在 TensorBoard 中显示文本数据

概述

使用 TensorFlow Text Summary API，您可以轻松地在 TensorBoard 中记录任意文本并进行查看。这在采样和检查输入数据，或在记录执行元数据或生成的文本方面非常实用。您还可以记录文本形式的诊断数据，这在模型开发过程中可能会有所帮助。

在本教程中，您将尝试 Text Summary API 的一些基本用例。

设置

try:
  # %tensorflow_version only exists in Colab.
  %tensorflow_version 2.x
except Exception:
  pass

# Load the TensorBoard notebook extension.
%load_ext tensorboard

import tensorflow as tf

from datetime import datetime
import json
from packaging import version
import tempfile

print("TensorFlow version: ", tf.__version__)
assert version.parse(tf.__version__).release[0] >= 2, \
    "This notebook requires TensorFlow 2.0 or above."

TensorFlow version:  2.5.0-dev20210219

记录一段文本

为了解 Text Summary API 的工作原理，现在您将在 TensorBoard 中简单记录一小段文本并查看其显示方式。

my_text = "Hello world! 😃"

# Clear out any prior log data.
!rm -rf logs

# Sets up a timestamped log directory.
logdir = "logs/text_basics/" + datetime.now().strftime("%Y%m%d-%H%M%S")
# Creates a file writer for the log directory.
file_writer = tf.summary.create_file_writer(logdir)

# Using the file writer, log the text.
with file_writer.as_default():
  tf.summary.text("first_text", my_text, step=0)

现在，使用 TensorBoard 检查文本。等待几秒，直至界面出现。

%tensorboard --logdir logs

组织多个文本流

如果有多个文本流，可以将它们保存在单独的名称空间中，从而便于对其进行组织，就像标量或其他数据一样。

请注意，如果您在多个步骤中记录文本，TensorBoard 将对要显示的步骤进行二次采样，以便演示内容易于管理。您可以使用 --samples_per_plugin 标志控制采样率。

# Sets up a second directory to not overwrite the first one.
logdir = "logs/multiple_texts/" + datetime.now().strftime("%Y%m%d-%H%M%S")
# Creates a file writer for the log directory.
file_writer = tf.summary.create_file_writer(logdir)

# Using the file writer, log the text.
with file_writer.as_default():
  with tf.name_scope("name_scope_1"):
    for step in range(20):
      tf.summary.text("a_stream_of_text", f"Hello from step {step}", step=step)
      tf.summary.text("another_stream_of_text", f"This can be kept separate {step}", step=step)
  with tf.name_scope("name_scope_2"):
    tf.summary.text("just_from_step_0", "This is an important announcement from step 0", step=0)

%tensorboard --logdir logs/multiple_texts --samples_per_plugin 'text=5'

Markdown 解释

TensorBoard 将文本摘要解释为 Markdown，因为多种格式可以使您记录的数据更易于阅读和理解，如下所示。（如果您不需要 Markdown 解释，请参见本议题，了解抑制解释的解决方法。）

# Sets up a third timestamped log directory under "logs"
logdir = "logs/markdown/" + datetime.now().strftime("%Y%m%d-%H%M%S")
# Creates a file writer for the log directory.
file_writer = tf.summary.create_file_writer(logdir)

some_obj_worth_noting = {
  "tfds_training_data": {
      "name": "mnist",
      "split": "train",
      "shuffle_files": "True",
  },
  "keras_optimizer": {
      "name": "Adagrad",
      "learning_rate": "0.001",
      "epsilon": 1e-07,
  },
  "hardware": "Cloud TPU",
}


# TODO: Update this example when TensorBoard is released with
# https://github.com/tensorflow/tensorboard/pull/4585
# which supports fenced codeblocks in Markdown.
def pretty_json(hp):
  json_hp = json.dumps(hp, indent=2)
  return "".join("\t" + line for line in json_hp.splitlines(True))

markdown_text = """
### Markdown Text

TensorBoard supports basic markdown syntax, including:

    preformatted code

**bold text**

| and | tables |
| ---- | ---------- |
| among | others |
"""

with file_writer.as_default():
  tf.summary.text("run_params", pretty_json(some_obj_worth_noting), step=0)
  tf.summary.text("markdown_jubiliee", markdown_text, step=0)

%tensorboard --logdir logs/markdown