李宏毅机器学习课后作业（hw2）

李宏毅机器学习课后作业（hw2）

直接上代码

import numpy as np
np.random.seed(0)
X_train_fpath = "C:\\Users\\13554\\jupyter practice\\lihongyi\\hw2\\data\\X_train"
Y_train_fpath = "C:\\Users\\13554\\jupyter practice\\lihongyi\\hw2\\data\\Y_train"
X_test_fpath = "C:\\Users\\13554\\jupyter practice\\lihongyi\\hw2\\data\\X_test"
output_fpath = "C:\\Users\\13554\\jupyter practice\\lihongyi\\hw2\\data\\output_{}.csv"
with open(X_train_fpath) as f:next(f)X_train = np.array([line.strip('\n').split(',')[1:] for line in f], dtype = float)
with open(Y_train_fpath) as f:next(f)Y_train = np.array([line.strip('\n').split(',')[1] for line in f], dtype = float)
with open(X_test_fpath) as f:next(f)X_test = np.array([line.strip('\n').split(',')[1:] for line in f], dtype = float)
def _normalize(X, train = True, specified_column = None, X_mean = None, X_std = None):if specified_column == None:specified_column = np.arange(X.shape[1])if train:X_mean = np.mean(X[:, specified_column] ,0).reshape(1, -1) #对所有行按列取平均值X_std  = np.std(X[:, specified_column], 0).reshape(1, -1)X[:,specified_column] = (X[:, specified_column] - X_mean) / (X_std + 1e-8)return X, X_mean, X_stddef _train_dev_split(X, Y, dev_ratio = 0.25):# This function spilts data into training set and development set.train_size = int(len(X) * (1 - dev_ratio))return X[:train_size], Y[:train_size], X[train_size:], Y[train_size:]X_train, X_mean, X_std = _normalize(X_train, train = True)
X_test, _, _= _normalize(X_test, train = False, specified_column = None, X_mean = X_mean, X_std = X_std)
dev_ratio = 0.2
X_train, Y_train, X_dev, Y_dev = _train_dev_split(X_train, Y_train, dev_ratio = dev_ratio)
train_size = X_train.shape[0]
dev_size = X_dev.shape[0]
test_size = X_test.shape[0]
data_dim = X_train.shape[1]
print('Size of training set: {}'.format(train_size))
print('Size of development set: {}'.format(dev_size))
print('Size of testing set: {}'.format(test_size))
print('Dimension of data: {}'.format(data_dim))def _shuffle(X, Y):# This function shuffles two equal-length list/array, X and Y, together.randomize = np.arange(len(X))np.random.shuffle(randomize)return (X[randomize], Y[randomize]) #将X,Y的所有行随机重新排列def _sigmoid(z):return np.clip(1 / (1.0 + np.exp(-z)), 1e-8, 1 - (1e-8))def _f(X, w, b):return _sigmoid(np.matmul(X, w) + b)def _predict(X, w, b):return np.round(_f(X, w, b)).astype(np.int) #round四舍五入，保证预测值是0-1def _accuracy(Y_pred, Y_label):# This function calculates prediction accuracyacc = 1 - np.mean(np.abs(Y_pred - Y_label))return acc
def _cross_entropy_loss(y_pred, Y_label):cross_entropy = -np.dot(Y_label, np.log(y_pred)) - np.dot((1 - Y_label), np.log(1 - y_pred))return cross_entropydef _gradient(X, Y_label, w, b):# This function computes the gradient of cross entropy loss with respect to weight w and bias b.y_pred = _f(X, w, b)pred_error = Y_label - y_predw_grad = -np.sum(pred_error * X.T, 1)#Loss对w，b的倒数b_grad = -np.sum(pred_error)return w_grad, b_gradw = np.zeros((data_dim,))
b = np.zeros((1,))
max_iter = 100
batch_size = 1
learning_rate = 0.02
# Keep the loss and accuracy at every iteration for plotting
train_loss = []
dev_loss = []
train_acc = []
dev_acc = []
# Calcuate the number of parameter updates
step = 1
# Iterative training
for epoch in range(max_iter): #max_iter是训练次数# Random shuffle at the begging of each epochX_train, Y_train = _shuffle(X_train, Y_train)# Mini-batch trainingfor idx in range(int(np.floor(train_size / batch_size))): #将batch_size行数据作为一次数据更新w,b，总共更新了len(X)*max_iter/8次X = X_train[idx*batch_size:(idx+1)*batch_size]Y = Y_train[idx*batch_size:(idx+1)*batch_size]# Compute the gradientw_grad, b_grad = _gradient(X, Y, w, b)   # gradient descent update# learning rate decay with timew = w - learning_rate/np.sqrt(step) * w_gradb = b - learning_rate/np.sqrt(step) * b_gradstep = step + 1y_train_pred = _f(X_train, w, b)Y_train_pred = np.round(y_train_pred)train_acc.append(_accuracy(Y_train_pred, Y_train))train_loss.append(_cross_entropy_loss(y_train_pred, Y_train) / train_size)y_dev_pred = _f(X_dev, w, b)Y_dev_pred = np.round(y_dev_pred)dev_acc.append(_accuracy(Y_dev_pred, Y_dev))dev_loss.append(_cross_entropy_loss(y_dev_pred, Y_dev) / dev_size)import matplotlib.pyplot as pltprint('Training accuracy: {}'.format(train_acc[len(train_acc)-1]))
print('Development accuracy: {}'.format(dev_acc[len(dev_acc)-1]))# Loss curve
plt.plot(train_loss)
plt.plot(dev_loss)
plt.title('Loss')
plt.legend(['train', 'dev'])
plt.savefig('loss.png')
plt.show()# Accuracy curve
plt.plot(train_acc)
plt.plot(dev_acc)
plt.title('Accuracy')
plt.legend(['train', 'dev'])
plt.savefig('acc.png')
plt.show()predictions = _predict(X_test, w, b)
with open(output_fpath.format('logistic'), 'w') as f:f.write('id,label\n')for i, label in  enumerate(predictions):f.write('{},{}\n'.format(i, label))# Print out the most significant weights
ind = np.argsort(np.abs(w))[::-1]
with open(X_test_fpath) as f:content = f.readline().strip('\n').split(',')
features = np.array(content)
for i in ind[0:10]:print(features[i], w[i])

李宏毅机器学习课后作业（hw2）相关推荐

李宏毅机器学习课后作业（hw1）
数据集下载链接: 链接: https://pan.baidu.com/s/1k7cEfzza7zZqaosEgPjlxw 提取码: 8z93 完整代码 import numpy as np impor ...
【深度解析→博文总结】李宏毅机器学习2023作业02Classification(Framewise Phoneme Prediction)
文章目录 [系列文章] [简要说明] [视频分享] [作业详情] [调参记录] [Simple Baseline:0.49798] [Medium Baseline:0.66440] [Stong B ...
吴恩达机器学习课后作业——偏差和方差
1.写在前面吴恩达机器学习的课后作业及数据可以在coursera平台上进行下载,只要注册一下就可以添加课程了.所以这里就不写题目和数据了,有需要的小伙伴自行去下载就可以了. 作业及数据下载网址:吴恩 ...
目录:吴恩达机器学习课后作业
简单介绍本博客为作者自行完成的吴恩达机器学习课后练习题目录,均使用PyTorch进行实现,无法保证对错,仅供参考. 作业题目以及源代码百度云盘连接提取码:3dvb 题目的命名方式与下表中的作业名 ...
吴恩达机器学习课后作业——SVM支持向量机
支持向量机一.作业内容在本练习的前半部分,您将对各种示例2D数据集使用支持向量机(svm).使用这些数据集进行试验将帮助您直观地了解支持向量机的工作方式,以及如何在支持向量机中使用高斯核.在练习的 ...
吴恩达机器学习课后作业——线性回归（Python实现）
1.写在前面吴恩达机器学习的课后作业及数据可以在coursera平台上进行下载,只要注册一下就可以添加课程了.所以这里就不写题目和数据了,有需要的小伙伴自行去下载就可以了. 作业及数据下载网址:吴恩 ...
⭐李宏毅机器学习2020作业汇总
更新进度:■■■■■□□□□□□□□□□□□□□□|30% 李宏毅机器学习code 序号主题完成情况作业一 Linear Regression ✅ 作业二 Classification ✅ 作业 ...
吴恩达机器学习课后作业——线性回归
一.单变量线性回归一.作业内容假设你是一家特许经营餐厅的首席执行官,正在考虑在不同的城市开一家新店,该连锁店已经在各个城市开设了餐车,文件ex1data1.txt中包含你这些城市的利润和人口数据. ...
hw1例题-李宏毅老师课后作业
写在前面跟着李宏毅老师从头开始学习深度学习,因此这里用CSDN记录深度学习的课后例题,需要注意的是以下代码来源于网上,应该是李老师的代码,版权不属于我,写这篇博客主要为了巩固自己关于机器(深度)学习 ...

李宏毅机器学习课后作业（hw2）

直接上代码

李宏毅机器学习课后作业（hw2）相关推荐

最新文章

热门文章