TensorFlow入门：实现简单的神经网络并用tensorboard可视化

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2018/5/14 13:40
# @Author  : HJH
# @Site    : 
# @File    : add_layer.py
# @Software: PyCharm

import tensorflow as tf
import numpy as np

def add_layer(inputs,in_size,out_size,nlayer,activation_function=None):
    layer_name='layer%s'%nlayer
    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.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

if __name__=='__main__':
    X = np.linspace(-1, 1, 300)[:, np.newaxis]
    noise = np.random.normal(0, 0.05, X.shape)
    y = np.square(X) - 0.5 + noise

    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')
    l1=add_layer(xs,1,10,nlayer=1,activation_function=tf.nn.relu)
    prediction=add_layer(l1,10,1,nlayer=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()
    writer=tf.summary.FileWriter("logs/",sess.graph)
    sess.run(init)

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

运行文件后，产生以下文件：

在dos中进入logs文件所在的文件夹，并输入tensorboard --logdir=logs

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