Tensorflow学习笔记：一维逻辑回归

#coding:utf-8
'''
一维逻辑回归
'''

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-x))

learning_rate = 0.01
training_epochs = 1000

x1 = np.random.normal(-4, 2, 1000)
x2 = np.random.normal(4, 2, 1000)
xs = np.append(x1, x2) 
ys = np.asarray([0.] * len(x1) + [1.] * len(x2)) 
plt.scatter(xs, ys) 
X = tf.placeholder(tf.float32, shape=(None,), name="x") 
Y = tf.placeholder(tf.float32, shape=(None,), name="y")
w = tf.Variable([0., 0.], name="parameter", trainable=True)

y_model = tf.sigmoid(w[1] * X + w[0]) 

cost = tf.reduce_mean(-Y * tf.log(y_model) - (1 - Y) * tf.log(1 - y_model))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) 

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    prev_err = 0
    for epoch in range(training_epochs):
        err,_ = sess.run([cost,train_op],feed_dict={X:xs,Y:ys})
        print(epoch,err)
        if abs(prev_err - err) < 0.0001:
            break
        prev_err = err
    
    w_val = sess.run(w,feed_dict={X:xs,Y:ys})
all_xs = np.linspace(-10, 10, 100) 
plt.plot(all_xs, sigmoid(-(all_xs * w_val[1] + w_val[0]))) 
plt.show()