Numpy实现Perceptron(感知机)

举报
AI浩 发表于 2021/12/23 00:40:52 2021/12/23
【摘要】 from __future__ import print_function, division import math import numpy as np # Import helper functi...
from __future__ import print_function, division
import math
import numpy as np

# Import helper functions
from mlfromscratch.utils import train_test_split, to_categorical, normalize, accuracy_score
from mlfromscratch.deep_learning.activation_functions import Sigmoid, ReLU, SoftPlus, LeakyReLU, TanH, ELU
from mlfromscratch.deep_learning.loss_functions import CrossEntropy, SquareLoss
from mlfromscratch.utils import Plot
from mlfromscratch.utils.misc import bar_widgets
import progressbar

class Perceptron():
    """The Perceptron. One layer neural network classifier.

    Parameters:
    -----------
    n_iterations: float
        The number of training iterations the algorithm will tune the weights for.
    activation_function: class
        The activation that shall be used for each neuron.
        Possible choices: Sigmoid, ExpLU, ReLU, LeakyReLU, SoftPlus, TanH
    loss: class
        The loss function used to assess the model's performance.
        Possible choices: SquareLoss, CrossEntropy
    learning_rate: float
        The step length that will be used when updating the weights.
    """
    def __init__(self, n_iterations=20000, activation_function=Sigmoid, loss=SquareLoss, learning_rate=0.01):
        self.n_iterations = n_iterations
        self.learning_rate = learning_rate
        self.loss = loss()
        self.activation_func = activation_function()
        self.progressbar = progressbar.ProgressBar(widgets=bar_widgets)

    def fit(self, X, y):
        n_samples, n_features = np.shape(X)
        _, n_outputs = np.shape(y)

        # Initialize weights between [-1/sqrt(N), 1/sqrt(N)]
        limit = 1 / math.sqrt(n_features)
        self.W = np.random.uniform(-limit, limit, (n_features, n_outputs))
        self.w0 = np.zeros((1, n_outputs))

        for i in self.progressbar(range(self.n_iterations)):
            # Calculate outputs
            linear_output = X.dot(self.W) + self.w0
            y_pred = self.activation_func(linear_output)
            # Calculate the loss gradient w.r.t the input of the activation function
            error_gradient = self.loss.gradient(y, y_pred) * self.activation_func.gradient(linear_output)
            # Calculate the gradient of the loss with respect to each weight
            grad_wrt_w = X.T.dot(error_gradient)
            grad_wrt_w0 = np.sum(error_gradient, axis=0, keepdims=True)
            # Update weights
            self.W  -= self.learning_rate * grad_wrt_w
            self.w0 -= self.learning_rate  * grad_wrt_w0

    # Use the trained model to predict labels of X
    def predict(self, X):
        y_pred = self.activation_func(X.dot(self.W) + self.w0)
        return y_pred


  
 
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50
  • 51
  • 52
  • 53
  • 54
  • 55
  • 56
  • 57
  • 58
  • 59
  • 60
  • 61
  • 62

文章来源: wanghao.blog.csdn.net,作者:AI浩,版权归原作者所有,如需转载,请联系作者。

原文链接:wanghao.blog.csdn.net/article/details/121558456

【版权声明】本文为华为云社区用户转载文章,如果您发现本社区中有涉嫌抄袭的内容,欢迎发送邮件进行举报,并提供相关证据,一经查实,本社区将立刻删除涉嫌侵权内容,举报邮箱: cloudbbs@huaweicloud.com
  • 点赞
  • 收藏
  • 关注作者

评论(0

0/1000
抱歉,系统识别当前为高风险访问,暂不支持该操作

全部回复

上滑加载中

设置昵称

在此一键设置昵称,即可参与社区互动!

*长度不超过10个汉字或20个英文字符,设置后3个月内不可修改。

*长度不超过10个汉字或20个英文字符,设置后3个月内不可修改。