3.5 多层感知机【斯坦福21秋季：实用机器学习中文版】代码实现

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• • • 题目
    • 完整代码

题目

3.5 多层感知机【斯坦福21秋季：实用机器学习中文版】代码实现

完整代码

'''
Description: 多层感知机的实现
Autor: 365JHWZGo
Date: 2022-03-21 16:02:55
LastEditors: 365JHWZGo
LastEditTime: 2022-03-21 16:59:52
'''

'''
main idea
x->linear->relu->linear->sigmoid->linear->y
[m,n]->[m,p]->[m,q]->[m,r]->[m,o]
'''

import math
import torch

torch.manual_seed(1)
m = 3
n = 4
p = 3
o = 2
r = 5

# func relu
def relu(X):
    return torch.maximum(X, torch.zeros(X.shape))

# func sigmoid
def sigmoid(X):
    return torch.tensor([1/(1+torch.exp(-y)) for x in X for y in x]).reshape(X.shape)

# func linear
def linear(X, w, b):
    return torch.matmul(X,w)+b

if __name__ == "__main__":
    # X.shape [m,n]
    X = torch.randn((m, n))
	
	# linear1
    w1 = torch.randn((n,p))
    b1 = torch.zeros((m,1))

    # linear2
    w2 = torch.randn((p,r))
    b2 = torch.zeros((m,1))
	
	# linear3
    w3 = torch.randn((r,o))
    b3 = torch.zeros((m,1))
    
    # mlp实现过程
    l1_x = linear(X,w1,b1)

    relu_x = relu(l1_x)
    print('func relu\n',relu_x)

    r_x = torch.relu(l1_x)
    print('torch.relu\n',r_x)

    l2_x = linear(relu_x,w2,b2)

    sigmoid_x = sigmoid(l2_x)
    print('func sigmoid\n',sigmoid_x)

    s_x = torch.sigmoid(l2_x)
    print('torch.sigmoid\n',s_x)

    out = linear(sigmoid_x,w3,b3)
    
    print(out)