tensorflow以逻辑回归模拟二维数据

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

plotdata = { “batchsize”:[], “loss”:[] }
def moving_average(a, w=10):
if len(a) < w:
return a[:]
return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]

生成模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(train_X.shape) 0.3 # y=2x，但是加入了噪声
显示模拟数据点
plt.plot(train_X, train_Y, ‘ro’, label=’Original data’)
plt.legend()
plt.show()

创建模型
占位符
X = tf.placeholder(“float”)
Y = tf.placeholder(“float”)
模型参数
W = tf.Variable(tf.random_normal([1]), name=”weight”)
b = tf.Variable(tf.zeros([1]), name=”bias”)

前向结构
z = tf.multiply(X, W)+ b

反向优化
cost =tf.reduce_mean( tf.square(Y - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent

初始化变量
init = tf.global_variables_initializer()
‘’ 训练参数
training_epochs = 20
display_step = 2

启动session
with tf.Session() as sess:
sess.run(init)

# Fit all training data
for epoch in range(training_epochs):
    for (x, y) in zip(train_X, train_Y):
        sess.run(optimizer, feed_dict={X: x, Y: y})

    #显示训练中的详细信息
    if epoch % display_step == 0:
        loss = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
        print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(W), "b=", sess.run(b))
        if not (loss == "NA" ):
            plotdata["batchsize"].append(epoch)
            plotdata["loss"].append(loss)

print (" Finished!")
print ("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), "W=", sess.run(W), "b=", sess.run(b))
#print ("cost:",cost.eval({X: train_X, Y: train_Y}))

#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()

plotdata["avgloss"] = moving_average(plotdata["loss"])
plt.figure(1)
plt.subplot(211)
plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
plt.xlabel('Minibatch number')
plt.ylabel('Loss')
plt.title('Minibatch run vs. Training loss')

plt.show()

print ("x=0.2，z=", sess.run(z, feed_dict={X: 0.2}))