er.py

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

def add_layer(inputs , in_size,out_size, n_layer , activation_function = 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)
            tf.summary.histogram(layer_name+'/biases', biases)
        with tf.name_scope('Wx_plus_b'):

            Wx_plus_b = tf.matmul(inputs,Weights) + biases
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
            tf.summary.histogram(layer_name+'/outputs', outputs)
    return outputs

x_data = np.linspace(-1,1,300)[:,np.newaxis]

noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data) - 0.5 + noise

fig = plt.figure()
ax = fig.add_subplot(1,1,1)

ax.scatter(x_data,y_data,s = 10,color = 'b')
plt.ion()
plt.show() 

with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32,[None , 1],name = 'x_input') #None 表示无论给多少个粒子都可以 输出为1
    ys = tf.placeholder(tf.float32,[None , 1],name = 'y_input')

从输入到隐层
l1 = add_layer(xs,1,10,n_layer=1,activation_function = tf.nn.tanh)

prediction = add_layer(l1 , 10,1,n_layer=2,activation_function = None)

with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
    reduction_indices=[1]))
    ##tf.summary.scaler('loss',loss)
    tf.summary.scalar('loss',loss)




with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

    init = tf.global_variables_initializer()
    sess = tf.Session()

    merged = tf.summary.merge_all()

    writer = tf.summary.FileWriter('logs/', sess.graph)

    sess.run(init)

for i in range(2001):
    sess.run(train_step , feed_dict = {xs:x_data,ys:y_data})
    if i%50==0:

        result = sess.run(merged , feed_dict = {xs:x_data,ys:y_data})
    writer.add_summary(result,i)
print(sess.run(loss, feed_dict = {xs:x_data,ys:y_data}))
try:
ax.lines.remove(lines[0])
except Exception:
pass
prediction_value = sess.run(prediction,feed_dict = {xs:x_data})
lines = plt.plot(x_data,prediction_value,'',lw = 5)
#plt.show()
plt.pause(0.1)