tensorboard_practice.py

import tensorflow as tf
import numpy as np

def add_layer(inputs,in_size,out_size,activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights=tf.Variable(tf.random_normal([in_size,out_size]))
        with tf.name_scope('biases'):
            biases=tf.Variable(tf.zeros([1,out_size])+0.1)
        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)
        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,activation_function=tf.nn.relu)
#add output layer
prediction=add_layer(l1,10,1,activation_function=None)

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

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

init=tf.global_variables_initializer()

sess=tf.Session()

#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:
#             print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))