_array_torch_tensor)
plt.title('sigmoid')
plt.show()
y_array_torch_tensor = torch.nn.functional.tanh(x_array_torch_tensor)
plt.plot(x_array_torch_tensor, y_array_torch_tensor)
plt.title('tanh')
plt.show()
y_array_torch_tensor = torch.nn.functional.relu(x_array_torch_tensor)
plt.plot(x_array_torch_tensor, y_array_torch_tensor)
plt.title('relu')
plt.show()
y_array_torch_tensor = torch.nn.functional.leaky_relu(x_array_torch_tensor)
plt.plot(x_array_torch_tensor, y_array_torch_tensor)
plt.title('leaky_relu')
plt.show()
y_array_torch_tensor = torch.nn.functional.gelu(x_array_torch_tensor)
plt.plot(x_array_torch_tensor, y_array_torch_tensor)
plt.title('gelu')
plt.show()
y_array_torch_tensor = torch.nn.functional.silu(x_array_torch_tensor)
plt.plot(x_array_torch_tensor, y_array_torch_tensor)
plt.title('silu')
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