TensorBoard
TensorBoard的官网教程如下：
https://www.tensorflow.org/versions/r0.7/how_tos/summaries_and_tensorboard/index.html
简单解释下：TensorBoard是个可视化工具，可以用来查看TensorFlow的图以及过程中的各种值和图像等。
1. 在tensorflow程序中给需要的节点添加“summary operations”，“summary operations”会收集该节点的数据，并标记上第几步、时间戳等标识，写入事件文件。
事件文件的形式如下所示：
2. TensorBoard读取事件文件，并可视化Tensorflow的流程。
利用官网提供的例子进行演示，官方例子提供了一个基于mnist的例子，我的文件的路径如下：
~/libsource/tensorflow/tensorflow/examples/tutorials/mnist，
其中~/libsource/tensorflow/改为用户自己的tensorflow路径即可。
上述目录下有一个mnist_with_summaries.py文件，即为加入了“summary operations”的mnist demo。
启动mnist_with_summaries.py，
python mnist_with_summaries.py
mnist_with_summaries.py的源码如下：
# Copyright 2015 The TensorFlow Authors. 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__importabsolute_importfrom__future__importdivisionfrom__future__importprint_functionimporttensorflowastffromtensorflow.examples.tutorials.mnistimportinput_dataflags = tf.app.flagsFLAGS = flags.FLAGSflags.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')deftrain():# Import datamnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, fake_data=FLAGS.fake_data) sess = tf.InteractiveSession()# Create a multilayer model.# Input placehoolderswithtf.name_scope('input'): x = tf.placeholder(tf.float32, [None,784], name='x-input') y_ = tf.placeholder(tf.float32, [None,10], name='y-input')withtf.name_scope('input_reshape'): image_shaped_input = tf.reshape(x, [-1,28,28,1]) tf.image_summary('input', image_shaped_input,10)# We can't initialize these variables to 0 - the network will get stuck.defweight_variable(shape):"""Create a weight variable with appropriate initialization."""initial = tf.truncated_normal(shape, stddev=0.1)returntf.Variable(initial)defbias_variable(shape):"""Create a bias variable with appropriate initialization."""initial = tf.constant(0.1, shape=shape)returntf.Variable(initial)defvariable_summaries(var, name):"""Attach a lot of summaries to a Tensor."""withtf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.scalar_summary('mean/'+ name, mean)withtf.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)defnn_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.withtf.name_scope(layer_name):# This Variable will hold the state of the weights for the layerwithtf.name_scope('weights'): weights = weight_variable([input_dim, output_dim]) variable_summaries(weights, layer_name +'/weights')withtf.name_scope('biases'): biases = bias_variable([output_dim]) variable_summaries(biases, layer_name +'/biases')withtf.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)returnactivations hidden1 = nn_layer(x,784,500,'layer1')withtf.name_scope('dropout'): keep_prob = tf.placeholder(tf.float32) tf.scalar_summary('dropout_keep_probability', keep_prob) dropped = tf.nn.dropout(hidden1, keep_prob) y = nn_layer(dropped,500,10,'layer2', act=tf.nn.softmax)withtf.name_scope('cross_entropy'): diff = y_ * tf.log(y)withtf.name_scope('total'): cross_entropy = -tf.reduce_mean(diff) tf.scalar_summary('cross entropy', cross_entropy)withtf.name_scope('train'): train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize( cross_entropy)withtf.name_scope('accuracy'):withtf.name_scope('correct_prediction'): correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))withtf.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 summariesdeffeed_dict(train):"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""iftrainorFLAGS.fake_data: xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data) k = FLAGS.dropoutelse: xs, ys = mnist.test.images, mnist.test.labels k =1.0return{x: xs, y_: ys, keep_prob: k}foriinrange(FLAGS.max_steps):ifi %10==0:# Record summaries and test-set accuracysummary, 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 trainifi %100==99:# Record execution statsrun_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%d'% i) train_writer.add_summary(summary, i) print('Adding run metadata for', i)else:# Record a summarysummary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True)) train_writer.add_summary(summary, i)defmain(_):iftf.gfile.Exists(FLAGS.summaries_dir): tf.gfile.DeleteRecursively(FLAGS.summaries_dir) tf.gfile.MakeDirs(FLAGS.summaries_dir) train()if__name__ =='__main__': tf.app.run()
其中
flags.DEFINE_string('summaries_dir','/tmp/mnist_logs','Summaries directory')
标识了事件文件的输出路径。该例中，输出路径为/tmp/mnist_logs
打开TensorBoard服务
tensorboard --logdir=/tmp/mnist_logs/
在浏览器中进行浏览http://0.0.0.0:6006，在这个可视化界面中，可以查看tensorflow图和各种中间输出等。
TensorBoard的不过是个调试工具，看起来很酷炫有没有，但怎么充分利用，我想还是要对tensorflow充分了解。下面要转向对tensorflow的学习中了。
通过pip方式安装的tensorflow，在使用tensorboard的时候，可能会出现如下Bug：
WARNING:tensorflow:IOError [Errno2] No suchfileordirectory:'/usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/TAG'onpath/usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/TAGWARNING:tensorflow:UnabletoreadTensorBoard tagStarting TensorBoardonport6006
解决方案：
下载tensorflow的github的源代码，将tensorflow的tensorboard目录下的TAG文件拷贝到Python下面的tensorboard目录下即可，我的目录如下：
sudo cp ~/libsource/tensorflow/tensorflow/tensorflow/tensorboard/TAG/usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/
