线性回归（2）

import tensorflow as tf
import numpy as np

def add_layer(inputs,in_size,out_size,n_layer,activation_funcion=None):
    layer_name='layer%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope('weights'):
            weights=tf.Variable(tf.random_normal([in_size,out_size]),name='W')
            tf.summary.histogram(layer_name+'/weights',weights)

        with tf.name_scope('biases'):
            biases=tf.Variable(tf.zeros([1,out_size])+0.1,name='b')
            tf.summary.histogram(layer_name+'/biases',biases)

        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b=tf.add(tf.matmul(inputs,weights),biases)

        if activation_funcion is None:
            outputs=Wx_plus_b
        else:
            outputs=activation_funcion(Wx_plus_b)
        tf.summary.histogram(layer_name+'/outputs',outputs)
    return outputs

x_data=np.linspace(-1,1,300,dtype=np.float32)[:,np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape).astype(np.float32)
y_data=np.square(x_data)-0.5+noise

xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])

l1=add_layer(xs,1,10,n_layer=1,activation_funcion=tf.nn.relu)
prediction=add_layer(l1,10,1,n_layer=2,activation_funcion=None)

with tf.name_scope('loss'):
    loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
    tf.summary.scalar('loss',loss)
    train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess=tf.Session()
merged=tf.summary.merge_all()
writer=tf.summary.FileWriter('logs/',sess.graph)
sess.run(tf.global_variables_initializer())

for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i%50==0:
        rs=sess.run(merged,feed_dict={xs:x_data,ys:y_data})
        writer.add_summary(rs,i)