Tensorflow可用于训练大规模深度神经网络计算，为方便Tensorflow程序的理解、调试与优化，退出TensorBoard可视化工具。

TensorBoard展现Tensorflow图，绘制图像生成的定量指标图基显示附加数据。

TensorBoard通过读取Tensorflow事件文件运行，Tensorflow事件文件包含运行Tensorflow时生成的总结数据。

首先，创建希望收集总结数据的Tensorflow图，然后选择在哪个节点标注总结指令。

以MNIST训练为例，如果希望记录随着时间推移，学习速度如何变化，目标函数如何变化。那么，可以通过向节点附加tf.summary.scalar 来输出学习速度和误差。然后，为每个scalar_summary 分配有意义的tag，如learning rate 或loss function

如果，希望显示一个特殊层中激活的分布，或者是梯度分布、权重分布，可以分别向梯度输出和权重变量附加tf.summary.histogram 收集此类数据。

在Tensorflow中，只有在运行指令时，指令才会执行。tensorflow提供tf.summary.merge_all 可以将操作合并为一个op，生成所有总结数据。当执行这个合并的总结op时，它会在特定步骤将所有总结数据生成一个序列化的Summary的protobuf对象。如果需要将总结数据写入磁盘，可以将其传递给tf.summary.FileWriter

FileWriter 构造函数包含参数logdir，所有事件都会写到它所指的目录。FileWriter 构造函数包含参数Graph，TensorBoard在接到Graph对象后，可以将图与张量形状可视化。

下面对于MNIST，为其添加总结op，并启动tensorboard --logdir=/tmp/tensorflow/mnist ，从而将统计数据可视化，来显示训练期间的权重、准确性的变化

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.losses.sparse_softmax_cross_entropy on the
  # raw logit outputs of the nn_layer above.
  with tf.name_scope('total'):
    cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
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/mnist_logs (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
                                      sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/test')
tf.global_variables_initializer().run()

在上面初始化FileWriters 后，在训练和测试模型时，必须向FileWriter 添加总结

# 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
    summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
    train_writer.add_summary(summary, i)

启动TensorBoard

启动TensorBoard可以使用

tensorboard --logdir=path/to/log-directory

或者是

python -m tensorboard.main

TensorBoard运行后，可以查看

localhost:6006