Tensorflow实现回归（例子）

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#制造数据，加上随机噪声
x_data = np.linspace(-0.5, 0.5, 200)[:,np.newaxis]
noise = np.random.normal(0, 0.02, x_data.shape)
y_data=np.square(x_data)+noise

#定义两层简单的网络
x=tf.placeholder(tf.float32,[None,1])
y=tf.placeholder(tf.float32,[None,1])

w1=tf.Variable(tf.random_normal([1,10]))
b1=tf.Variable(tf.zeros([1,10]))

wx_plus_b1=tf.matmul(x,w1)+b1
l1=tf.nn.tanh(wx_plus_b1)

w2=tf.Variable(tf.random_normal([10,1]))
b2=tf.Variable(tf.zeros([1,1]))

wx_plus_b2=tf.matmul(l1,w2)+b2
predict=tf.nn.tanh(wx_plus_b2)

#损失函数选用SME
loss=tf.reduce_mean(tf.square(y-predict))
#优化函数选取梯度下降法
train=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

with tf.Session() as sess:
    predict_y = None
    sess.run(tf.global_variables_initializer())
    for i in range(2000):
        sess.run(train,feed_dict={x:x_data,y:y_data})

    #训练完成后，通过模型得到预测的y值
    predict_y=sess.run(predict,feed_dict={x:x_data})
    plt.figure()
    plt.scatter(x_data,y_data)
    plt.plot(x_data,predict_y,'r',lw=5)
    plt.show()

更改代码实现Tensorboard 网络结构绘制

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#制造数据，加上随机噪声
x_data = np.linspace(-0.5, 0.5, 200)[:,np.newaxis]
noise = np.random.normal(0, 0.02, x_data.shape)
y_data=np.square(x_data)+noise
with tf.name_scope('input'):
#定义两层简单的网络
    x=tf.placeholder(tf.float32,[None,1],name='input-x')
    y=tf.placeholder(tf.float32,[None,1],name='input-y')

with tf.name_scope('layer'):
    with tf.name_scope('weights1'):
        w1=tf.Variable(tf.random_normal([1,10]),name='w1')
    with tf.name_scope('biases1'):
        b1=tf.Variable(tf.zeros([1,10]),name='b1')
    with tf.name_scope('wx_plus_b1'):
        wx_plus_b1=tf.matmul(x,w1)+b1
    with tf.name_scope('tanh1'):
        l1=tf.nn.tanh(wx_plus_b1)
    with tf.name_scope('weights2'):
        w2=tf.Variable(tf.random_normal([10,1]))
    with tf.name_scope('biases2'):
        b2=tf.Variable(tf.zeros([1,1]))
    with tf.name_scope('wx_plus_b2'):
        wx_plus_b2=tf.matmul(l1,w2)+b2
    with tf.name_scope('tanh2'):
        predict=tf.nn.tanh(wx_plus_b2)
with tf.name_scope('loss'):
#损失函数选用SME
    loss=tf.reduce_mean(tf.square(y-predict),name='loss')
with tf.name_scope('train'):
#优化函数选取梯度下降法
    train=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    writer=tf.summary.FileWriter('logs/',sess.graph)
    for i in range(2000):
        sess.run(train,feed_dict={x:x_data,y:y_data})

    #训练完成后，通过模型得到预测的y值
    predict_y=sess.run(predict,feed_dict={x:x_data})
    plt.figure()
    plt.scatter(x_data,y_data)
    plt.plot(x_data,predict_y,'r',lw=5)
    plt.show()