tensorboard_practice_2.py

import tensorflow as tf
import numpy as np

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]))
            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

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

#create layer
with tf.name_scope('inputs'):
    xs=tf.placeholder(tf.float32,[None,1],name='x_input')
    ys=tf.placeholder(tf.float32,[None,1],name='y_input')

#add hidden layer
l1=add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu)
#add output layer
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.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()
#tensorboard --logdir=logs
writer=tf.summary.FileWriter("logs/",sess.graph)

sess.run(tf.global_variables_initializer())

with tf.Session() as sess:
    sess.run(init)
    for i in range(1000):
        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)