The computations you’ll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. To make it easier to understand, debug, and optimize TensorFlow programs, we’ve included a suite of visualization tools called TensorBoard. You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. When TensorBoard is fully configured, it looks like this:
TensorBoard 涉及到的運算在訓練大量的深度神經網絡中出現的複雜運算。
爲了更方便 TensorFlow 程序的理解、調試與優化，我們發佈了一套叫做 TensorBoard 的可視化工具。你可以用 TensorBoard 來展現你的 TensorFlow 圖像，繪製圖像生成的定量指標圖以及附加數據。
當 TensorBoard 設置完成後，它應該是這樣子的：
This tutorial is intended to get you started with simple TensorBoard usage. There are other resources available as well! The TensorBoard’s GitHub has a lot more information on TensorBoard usage, including tips & tricks, and debugging information.
這篇教程傾向於TensorBoard的簡單用法。github上有詳細的信息，包括使用，提示，調試。
第一步序列化數據
TensorBoard operates by reading TensorFlow events files, which contain summary data that you can generate when running TensorFlow. Here’s the general lifecycle for summary data within TensorBoard.
讀取events files 創建TensorBoard。下面講解一下summary data的生命週期。
First, create the TensorFlow graph that you’d like to collect summary data from, and decide which nodes you would like to annotate with summary operations.
第一步創建tf graph,這個graph由一些summary data 的nodes組成。
For example, suppose you are training a convolutional neural network for recognizing MNIST digits. You’d like to record how the learning rate varies over time, and how the objective function is changing. Collect these by attaching tf.summary.scalar ops to the nodes that output the learning rate and loss respectively. Then, give each scalar_summary a meaningful tag, like ‘learning rate’ or ‘loss function’.
回顧一下MNIST的例子，我們打算記錄學習率，和目標函數的變化。應用怎麼做？使用tf.summary.scalar操作
Perhaps you’d also like to visualize the distributions of activations coming off a particular layer, or the distribution of gradients or weights. Collect this data by attaching tf.summary.histogram ops to the gradient outputs and to the variable that holds your weights, respectively.
如果你想看到主要層的分佈或者權重分佈，怎麼做？使用tf.summary.histogram操作
For details on all of the summary operations available, check out the docs on summary operations.
更多詳情，請看summary文檔
Operations in TensorFlow don’t do anything until you run them, or an op that depends on their output. And the summary nodes that we’ve just created are peripheral to your graph: none of the ops you are currently running depend on them. So, to generate summaries, we need to run all of these summary nodes. Managing them by hand would be tedious, so use tf.summary.merge_all to combine them into a single op that generates all the summary data.
我們需要運行所有節點，管理這個節點的運行效率很低。因此我們使用tf.summary.merge_all 合併所有節點成一個單獨的操作，一次生成所有的summary data
Then, you can just run the merged summary op, which will generate a serialized Summary protobuf object with all of your summary data at a given step. Finally, to write this summary data to disk, pass the summary protobuf to a tf.summary.FileWriter.
接着，合併成一個操作之後，會生成一個序列化的Summary protobuf對象。爲了將這個對象寫入磁盤中，我們使用tf.summary.FileWriter
The FileWriter takes a logdir in its constructor - this logdir is quite important, it’s the directory where all of the events will be written out. Also, the FileWriter can optionally take a Graph in its constructor. If it receives a Graph object, then TensorBoard will visualize your graph along with tensor shape information. This will give you a much better sense of what flows through the graph: see Tensor shape information.
FileWriter （logdir,graph）logdir是存儲events files的目錄。graphDef是可選的，添加則顯示graph
Now that you’ve modified your graph and have a FileWriter, you’re ready to start running your network! If you want, you could run the merged summary op every single step, and record a ton of training data. That’s likely to be more data than you need, though. Instead, consider running the merged summary op every n steps.
現在我們修改MNIST的代碼，添加上述操作。我們使用每100步一次summary 操作。
The code example below is a modification of the simple MNIST tutorial, in which we have added some summary ops, and run them every ten steps. If you run this and then launch tensorboard –logdir=/tmp/tensorflow/mnist, you’ll be able to visualize statistics, such as how the weights or accuracy varied during training. The code below is an excerpt; full source is here.
下面代碼是MNIST的修改完的代碼。使用tensorboard –logdir=/tmp/tensorflow/mnist去運行TensorBoard。你將會看到一些關於權重變化率，正確率的統計量在模型運行期間。
# -*- coding: utf-8 -*-
"""
A simple MNIST classifier which displays summaries in TensorBoard.
This is an unimpressive MNIST model, but it is a good example of using
tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
naming summary tags so that they are grouped meaningfully in TensorBoard.
It demonstrates the functionality of every TensorBoard dashboard.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import tensorflow as tf
import input_data
FLAGS = None
def train():
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir,
one_hot=True,
fake_data=FLAGS.fake_data)
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)))
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 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(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)
# 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()
# 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 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}
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, 'step%03d' % 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)
train_writer.close()
test_writer.close()
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('--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=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
'tensorflow/mnist/input_data'),
help='Directory for storing input data')
parser.add_argument(
'--log_dir',
type=str,
default=os.path.join(os.getenv('TEST_TMPDIR', '/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)運行：
tensorboard –logdir=D:\tmp\tensorflow\mnist\logs
