Tensorboard从入门到精通（1）——Visualizing Learning

简单版：

有个要可视化的变量，var首先

    tf.summary.scalar('min', tf.reduce_min(var))
    tf.summary.histogram('histogram', var)

然后合并

merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',sess.graph)

最后在循环中不断添加每一步的训练日志

for i in range(FLAGS.max_steps):
    summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
    train_writer.add_summary(summary, i)

详细版：

1、概念

我们可以使用TensorBoard做什么：

模型结构、损失函数曲线、精度曲线等等，都可以在TensorBoard中可视化的表现出来。

2、安装

使用PIP安装TensorFlow时就会自动安装TensorBoard

3、序列化summary数据

TensorBoard的运行需要依靠读取运行TensorFlow时生成的summary数据。那么如何获得summary数据呢？

首先，建立TensorFlow图，它是summary 数据的来源。之后我们需要在图中指定哪些nodes需要导出summary数据，方法是对指定的nodes添加一个tf.summary.scalar。需要说明的是，我们所创建的summary nodes只是图(graph)的外面的分支，TensorFlow中的其他操作并不依靠这部分summary nodes。另外根据TensorFlow的原理，只有图运转起来时才能生成summaries data，一个一个管理summaries data是非常枯燥麻烦的，所以可以使用 tf.summary.merge_all 把所有的summary汇总在一起。

通过上面这一步，我们已经可以生成一个序列化的Summary对象，为了把这些数据写到硬盘上，还需要借助 tf.summary.FileWriter 函数。

我们既可以每个周期都存一下 summary data，也可以N个周期存一下，这取决于我们对数据的需求。

下面举个例子。

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()

初始化好FileWriters后，我们还需要在训练过程中不断的把summaries写入FilesWriters

# 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)

等到训练结束，我们就可以用tensorboard查看训练中的summaries了。

4、加载TensorBoard

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

log-directory是FileWriter初始化时设定的目录

最后我们打开浏览器，输入 localhost:6006 就可以看到TensorBoard了。