from mxnet import autograd, nd num_inputs = 2 num_examples = 1000 true_w = [2,-3.4] true_b = 4.2 feature = nd.random.normal(scale=1,shape=(num_examples,num_inputs)) labels = true_w[0]*feature[:,0] + true_w[1]*feature[:,1] + true_b #print(labels.shape…

代码来自:https://zh.gluon.ai/chapter_supervised-learning/linear-regression-gluon.html from mxnet import ndarray as nd from mxnet import autograd from mxnet import gluon num_inputs = 2 num_examples = 1000 true_w = [2, -3.4] true_b = 4.2 X = nd.random_norm…

1. 下载教程 可以用浏览器下载zip格式并解压,在解压目录文件资源管理器的地址栏输入cmd进入命令行模式. 也可以 git pull https://github.com/mli/gluon-tutorials-zh 2.安装gluon CPU 添加源: # 优先使用清华conda镜像 conda config --prepend channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ # 也可选用科大conda镜像…

本文主要探索如何使用深度学习框架 MXNet 或 TensorFlow 实现线性回归模型?并且以 Kaggle 上数据集 USA_Housing 做线性回归任务来预测房价. 回归任务,scikit-learn 亦可以实现,具体操作可以查看 线性回归模型的原理与 scikit-learn 实现. 载入数据 import pandas as pd import numpy as np name = '../dataset/USA_Housing.csv' dataset = pd.read_csv(…

from mxnet import nd,autograd,init,gluon from mxnet.gluon import data as gdata,loss as gloss,nn num_inputs = 2 num_examples = 1000 true_w = [2,-3.4] true_b = 4.2 features = nd.random.normal(scale=1,shape=(num_examples,num_inputs)) labels = true_w[0]*…

http://mxnet.apache.org/api/python/gluon/data.html import sys import os import time import mxnet as mx from mxnet import autograd,nd from mxnet import gluon,init from mxnet.gluon import data as gdata,loss as gloss from mxnet.gluon import nn #gdata.Ar…

%matplotlib inline import mxnet from mxnet import nd,autograd from mxnet import gluon,init from mxnet.gluon import data as gdata,loss as gloss,nn import gluonbook as gb n_train, n_test, num_inputs = 20,100,200 true_w = nd.ones((num_inputs, 1)) * 0.01…

1. 数据集 dataset_train = gluon.data.ArrayDataset(X_train, y_train) data_iter = gluon.data.DataLoader(dataset_train, batch_size, shuffle=True) for data, label in data_iter: ... 2. 模型 gluon.nn:神经网络 gluon.nn.Sequential(),可添加: gluon.nn.Flatten() ⇒ Flattens…

1.线性回归从零实现 from mxnet import ndarray as nd import matplotlib.pyplot as plt import numpy as np import time num_inputs = 2 num_examples = 1000 w = [2,-3.4] b = 4.2 x = nd.random.normal(scale=1,shape=(num_examples,num_inputs)) y = nd.dot(x,nd.array(w).T…

scikit-learn对于线性回归提供了比较多的类库,这些类库都可以用来做线性回归分析,本文就对这些类库的使用做一个总结,重点讲述这些线性回归算法库的不同和各自的使用场景. 线性回归的目的是要得到输出向量\(\mathbf{Y}\)和输入特征\(\mathbf{X}\)之间的线性关系,求出线性回归系数\(\mathbf\theta\),也就是 \(\mathbf{Y = X\theta}\).其中\(\mathbf{Y}\)的维度为mx1,\(\mathbf{X}\)的维度为mxn,而\(\m…