tensorboard tensorflow中的可视化组件
在新版本的tensorflow 中tensorboard已经被整合,无需下载.其执行是利用了一个封装的内置服务器,性能不错. 我们可以将神经网络运行时的各类数据存储下来进行可视化展示,我首先展示其功能,然后再分解代码.本处例子源自tensorflow的官方源码,如果你需要了解更多,建议直接阅读官方文档
展示
最重要的网络结构的展示
基本数据的展示
在本例子中获取了,mean,stddev,max,min等数据.其他部分还包括images,图片本例子中展示的则是,mnist的展示图.
更多部分建议你运行源码自己体验一下
CODE
tf.summary使我们需要的 用来想tensorboard写入数据的方法tf.summary.scalar(‘accuracy’, accuracy) 如代码,scalar可以将数据传入,并在tensorboard中最终以表格的形式展示tf.summary.image(‘input’, image_shaped_input, NUM_CLASSES) image方法则是前面图片中image模块的数据传入方法
引用,定义基本参数
from __future__
import absolute_import
from __future__
import division
from __future__
import print_function
import argparse
import os
import sys
import tensorflow
as tf
from tensorflow.examples.tutorials.mnist
import input_data
os.environ[
'TF_CPP_MIN_LOG_LEVEL'] =
'2'
FLAGS =
None
NUM_CLASSES =
10
IMAGE_SIZE =
28
IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE
官方文档的代码写的咋一看非常复杂,不过结构上并不复杂.
读取数据,定义定义可视化节点
mnist = input_data.read_data_sets(
"/home/fonttian/Data/MNIST_data/",
one_hot=True,
fake_data=FLAGS.fake_data)
sess = tf.InteractiveSession()
with tf.name_scope(
'input'):
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS], name=
'x-input')
y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES], name=
'y-input')
with tf.name_scope(
'input_reshape'):
image_shaped_input = tf.reshape(x, [-
1, IMAGE_SIZE, IMAGE_SIZE,
1])
tf.summary.image(
'input', image_shaped_input, NUM_CLASSES)
``` 抽取代码部分内容,封装为函数
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# 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 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)))
tf.summary.scalar(
'stddev', stddev)
tf.summary.scalar(
'max', tf.reduce_max(var))
tf.summary.scalar(
'min', tf.reduce_min(var))
tf.summary.histogram(
'histogram', var)
def feed_dict(train):
"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
if train
or FLAGS.fake_data:
xs, ys = mnist.train.next_batch(
100, fake_data=FLAGS.fake_data)
k = FLAGS.dropout
else:
xs, ys = mnist.test.images, mnist.test.labels
k =
1.0
return {x: xs, y_: ys, keep_prob: k}
定义我们的神经网络
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.
"""
with tf.name_scope(layer_name):
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, IMAGE_PIXELS, FLAGS.hidden1_units,
'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)
y = nn_layer(dropped, FLAGS.hidden1_units, NUM_CLASSES,
'layer2', act=tf.identity)
定义损失函数和优化算法,准确率
with tf.name_scope(
'cross_entropy'):
diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=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(FLAGS.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)
写入数据
# Merge all the summaries
and write them
out to
# /tmp/tensorflow/mnist/logs/mnist_with_summaries (
by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(
FLAGS.log_dir +
'/train', sess.graph)
test_writer = tf.summary.FileWriter(
FLAGS.log_dir +
'/test')
tf.global_variables_initializer().run()
回话设计
for i
in range(
FLAGS.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,
'stepd' % i)
train_writer.add_summary(summary, 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)
# 停止writer
train_writer.close()
test_writer.close()
执行CODE
def main(_):
if tf.gfile.Exists(FLAGS.log_dir):
tf.gfile.DeleteRecursively(FLAGS.log_dir)
tf.gfile.MakeDirs(FLAGS.log_dir)
train()
if __name__ ==
'__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--fake_data', nargs=
'?', const=True,
type=bool,
default=False,
help=
'If true, uses fake data for unit testing.')
parser.add_argument(
'--max_steps',
type=int, default=
1000,
help=
'Number of steps to run trainer.')
parser.add_argument(
'--hidden1_units',
type=float, default=
500,
help=
'The number of neurons in the first hidden.')
parser.add_argument(
'--learning_rate',
type=float, default=
0.001,
help=
'Initial learning rate')
parser.add_argument(
'--dropout',
type=float, default=
0.9,
help=
'Keep probability for training dropout.')
parser.add_argument(
'--data_dir',
type=str,
default=
'/home/fonttian/Data/MNIST_data/',
help=
'Directory for storing input data')
parser.add_argument(
'--log_dir',
type=str,
default=
'/home/fonttian/Documents/tensorflow/TensorFlow-Basics/tmp/tensorflow/mnist/logs/mnist_with_summaries',
help=
'Summaries log directory')
FLAGS, unparsed =
parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[
0]] + unparsed)
tensorboard的运行
建议你运行该代码,进行更深入的尝试,