概述

在《TensorBoard可视化实战(二)》中讲述了如何使生成的图更具有可阅读性，但是生成的可阅读参数较少，本文旨在增加TensorBoard的展示信息。

mnist_board_2.py

代码

import os

import tensorflow as tf

LOGDIR = './mnist'

mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)

# 加上name值，方便在tensorboard里面查看

def conv_layer(input, size_in, size_out, name='conv'):

# 定义名字作用域

with tf.name_scope(name):

w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name='W')

# w的值也是对训练准确率有很大的影响，下面初始化的方法就会造成网络不收敛，因为训练时w的变化幅度比较小

# w = tf.Variable(tf.zeros([5, 5, size_in, size_out]), name='W')

b = tf.Variable(tf.constant(0.1, shape=[size_out]), name='B')

conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding='SAME')

act = tf.nn.relu(conv + b)

# 分布情况：在训练过程中查看分布情况

tf.summary.histogram('weights', w)

tf.summary.histogram('biases', b)

tf.summary.histogram('activations', act)

return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def fc_layer(input, size_in, size_out, name='fc'):

with tf.name_scope(name):

w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name='W')

b = tf.Variable(tf.constant(0.1, shape=[size_out]), name='B')

act = tf.nn.relu(tf.matmul(input, w) + b)

tf.summary.histogram('weights', w)

tf.summary.histogram('biases', b)

tf.summary.histogram('activations', act)

return act

def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):

tf.reset_default_graph()

sess = tf.Session()

# setup placeholders, and reshape the data

x = tf.placeholder(tf.float32, shape=[None, 784], name='x')

x_image = tf.reshape(x, [-1, 28, 28, 1])

# 显示当前的输入：数据集中的图像

tf.summary.image('input', x_image, 3)

y = tf.placeholder(tf.float32, shape=[None, 10], name='labels')

if use_two_conv:

conv1 = conv_layer(x_image, 1, 32, 'conv1')

conv_out = conv_layer(conv1, 32, 64, 'conv2')

else:

conv1 = conv_layer(x_image, 1, 64, 'conv')

conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])

if use_two_fc:

fc1 = fc_layer(flattened, 7 * 7 * 64, 1024, 'fc1')

embedding_input = fc1

embedding_size = 1024

logits = fc_layer(fc1, 1024, 10, 'fc2')

else:

embedding_input = flattened

embedding_size = 7 * 7 * 64

logits = fc_layer(flattened, 7 * 7 * 64, 10, 'fc')

with tf.name_scope('loss'):

xent = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y), name='loss')

# 指标变化：随着网络的迭代，loss值的变化

tf.summary.scalar('loss', xent)

with tf.name_scope('train'):

train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)

with tf.name_scope('accuracy'):

correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 指标变化：随着迭代进行，精度的变化情况

tf.summary.scalar('accuracy', accuracy)

# 把所有要显示的参数聚在一起

summ = tf.summary.merge_all()

emdedding = tf.Variable(tf.zeros([1024, embedding_size]))

assignment = emdedding.assign(embedding_input)

saver = tf.train.Saver()

sess.run(tf.global_variables_initializer())

# 保存路径

tenboard_dir = './tensorboard/test3/'

# 指定一个文件用来保存图

writer = tf.summary.FileWriter(tenboard_dir + hparam)

# 把图add进去

writer.add_graph(sess.graph)

for i in range(2001):

batch = mnist.train.next_batch(100)

# 每迭代5次对结果进行保存

if i % 5 == 0:

[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})

writer.add_summary(s, i)

sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})

def make_hparam_string(learning_rate, use_two_fc, use_two_conv):

conv_param = 'conv=2' if use_two_conv else 'conv=1'

fc_param = 'fc=2' if use_two_fc else 'fc=1'

return 'lr_%.0E,%s,%s' % (learning_rate, conv_param, fc_param)

def main():

# You can try adding some more learning rates

for learning_rate in [1E-4]:

# Include 'False' as a value to try different model architectures.

for use_two_fc in [True]:

for use_two_conv in [True]:

# Construct a hyperparameter string for each one(example: 'lr_1E-3,fc=2,conv=2')

hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)

print('Starting run for %s' % hparam)

# Actually run with the new settings

mnist_model(learning_rate, use_two_fc, use_two_conv, hparam)

if __name__ == '__main__':

main()

运行示例

运行mnist_board_2.py后，生成如下test3文件

命令窗口进入生成文件的tensorboard目录，执行tensorboard --logdir=./test3，如下图所示

在浏览器输入网址：http://localhost:6006，即可查看生成图

损失值、准确率随着迭代次数的进行，其指标变化情况

输入值的显示

权重、偏置等在训练过程中的分布情况(二维显示)：

权重、偏置等在训练过程中的分布情况(三维显示)：

总结

主要增加如下信息：

1.权重、偏置等在训练过程中的分布情况：tf.summary.histogram('weights', w)

2.损失值、准确率随着迭代次数的进行，其指标变化情况：tf.summary.scalar('accuracy', accuracy)

3.输入值的显示：tf.summary.image('input', x_image, 3)

上图展示的数据比文章《TensorBoard可视化实战(二)》明显增多，可以观察loss、accuracy值的变化情况，那么能否对比不同的参数(w、b、学习率)值之间的loss、accuracy变化情况呢？以此来直观的挑选出最佳参数值？答案当然是可以的。

下篇文章讲述如何实现上述情况——利用TensorBoard进行参数选择。