TensorFlow北大公开课学习笔记-4.1损失函数

#coding:utf-8
#0导入模块，生成模拟数据集
import tensorflow as tf
import numpy as np
BATCH_SIZE=8
seed=23455


rdm=np.random.RandomState(seed)
X=rdm.rand(32,2)
Y_=[[x1+x2+(rdm.rand()/10.0-0.05)] for (x1,x2) in X]

# 1定义神经网络的输入，参数和输出，定义前向传播过程
x=tf.placeholder(tf.float32,shape=(None,2))
y_=tf.placeholder(tf.float32,shape=(None,1))
w1=tf.Variable(tf.random_normal([2,1],stddev=1,seed=1))
y=tf.matmul(x,w1)

# 2定义损失函数及反向传播方法
#  定义损失函数为MSE，反向传播方法为梯度下降。
loss_mse=tf.reduce_mean(tf.square(y_-y))
train_step=tf.train.GradientDescentOptimizer(0.001).minimize(loss_mse)

# 3生成会话，训练STEPS轮
with tf.Session() as sess:
    init_op=tf.global_variables_initializer()
    sess.run(init_op)
    STEPS=20000
    for i in range(STEPS):
        start=(i*BATCH_SIZE)%32
        end=start+BATCH_SIZE
        sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})
        if i%500==0:
            print("After %d training steps,w1 is:" %(i))
            print(sess.run(w1),"\n")
    print("Final w1 is:\n",sess.run(w1))