TensorBoard的使用

作者: V_爱一世春秋 | 来源:发表于2019-11-22 09:18 被阅读0次

TensorBoard是TensorFlow官方推出的可视化工具，它可以将模型训练过程中的各种汇总数据展示出来。我们在使用TensorFlow训练大型深度学习神经网络时，中间的计算过程可能非常复杂，可以使用TensorBoard观察训练过程中的各种可视化数据。它的流程是，在执行TensorFlow计算图的过程中，将各种类型的数据汇总并记录到日志文件中。然后使用TensorBoard读取这些日志文件，解析数据并生成数据可视化Web页面。

用一个MNIST数据集的手写字体识别，看一下各种类型数据的汇总和展示。

先说一下我的环境，我用的是Cuda9.0+Ubuntu16.04+Tensorflow-gpu1.12+Python3.6。

训练过程

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

max_steps=1000

learning_rate=0.001

dropout=0.9

data_dir='/tmp/tensorflow/mnist/input_data'

#汇总数据的日志存放地址

log_dir='/tmp/tensorflow/mnist/logs/mnist_with_summaries'

# Import data

mnist = input_data.read_data_sets(data_dir,one_hot=True)

sess = tf.InteractiveSession()

# Create a multilayer model.

# Input placeholders

with tf.name_scope('input'):

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

y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')

with tf.name_scope('input_reshape'):

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

tf.summary.image('input', image_shaped_input, 10)

# We can't initialize these variables to 0 - the network will get stuck.

def weight_variable(shape):

"""Create a weight variable with appropriate initialization."""

initial = tf.truncated_normal(shape, stddev=0.1)

return tf.Variable(initial)

def bias_variable(shape):

"""Create a bias variable with appropriate initialization."""

initial = tf.constant(0.1, shape=shape)

return tf.Variable(initial)

def variable_summaries(var):

"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""

with tf.name_scope('summaries'):

mean = tf.reduce_mean(var)

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

with tf.name_scope('stddev'):

stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))

#进行记录和汇总

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

tf.summary.scalar('max', tf.reduce_max(var))

tf.summary.scalar('min', tf.reduce_min(var))

#直接记录变量var的直方图数据

tf.summary.histogram('histogram', var)

def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):

"""Reusable code for making a simple neural net layer.

It does a matrix multiply, bias add, and then uses relu to nonlinearize.

It also sets up name scoping so that the resultant graph is easy to read,

and adds a number of summary ops.

"""

# Adding a name scope ensures logical grouping of the layers in the graph.

with tf.name_scope(layer_name):

# This Variable will hold the state of the weights for the layer

with tf.name_scope('weights'):

weights = weight_variable([input_dim, output_dim])

variable_summaries(weights)

with tf.name_scope('biases'):

biases = bias_variable([output_dim])

variable_summaries(biases)

with tf.name_scope('Wx_plus_b'):

preactivate = tf.matmul(input_tensor, weights) + biases

tf.summary.histogram('pre_activations', preactivate)

activations = act(preactivate, name='activation')

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

return activations

hidden1 = nn_layer(x, 784, 500, 'layer1')

with tf.name_scope('dropout'):

keep_prob = tf.placeholder(tf.float32)

tf.summary.scalar('dropout_keep_probability', keep_prob)

dropped = tf.nn.dropout(hidden1, keep_prob)

# Do not apply softmax activation yet, see below.

y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

with tf.name_scope('cross_entropy'):

# The raw formulation of cross-entropy,

#

# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),

# reduction_indices=[1]))

#

# can be numerically unstable.

#

# So here we use tf.nn.softmax_cross_entropy_with_logits on the

# raw outputs of the nn_layer above, and then average across

# the batch.

diff = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)

with tf.name_scope('total'):

cross_entropy = tf.reduce_mean(diff)

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

with tf.name_scope('train'):

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

cross_entropy)

with tf.name_scope('accuracy'):

with tf.name_scope('correct_prediction'):

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

with tf.name_scope('accuracy'):

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

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

# Merge all the summaries and write them out to /tmp/mnist_logs (by default)

merged = tf.summary.merge_all()

train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph)

test_writer = tf.summary.FileWriter(log_dir + '/test')

tf.global_variables_initializer().run()

# Train the model, and also write summaries.

# Every 10th step, measure test-set accuracy, and write test summaries

# All other steps, run train_step on training data, & add training summaries

def feed_dict(train):

"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""

if train:

xs, ys = mnist.train.next_batch(100)

k = dropout

else:

xs, ys = mnist.test.images, mnist.test.labels

k = 1.0

return {x: xs, y_: ys, keep_prob: k}

saver = tf.train.Saver()

for i in range(max_steps):

if i % 10 == 0: # Record summaries and test-set accuracy

summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))

test_writer.add_summary(summary, i)

print('Accuracy at step %s: %s' % (i, acc))

else: # Record train set summaries, and train

if i % 100 == 99: # Record execution stats

run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)

run_metadata = tf.RunMetadata()

summary, _ = sess.run([merged, train_step],

feed_dict=feed_dict(True),

options=run_options,

run_metadata=run_metadata)

train_writer.add_run_metadata(run_metadata, 'step%03d' % i)

train_writer.add_summary(summary, i)

saver.save(sess, log_dir+"/model.ckpt", i)

print('Adding run metadata for', i)

else: # Record a summary

summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))

train_writer.add_summary(summary, i)

train_writer.close()

test_writer.close()