TensorFlow学习笔记（6）：TensorBoard之Embeddings

本文基于TensorFlow官网的How-Tos写成。

TensorBoard是TensorFlow自带的一个可视化工具，Embeddings是其中的一个功能，用于在二维或三维空间对高维数据进行探索。

An embedding is a map from input data to points in Euclidean space.

本文使用MNIST数据讲解Embeddings的使用方法。

代码

# -*- coding: utf-8 -*-

# @author: 陈水平

# @date: 2017-02-08

# @description: hello world program to set up embedding projector in TensorBoard based on MNIST

# @ref: http://yann.lecun.com/exdb/mnist/, https://www.90168.org/images/mnist_10k_sprite.png

# 

import numpy as np

import tensorflow as tf

from tensorflow.contrib.tensorboard.plugins import projector

from tensorflow.examples.tutorials.mnist import input_data

import os

PATH_TO_MNIST_DATA = "MNIST_data"

LOG_DIR = "log"

IMAGE_NUM = 10000

# Read in MNIST data by utility functions provided by TensorFlow

mnist = input_data.read_data_sets(PATH_TO_MNIST_DATA, one_hot=False)

# Extract target MNIST image data

plot_array = mnist.test.images[:IMAGE_NUM]  # shape: (n_observations, n_features)

# Generate meta data

np.savetxt(os.path.join(LOG_DIR, 'metadata.tsv'),www.90168.org mnist.test.labels[:IMAGE_NUM], fmt='%d')

# Download sprite image

# https://www.tensorflow.org/images/mnist_10k_sprite.png, 100x100 thumbnails

PATH_TO_SPRITE_IMAGE = os.path.join(LOG_DIR, 'mnist_10k_sprite.png')  

# To visualise your embeddings, there are 3 things you need to do:

# 1) Setup a 2D tensor variable(s) that holds your embedding(s)

session = tf.InteractiveSession()

embedding_var = tf.Variable(plot_array, name='embedding')

tf.global_variables_initializer().run()

# 2) Periodically save your embeddings in a LOG_DIR

# Here we just save the Tensor once, so we set global_step to a fixed number

saver = tf.train.Saver()

saver.save(session, os.path.join(LOG_DIR, "model.ckpt"), global_step=0)

# 3) Associate metadata and sprite image with your embedding

# Use the same LOG_DIR where you stored your checkpoint.

summary_writer = tf.summary.FileWriter(LOG_DIR)

config = projector.ProjectorConfig()

# You can add multiple embeddings. Here we add only one.

embedding = config.embeddings.add()

embedding.tensor_name = embedding_var.name

# Link this tensor to its metadata file (e.g. labels).

embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv')

# Link this tensor to its sprite image.

embedding.sprite.image_path = PATH_TO_SPRITE_IMAGE

embedding.sprite.single_image_dim.extend([28, 28])

# Saves a configuration file that TensorBoard will read during startup.

projector.visualize_embeddings(summary_writer, config)

首先，从这里下载图片，放到log目录下；然后执行上述代码；最后，执行下面的命令启动TensorBoard。

tensorboard --logdir=log

执行后，命令行会显示如下提示信息：

Starting TensorBoard 39 on port 6006

(You can navigate to http://xx.xxx.xx.xxx:6006)

打开浏览器，输入上面的链接地址，点击导航栏的EMBEDDINGS即可看到效果：

巴特西

TensorFlow学习笔记（6）：TensorBoard之Embeddings

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