TensorBoard

TensorBoard的官網教程如下：
https://www.tensorflow.org/versions/r0.7/how_tos/summaries_and_tensorboard/index.html

簡單解釋下：TensorBoard是個可視化工具，可以用來查看TensorFlow的圖以及過程中的各種值和圖像等。
1. 在tensorflow程序中給需要的節點添加“summary operations”，“summary operations”會收集該節點的數據，並標記上第幾步、時間戳等標識，寫入事件文件。
事件文件的形式如下所示：

2. TensorBoard讀取事件文件，並可視化Tensorflow的流程。

Demo演示

利用官網提供的例子進行演示，官方例子提供了一個基於mnist的例子，我的文件的路徑如下：
~/libsource/tensorflow/tensorflow/examples/tutorials/mnist，
其中~/libsource/tensorflow/改爲用戶自己的tensorflow路徑即可。
上述目錄下有一個mnist_with_summaries.py文件，即爲加入了“summary operations”的mnist demo。
啓動mnist_with_summaries.py，

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__ 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')
    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.image_summary('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, 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')

  with tf.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)

  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 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%d' % 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)


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()

其中

flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')

標識了事件文件的輸出路徑。該例中，輸出路徑爲/tmp/mnist_logs

打開TensorBoard服務

tensorboard --logdir=/tmp/mnist_logs/

在瀏覽器中進行瀏覽http://0.0.0.0:6006，在這個可視化界面中，可以查看tensorflow圖和各種中間輸出等。

TensorBoard的不過是個調試工具，看起來很酷炫有沒有，但怎麼充分利用，我想還是要對tensorflow充分了解。下面要轉向對tensorflow的學習中了。

Error 2 Bug解決

通過pip方式安裝的tensorflow，在使用tensorboard的時候，可能會出現如下Bug：

WARNING:tensorflow:IOError [Errno 2] No such file or directory: '/usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/TAG' on path /usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/TAG
WARNING:tensorflow:Unable to read TensorBoard tag
Starting TensorBoard  on port 6006

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，我的目錄如下：

sudo cp ~/libsource/tensorflow/tensorflow/tensorflow/tensorboard/TAG /usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/

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TensorBoard

Demo演示

Error 2 Bug解決

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Visio圖標下載鏈接

https://yachay.unat.edu.pe/blog/index.php?comment_area=format_blog&comment_component=blog&comment_co

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