在學習深度網絡框架的過程中，我們發現一個問題，就是如何輸出各層網絡參數，用於更好地理解，調試和優化網絡？針對這個問題，TensorFlow開發了一個特別有用的可視化工具包：TensorBoard，既可以顯示網絡結構，又可以顯示訓練和測試過程中各層參數的變化情況。本博文分爲四個部分，第一部分介紹相關函數，第二部分是代碼測試，第三部分是運行結果，第四部分介紹相關參考資料。
一. 相關函數
TensorBoard的輸入是tensorflow保存summary data的日誌文件。日誌文件名的形式如：events.out.tfevents.1467809796.lei-All-Series 或 events.out.tfevents.1467809800.lei-All-Series。TensorBoard可讀的summary data有scalar，images，audio，histogram和graph。那麼怎麼把這些summary data保存在日誌文件中呢？
數值如學習率，損失函數用scalar_summary函數。tf.scalar_summary(節點名稱，獲取的數據)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.scalar_summary('accuracy', accuracy)各層網絡權重，偏置的分佈，用histogram_summary函數
preactivate = tf.matmul(input_tensor, weights) + biases
tf.histogram_summary(layer_name + '/pre_activations', preactivate)其他幾種summary data也是同樣的方式獲取，只是對應的獲取函數名稱換一下。這些獲取summary data函數節點和graph是獨立的，調用的時候也需要運行session。當需要獲取的數據較多的時候，我們一個一個去保存獲取到的數據，以及一個一個去運行會顯得比較麻煩。tensorflow提供了一個簡單的方法，就是合併所有的summary data的獲取函數，保存和運行只對一個對象進行操作。比如，寫入默認路徑中，比如/tmp/mnist_logs (by default)
merged = tf.merge_all_summaries()
train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train', sess.graph)
test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test')SummaryWriter從tensorflow獲取summary data，然後保存到指定路徑的日誌文件中。以上是在建立graph的過程中，接下來執行，每隔一定step，寫入網絡參數到默認路徑中，形成最開始的文件：events.out.tfevents.1467809796.lei-All-Series 或 events.out.tfevents.1467809800.lei-All-Series。
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 summarieis, and train
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)二. 代碼測試
# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""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 tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '
'for unit testing.')
flags.DEFINE_integer('max_steps', 1000, 'Number of steps to run trainer.')
flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
flags.DEFINE_float('dropout', 0.9, 'Keep probability for training dropout.')
flags.DEFINE_string('data_dir', '/tmp/data', 'Directory for storing data')
flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')
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 placehoolders
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [None, 784], name='x-input')
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
tf.image_summary('input', image_shaped_input, 10)
y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
keep_prob = tf.placeholder(tf.float32)
tf.scalar_summary('dropout_keep_probability', keep_prob)
# 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, name):
"""Attach a lot of summaries to a Tensor."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))
tf.histogram_summary(name, 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, layer_name + '/weights')
with tf.name_scope('biases'):
biases = bias_variable([output_dim])
variable_summaries(biases, layer_name + '/biases')
with tf.name_scope('Wx_plus_b'):
preactivate = tf.matmul(input_tensor, weights) + biases
tf.histogram_summary(layer_name + '/pre_activations', preactivate)
activations = act(preactivate, 'activation')
tf.histogram_summary(layer_name + '/activations', activations)
return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
dropped = tf.nn.dropout(hidden1, keep_prob)
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.nn.softmax)
with tf.name_scope('cross_entropy'):
diff = y_ * tf.log(y)
with tf.name_scope('total'):
cross_entropy = -tf.reduce_mean(diff)
tf.scalar_summary('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.scalar_summary('accuracy', accuracy)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.merge_all_summaries()
train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train', sess.graph)
test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test')
tf.initialize_all_variables().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 summarieis, and train
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)
def main(_):
if tf.gfile.Exists(FLAGS.summaries_dir):
tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
tf.gfile.MakeDirs(FLAGS.summaries_dir)
train()
if __name__ == '__main__':
tf.app.run()
三. 運行結果
代碼運行
生成文件
調用TensorBoard可視化運行結果
tensorboard --logdir=/tmp/mnist_logs/train/
打開鏈接 http://0.0.0.0:6006
EVENTS是訓練參數統計顯示，可以看到整個訓練過程中，各個參數的變換情況
IMAGES輸入和輸出標籤，省略
GRAPH網絡結構顯示
雙擊進去，可以顯示更多的細節，包括右邊的列表顯示
HISTOGRAM訓練過程參數分佈情況顯示
四. 參考資料
如果你想了解更多信息，可以參考一下資料:
https://www.tensorflow.org/versions/r0.9/how_tos/summaries_and_tensorboard/index.html
https://github.com/tensorflow/tensorflow/blob/r0.9/tensorflow/tensorboard/README.md
https://www.tensorflow.org/versions/r0.9/how_tos/graph_viz/index.html
