Tensorflow可视化好助手 Tensorboard (四)-Deep Learning

可视化好助手 Tensorboard

4.1 Tensorboard 可视化好帮手1
4.2 Tensorboard 可视化好帮手2

4.1 Tensorboard 可视化好帮手1

效果
搭建图纸
可能会遇到的问题

注意: 本节内容会用到浏览器, 而且与 tensorboard 兼容的浏览器是 Google Chrome. 使用其他的浏览器不保证所有内容都能正常显示.
学会用 Tensorflow 自带的 tensorboard 去可视化我们所建造出来的神经网络是一个很好的学习理解方式. 用最直观的流程图告诉你你的神经网络是长怎样,有助于你发现编程中间的问题和疑问.

相关代码：

# View more python learning tutorial on my Youtube and Youku channel!!!# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
# Youku video tutorial: http://i.youku.com/pythontutorial"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
from __future__ import print_function
import tensorflow as tfdef add_layer(inputs, in_size, out_size, activation_function=None):# add one more layer and return the output of this layerwith tf.name_scope('layer'):with tf.name_scope('weights'):Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')with tf.name_scope('biases'):biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')with tf.name_scope('Wx_plus_b'):Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)if activation_function is None:outputs = Wx_plus_belse:outputs = activation_function(Wx_plus_b, )return outputs# define placeholder for inputs to network
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)# the error between prediciton and real data
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)sess = tf.Session()# tf.train.SummaryWriter soon be deprecated, use following
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:  # tensorflow version < 0.12writer = tf.train.SummaryWriter('logs/', sess.graph)
else: # tensorflow version >= 0.12writer = tf.summary.FileWriter("logs/", sess.graph)# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:init = tf.initialize_all_variables()
else:init = tf.global_variables_initializer()
sess.run(init)# direct to the local dir and run this in terminal:
# $ tensorboard --logdir=logs

（1）效果

这次我们会介绍如何可视化神经网络。因为很多时候我们都是做好了一个神经网络，但是没有一个图像可以展示给大家看。这一节会介绍一个TensorFlow的可视化工具 — tensorboard

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Tensorflow可视化好助手 Tensorboard (四)-Deep Learning

可视化好助手 Tensorboard

4.1 Tensorboard 可视化好帮手1

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