TensorBoard
TensorBoard的官網(wǎng)教程如下：
https://www.tensorflow.org/versions/r0.7/how_tos/summaries_and_tensorboard/index.html
簡(jiǎn)單解釋下：TensorBoard是個(gè)可視化工具宋欺，可以用來(lái)查看TensorFlow的圖以及過(guò)程中的各種值和圖像等泞辐。
1. 在tensorflow程序中給需要的節(jié)點(diǎn)添加“summary operations”每瞒，“summary operations”會(huì)收集該節(jié)點(diǎn)的數(shù)據(jù)，并標(biāo)記上第幾步、時(shí)間戳等標(biāo)識(shí)是辕，寫入事件文件说搅。
事件文件的形式如下所示：
2. TensorBoard讀取事件文件析孽，并可視化Tensorflow的流程伍绳。
利用官網(wǎng)提供的例子進(jìn)行演示，官方例子提供了一個(gè)基于mnist的例子乍桂，我的文件的路徑如下：
~/libsource/tensorflow/tensorflow/examples/tutorials/mnist冲杀，
其中~/libsource/tensorflow/改為用戶自己的tensorflow路徑即可效床。
上述目錄下有一個(gè)mnist_with_summaries.py文件，即為加入了“summary operations”的mnist demo权谁。
啟動(dòng)mnist_with_summaries.py剩檀，
python mnist_with_summaries.py
mnist_with_summaries.py的源碼如下：
# 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')
標(biāo)識(shí)了事件文件的輸出路徑。該例中旺芽，輸出路徑為/tmp/mnist_logs
打開(kāi)TensorBoard服務(wù)
tensorboard --logdir=/tmp/mnist_logs/
在瀏覽器中進(jìn)行瀏覽http://0.0.0.0:6006沪猴，在這個(gè)可視化界面中，可以查看tensorflow圖和各種中間輸出等采章。
TensorBoard的不過(guò)是個(gè)調(diào)試工具运嗜，看起來(lái)很酷炫有沒(méi)有，但怎么充分利用悯舟，我想還是要對(duì)tensorflow充分了解担租。下面要轉(zhuǎn)向?qū)ensorflow的學(xué)習(xí)中了。
通過(guò)pip方式安裝的tensorflow抵怎，在使用tensorboard的時(shí)候奋救，可能會(huì)出現(xiàn)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
解決方案：
下載tensorflow的github的源代碼，將tensorflow的tensorboard目錄下的TAG文件拷貝到Python下面的tensorboard目錄下即可反惕，我的目錄如下：
sudo cp ~/libsource/tensorflow/tensorflow/tensorflow/tensorboard/TAG/usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/
