Tensorboard.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%s' %n_layer):
        with tf.name_scope('weight'):
            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.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

with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 1], name='x_inputs')
    ys = tf.placeholder(tf.float32, [None, 1], name='y_inputs')

l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
predition = 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 - predition),
                                        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()
with tf.Session() as sess:
    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter('logs/', sess.graph)

    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)