使用tensorflow自定义线性分类器预测 良/恶性肿瘤

import tensorflow as tf

import numpy as np

import pandas as pd

train = pd.read_csv('../Datasets/Breast-Cancer/breast-cancer-train.csv')

test = pd.read_csv('../Datasets/Breast-Cancer/breast-cancer-test.csv')

X_train = np.float32(train[['Clump Thickness','Cell Size']].T)

y_train = np.float32(train['Type'].T)

X_test = np.float32(test[['Clump Thickness','Cell Size']].T)

y_test = np.float32(test['Type'].T)

#定义一个tensorflow的变量b作为线性模型的截距，同时设置初始值为 1 0
b = tf.Variable(tf.zeros([1]))

#定义一个tensorflow的变量w作为线性模型的参数，并设置初始值为-1.0至1.0之间均匀分布的随机数

W = tf.Variable(tf.random_uniform([1,2],-1.0,1.0))

#显示定义这个线性函数

y = tf.matmul(W,X_train)+b

#使用tensorflow中的reduce_mean 取得训练集上均方误差

loss = tf.reduce_mean(tf.square(y-y_train))

#使用梯度下降法估计参数W， b,并且设置迭代步长为0.01，这个与Scikit-learn中的SGDRegressor类似D

optimizer = tf.train.GradientDescentOptimizer(0.01)

#以最小二乘损失为优化目标

train = optimizer.minimize(loss)

#初始化所有变量
init = tf.initialize_all_variables()

#开启tensorflow中的会话
sess = tf.Session()

sess.run(init)

#迭代1000轮次，训练参数

for  step in range(0,1000):
sess.run(train)
if step % 200 == 0:
print(step,sess.run(W),sess.run(b))

test_negative = test.loc[test['Type'] == 0][['Clump Thickness','Cell Size']]

test_positive = test.loc[test['Type'] == 1][['Clump Thickness','Cell Size']]

#以最终的参数作图
import matplotlib.pyplot as plot
plot.scatter(test_negative['Clump Thickness'],test_negative['Cell Size'],marker='o',s=200,c='red')

plot.scatter(test_positive['Clump Thickness'],test_positive['Cell Size'],marker='x',s=150,c='black')

plot.xlabel('Clump Thickness')
plot.ylabel('Cell Size')

lx = np.arange(0,12)

ly = (0.5-sess.run(b)-lx*sess.run(W)[0][0])/sess.run(W)[0][1]

plot.plot(lx,ly,color='green')

plot.show()