深度学习（激活函数）

这里实现了一下常见的激活函数。

其中prelu参数是一个可学习参数，这里设成前向传播类。

代码如下：

import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn

def sigmoid(x):
    return 1.0/(1.0 + np.exp(-x))

def softmax(x):
    return np.exp(x)/np.sum(np.exp(x))

def tanh(x):
    return (np.exp(x) - np.exp(-x))/(np.exp(x) + np.exp(-x))

def relu(x):
    return np.where(x > 0, x, 0)

def leaky_relu(x,a=0.1):
    return np.where(x > 0, x, a * x)

def rrelu(x,lower=0.1,upper=0.3):
    return np.where(x > 0, x, np.random.rand(len(x))*(upper-lower)+lower * x)

def elu(x,a=1.0):
    return np.where(x > 0, x, a * (np.exp(x) - 1))

def selu(x,a=1.67,l=1.05):
    return np.where(x > 0, l * x, l * a * (np.exp(x) - 1))

def celu(x,a=1.0):
    return np.where(x > 0, x,  a * (np.exp(x/a) - 1))

def gelu(x):
    return 0.5*x*(1+tanh(np.sqrt(2/np.pi)*(x+0.044715*x**3)))

def relu6(x):
    return np.minimum(np.maximum(0, x), 6)

def swish(x,b=1.0):
    return x*sigmoid(b*x)

def hardswish(x):
    return x * relu6(x+3) / 6.0

def silu(x):
    return x*sigmoid(x)

def softplus(x):
    return np.log(1+np.exp(x))

def mish(x):
    return x*tanh(softplus(x))

class prelu(nn.Module):
    def __init__(self):
        super(prelu, self).__init__()
        self.a = nn.Parameter(torch.ones(1)*0.1)
    def forward(self, x):
        return torch.where(x > 0, x, self.a * x)

if __name__ == '__main__':
    
    x = np.linspace(-10,10,1000)
    functions = [sigmoid,softmax,tanh,relu,
                 leaky_relu,rrelu,elu,selu,celu,
                 gelu,relu6,swish,hardswish,
                 silu,softplus,mish]
   
    for i,func in enumerate(functions):
        plt.subplot(4,4,i+1)
        plt.plot(x,func(x)) 
    plt.show()

    model = prelu()
    tensor_x = torch.tensor(x)
    tensor_y = model(tensor_x)
    plt.plot(tensor_x.numpy(),tensor_y.detach().numpy())
    plt.show()

结果如下：