blocks|key|1362087|text|听起来，您想要在TensorBoard上运行the的可视化部分。正如您所描述的，Tensorflow的API只在如何记录中提供了基本的命令。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1362088|我已经用MNIST数据集将我的工作解决方案上传到我的GitHub回购.+|style|BOLD|1362089|是的，它分为三个一般步骤：|1362090|为每个维度创建元数据。|ordered-list-item|1362091|将图像与每个维度相关联。|1362092|将数据加载到TensorFlow中，并将嵌入保存到LOG_DIR中。|1362093|只有泛型细节被嵌入到TensorFlow+r0.12发行版中。在官方源代码中没有我所知道的完整代码示例。|1362094|我发现有两项涉及的任务在“如何”中没有被记录下来。|1362095|从源中准备数据|1362096|将数据加载到tf.Variable中|CODE|1362097|虽然TensorFlow是为使用GPU而设计的，但在这种情况下，我选择用CPU生成the可视化，因为这个过程占用的内存比我的MacBookPro+GPU所能访问的内存要多。TensorFlow包含了对MNIST数据集的API访问，所以我使用了它。MNIST数据是一个结构化的numpy数组。使用tf.stack函数可以将此数据集堆叠成张量列表，这些张量可以嵌入到可视化中。下面的代码包含了如何提取数据并设置TensorFlow嵌入变量。|1362098|with+tf.device("/cpu:0"):
++++embedding+=+tf.Variable(tf.stack(mnist.test.images[:FLAGS.max_steps],+axis=0),+trainable=False,+name='embedding')|code-block|syntax|javascript|1362099|创建元数据文件是通过对numpy数组的切片来完成的。|1362100|def+save_metadata(file):
++++with+open(file,+'w')+as+f:
++++++++for+i+in+range(FLAGS.max_steps):
++++++++++++c+=+np.nonzero(mnist.test.labels[::1])[1:][0][i]
++++++++++++f.write('{}\n'.format(c))|1362101|具有要与之关联的图像文件，如“如何-+to”中所述。我已经将前10,000个MNIST图像的png文件上载到我的GitHub。|1362102|到目前为止，TensorFlow对我来说工作得很好，它的计算速度快，文档完整，而且这个API在功能上似乎已经完成了我现在要做的任何事情。我期待着在未来的一年中用自定义数据集生成更多的可视化。这篇文章是从我的博客编辑的。祝你好运，请告诉我事情进展如何。:)|1362103|entityMap|0|LINK|mutability|MUTABLE|url|https://www.tensorflow.org/how_tos/embedding_viz/|1|https://github.com/normanheckscher/mnist-tensorboard-embeddings/blob/master/mnist_t-sne.py|2|3|http://www.normansoft.com/blog/embedding-tensorboard-visua.html^0|1K|4|0|0|Y|1|O|A|1|0|0|D|0|0|B|0|0|C|0|0|Y|0|0|1G|0|0|P|0|0|7|0|0|I|6|B|0|0|62|43|8|0|0|0|Q|0|0|0|1R|1I|8|2|0|0|3J|2T|4|3|0^^$0|@$1|2|3|4|5|6|7|1R|8|@]|9|@$A|1S|B|1T|1|1U]]|C|$]]|$1|D|3|E|5|6|7|1V|8|@$A|1W|B|1X|F|G]]|9|@$A|1Y|B|1Z|1|20]]|C|$]]|$1|H|3|I|5|6|7|21|8|@$A|22|B|23|F|G]]|9|@]|C|$]]|$1|J|3|K|5|L|7|24|8|@$A|25|B|26|F|G]]|9|@]|C|$]]|$1|M|3|N|5|L|7|27|8|@$A|28|B|29|F|G]]|9|@]|C|$]]|$1|O|3|P|5|L|7|2A|8|@$A|2B|B|2C|F|G]]|9|@]|C|$]]|$1|Q|3|R|5|6|7|2D|8|@$A|2E|B|2F|F|G]]|9|@]|C|$]]|$1|S|3|T|5|6|7|2G|8|@$A|2H|B|2I|F|G]]|9|@]|C|$]]|$1|U|3|V|5|L|7|2J|8|@$A|2K|B|2L|F|G]]|9|@]|C|$]]|$1|W|3|X|5|L|7|2M|8|@$A|2N|B|2O|F|G]|$A|2P|B|2Q|F|Y]]|9|@]|C|$]]|$1|Z|3|10|5|6|7|2R|8|@$A|2S|B|2T|F|G]|$A|2U|B|2V|F|Y]]|9|@]|C|$]]|$1|11|3|12|5|13|7|2W|8|@]|9|@]|C|$14|15]]|$1|16|3|17|5|6|7|2X|8|@$A|2Y|B|2Z|F|G]]|9|@]|C|$]]|$1|18|3|19|5|13|7|30|8|@]|9|@]|C|$14|15]]|$1|1A|3|1B|5|6|7|31|8|@$A|32|B|33|F|G]]|9|@$A|34|B|35|1|36]]|C|$]]|$1|1C|3|1D|5|6|7|37|8|@$A|38|B|39|F|G]]|9|@$A|3A|B|3B|1|3C]]|C|$]]|$1|1E|3|-4|5|6|7|3D|8|@]|9|@]|C|$]]]|1F|$1G|$5|1H|1I|1J|C|$1K|1L]]|1M|$5|1H|1I|1J|C|$1K|1N]]|1O|$5|1H|1I|1J|C|$1K|1N]]|1P|$5|1H|1I|1J|C|$1K|1Q]]]]

It sounds like you want to get the Visualization section with t-SNE running on TensorBoard. As you've described, the API of Tensorflow has only provided the bare essential commands in the <a href="https://www.tensorflow.org/how_tos/embedding_viz/" rel="nofollow noreferrer">how-to document</a>.

I’ve uploaded my working solution with the MNIST dataset to <a href="https://github.com/normanheckscher/mnist-tensorboard-embeddings/blob/master/mnist_t-sne.py" rel="nofollow noreferrer">my GitHub repo</a>.

Yes, it is broken down into three general steps:

<ol>
<li>Create metadata for each dimension.</li>
<li>Associate images with each dimension.</li>
<li>Load the data into TensorFlow and save the embeddings in a LOG_DIR. </li>
</ol>

Only generic details are inculded with the TensorFlow r0.12 release. There is no full code example that I’m aware of within the official source code. 

I found that there were two tasks involved that were not documented in the how to.

<ol>
<li>Preparing the data from the source</li>
<li>Loading the data into a <code>tf.Variable</code></li>
</ol>

While TensorFlow is designed for the use of GPUs, in this situation I opted to generate the t-SNE visualization with the CPU as the process took up more memory than my MacBookPro GPU has access to. API access to the MNIST dataset is included with TensorFlow, so I used that. The MNIST data comes as a structured a numpy array. Using the <code>tf.stack</code> function enables this dataset to be stacked into a list of tensors which can be embedded into a visualization. The following code contains is how I extracted the data and setup the TensorFlow embedding variable. 

<pre><code>with tf.device("/cpu:0"):
 embedding = tf.Variable(tf.stack(mnist.test.images[:FLAGS.max_steps], axis=0), trainable=False, name='embedding')
</code></pre>

Creating the metadata file was perfomed with the slicing of a numpy array.

<pre><code>def save_metadata(file):
 with open(file, 'w') as f:
 for i in range(FLAGS.max_steps):
 c = np.nonzero(mnist.test.labels[::1])[1:][0][i]
 f.write('{}\n'.format(c))
</code></pre>

Having an image file to associate with is as described in the how-to. I've uploaded a png file of the first 10,000 MNIST images to <a href="https://github.com/normanheckscher/mnist-tensorboard-embeddings/blob/master/mnist_t-sne.py" rel="nofollow noreferrer">my GitHub</a>.

So far TensorFlow works beautifully for me, it’s computationaly quick, well documented and the API appears to be functionally complete for anything I am about to do for the moment. I look forward to generating some more visualizations with custom datasets over the coming year. This post was edited from <a href="http://www.normansoft.com/blog/embedding-tensorboard-visua.html" rel="nofollow noreferrer">my blog</a>. Best of luck to you, please let me know how it goes. :)

blocks|key|344700|text|看看这个“动手TensorBoard+(2017年TensorFlow+Dev首脑会议)”https://www.youtube.com/watch?v=eBbEDRsCmv4，它演示了在MNIST数据集中嵌入TensorBoard。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|344701|在这里可以找到对话的示例代码和幻灯片，https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial|344702|entityMap|0|LINK|mutability|MUTABLE|url|https://www.youtube.com/watch?v=eBbEDRsCmv4|1|https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial^0|19|17|0|0|J|1R|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

Check out this talk "Hands-on TensorBoard (TensorFlow Dev Summit 2017)" <a href="https://www.youtube.com/watch?v=eBbEDRsCmv4" rel="nofollow noreferrer">https://www.youtube.com/watch?v=eBbEDRsCmv4</a> It demonstrates TensorBoard embedding on the MNIST dataset.

Sample code and slides for the talk can be found here <a href="https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial" rel="nofollow noreferrer">https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial</a>

blocks|key|1163608|text|我用过FastText的预训练字向量和TensorBoard。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1163609|import+os
import+tensorflow+as+tf
import+numpy+as+np
import+fasttext
from+tensorflow.contrib.tensorboard.plugins+import+projector

#+load+model
word2vec+=+fasttext.load_model('wiki.en.bin')

#+create+a+list+of+vectors
embedding+=+np.empty((len(word2vec.words),+word2vec.dim),+dtype=np.float32)
for+i,+word+in+enumerate(word2vec.words):
++++embedding[i]+=+word2vec[word]

#+setup+a+TensorFlow+session
tf.reset_default_graph()
sess+=+tf.InteractiveSession()
X+=+tf.Variable([0.0],+name='embedding')
place+=+tf.placeholder(tf.float32,+shape=embedding.shape)
set_x+=+tf.assign(X,+place,+validate_shape=False)
sess.run(tf.global_variables_initializer())
sess.run(set_x,+feed_dict={place:+embedding})

#+write+labels
with+open('log/metadata.tsv',+'w')+as+f:
++++for+word+in+word2vec.words:
++++++++f.write(word+%2B+'\n')

#+create+a+TensorFlow+summary+writer
summary_writer+=+tf.summary.FileWriter('log',+sess.graph)
config+=+projector.ProjectorConfig()
embedding_conf+=+config.embeddings.add()
embedding_conf.tensor_name+=+'embedding:0'
embedding_conf.metadata_path+=+os.path.join('log',+'metadata.tsv')
projector.visualize_embeddings(summary_writer,+config)

#+save+the+model
saver+=+tf.train.Saver()
saver.save(sess,+os.path.join('log',+"model.ckpt"))|code-block|syntax|javascript|1163610|然后在终端中运行以下命令：|1163611|tensorboard+--logdir=log|1163612|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/facebookresearch/fastText/^0|3|F|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|10|8|@]|9|@]|C|$G|H]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]]]

I've used <a href="https://github.com/facebookresearch/fastText/" rel="nofollow noreferrer">FastText's pre-trained word vectors</a> with TensorBoard.

<pre class="lang-py prettyprint-override"><code>import os
import tensorflow as tf
import numpy as np
import fasttext
from tensorflow.contrib.tensorboard.plugins import projector

# load model
word2vec = fasttext.load_model('wiki.en.bin')

# create a list of vectors
embedding = np.empty((len(word2vec.words), word2vec.dim), dtype=np.float32)
for i, word in enumerate(word2vec.words):
 embedding[i] = word2vec[word]

# setup a TensorFlow session
tf.reset_default_graph()
sess = tf.InteractiveSession()
X = tf.Variable([0.0], name='embedding')
place = tf.placeholder(tf.float32, shape=embedding.shape)
set_x = tf.assign(X, place, validate_shape=False)
sess.run(tf.global_variables_initializer())
sess.run(set_x, feed_dict={place: embedding})

# write labels
with open('log/metadata.tsv', 'w') as f:
 for word in word2vec.words:
 f.write(word + '\n')

# create a TensorFlow summary writer
summary_writer = tf.summary.FileWriter('log', sess.graph)
config = projector.ProjectorConfig()
embedding_conf = config.embeddings.add()
embedding_conf.tensor_name = 'embedding:0'
embedding_conf.metadata_path = os.path.join('log', 'metadata.tsv')
projector.visualize_embeddings(summary_writer, config)

# save the model
saver = tf.train.Saver()
saver.save(sess, os.path.join('log', "model.ckpt"))
</code></pre>

Then run this command in your terminal:

<pre><code>tensorboard --logdir=log
</code></pre>

blocks|key|343572|text|TensorFlow+to+GitHub存储库中提出了一个问题：没有使用tensorboard嵌入选项卡#6322的真正代码示例+(镜像)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|343573|它包含了一些有趣的指针。|343574|如果感兴趣，一些使用TensorBoard嵌入来显示字符和单词嵌入的代码：https://github.com/Franck-Dernoncourt/NeuroNER|343575|示例：|343576|​|343577|📷|atomic|343578|343579|343580|343581|343582|金融时报：如何在TensorBoard的“嵌入”选项卡中选择要查看的检查点？|343583|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/tensorflow/tensorflow/issues/6322|1|https://web.archive.org/web/20170308181419/https://github.com/tensorflow/tensorflow/issues/6322|2|https://github.com/Franck-Dernoncourt/NeuroNER|3|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/475da68f3c14ee31ce037a830286e7d1.jpg|imageAlt|4|https://developer.qcloudimg.com/http-save/yehe-900000/7d0712bba68fa8ac191c3d4eb5436da7.png|5|https://stackoverflow.com/q/42679552/395857^0|W|W|0|1U|2|1|0|0|11|1A|2|0|0|0|0|1|3|0|0|0|0|1|4|0|0|5|X|5|0^^$0|@$1|2|3|4|5|6|7|1G|8|@]|9|@$A|1H|B|1I|1|1J]|$A|1K|B|1L|1|1M]]|C|$]]|$1|D|3|E|5|6|7|1N|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|1O|8|@]|9|@$A|1P|B|1Q|1|1R]]|C|$]]|$1|H|3|I|5|6|7|1S|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|1T|8|@]|9|@]|C|$]]|$1|L|3|M|5|N|7|1U|8|@]|9|@$A|1V|B|1W|1|1X]]|C|$]]|$1|O|3|K|5|6|7|1Y|8|@]|9|@]|C|$]]|$1|P|3|K|5|6|7|1Z|8|@]|9|@]|C|$]]|$1|Q|3|M|5|N|7|20|8|@]|9|@$A|21|B|22|1|23]]|C|$]]|$1|R|3|K|5|6|7|24|8|@]|9|@]|C|$]]|$1|S|3|T|5|6|7|25|8|@]|9|@$A|26|B|27|1|28]]|C|$]]|$1|U|3|-4|5|6|7|29|8|@]|9|@]|C|$]]]|V|$W|$5|X|Y|Z|C|$10|11]]|12|$5|X|Y|Z|C|$10|13]]|14|$5|X|Y|Z|C|$10|15]]|16|$5|17|Y|18|C|$19|1A|1B|-4]]|1C|$5|17|Y|18|C|$19|1D|1B|-4]]|1E|$5|X|Y|Z|C|$10|1F]]]]

An issue has been raised in the TensorFlow to GitHub repository: <a href="https://github.com/tensorflow/tensorflow/issues/6322" rel="nofollow noreferrer">No real code example for using the tensorboard embedding tab #6322</a> (<a href="https://web.archive.org/web/20170308181419/https://github.com/tensorflow/tensorflow/issues/6322" rel="nofollow noreferrer">mirror</a>).

It contains some interesting pointers.

<hr>

If interested, some code that uses TensorBoard embeddings to display character and word embeddings:
<a href="https://github.com/Franck-Dernoncourt/NeuroNER" rel="nofollow noreferrer">https://github.com/Franck-Dernoncourt/NeuroNER</a>

Example:

<a href="https://i.stack.imgur.com/SLXk1.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/SLXk1.jpg" alt="enter image description here"></a>

<a href="https://i.stack.imgur.com/U5y9j.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/U5y9j.png" alt="enter image description here"></a>

FYI: <a href="https://stackoverflow.com/q/42679552/395857">How can I select which checkpoint to view in TensorBoard's embeddings tab?</a>

blocks|key|1362320|text|接受的答案非常有助于理解总的顺序：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1362321|为每个向量创建元数据(示例)|ordered-list-item|1362322|将图像(精灵)与每个矢量相关联|1362323|将数据加载到TensorFlow中，并使用检查点和摘要编写器保存嵌入(请记住，在整个过程中路径是一致的)。|1362324|对我来说，基于MNIST的例子仍然过于依赖于预先训练过的数据和预先生成的sprite和元数据文件。为了填补这一空白，我自己创建了一个示例，并决定在这里为感兴趣的人共享它-代码在GitHub上。|offset|length|1362325|​|1362326|📷|atomic|1362327|1362328|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/altermarkive/tensorflow-embeddings-minimalistic-example|1|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/c285f1e86fe0d26c151a3be27d482d43.png|imageAlt^0|0|0|0|0|2G|6|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|17|8|@]|9|@]|A|$]]|$1|E|3|F|5|D|7|18|8|@]|9|@]|A|$]]|$1|G|3|H|5|D|7|19|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|1A|8|@]|9|@$K|1B|L|1C|1|1D]]|A|$]]|$1|M|3|N|5|6|7|1E|8|@]|9|@]|A|$]]|$1|O|3|P|5|Q|7|1F|8|@]|9|@$K|1G|L|1H|1|1I]]|A|$]]|$1|R|3|N|5|6|7|1J|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1K|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]|10|$5|11|W|12|A|$13|14|15|-4]]]]

The accepted answer was very helpful to understand the general sequence:

<ol>
<li>Create metadata for each vector (sample)</li>
<li>Associate images (sprites) with each vector </li>
<li>Load the data into TensorFlow and save the embeddings using checkpoint and summary writer (mind that the paths are consistent throughout the process).</li>
</ol>

For me, the MNIST-based example still relied too much on pre-trained data and pre-generated sprite &amp; metadata files. To fill this gap I created such an example myself and decided to share it here for anyone interested - the code is on <a href="https://github.com/altermarkive/tensorflow-embeddings-minimalistic-example" rel="nofollow noreferrer">GitHub</a>.

<a href="https://i.stack.imgur.com/i77UL.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/i77UL.png" alt="enter image description here"></a>

blocks|key|1362361|text|进行预先训练的嵌入，并将其可视化在拉伸板上。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1362362|嵌入->训练嵌入|1362363|metadata.tsv+->元数据信息|1362364|max_size+->+embedding.shape|1362365|import+tensorflow+as+tf
from+tensorflow.contrib.tensorboard.plugins+import+projector

sess+=+tf.InteractiveSession()

with+tf.device("/cpu:0"):
++++tf_embedding+=+tf.Variable(embedding,+trainable+=+False,+name+=+"embedding")

tf.global_variables_initializer().run()
path+=+"tensorboard"
saver+=+tf.train.Saver()
writer+=+tf.summary.FileWriter(path,+sess.graph)
config+=+projector.ProjectorConfig()
embed+=+config.embeddings.add()
embed.tensor_name+=+"embedding"
embed.metadata_path+=+"metadata.tsv"
projector.visualize_embeddings(writer,+config)
saver.save(sess,+path%2B'/model.ckpt'+,+global_step=max_size+)|code-block|syntax|javascript|1362366|$+tensorboard+--logdir="tensorboard“--port=8080|1362367|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|R|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|S|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|T|8|@]|9|@]|A|$]]|$1|H|3|I|5|J|7|U|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|V|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

To take pretrained embeddings and visualize it on tensorboard.

embedding -> trained embedding

metadata.tsv -> metadata information

max_size -> embedding.shape[0]

<pre><code>import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

sess = tf.InteractiveSession()

with tf.device("/cpu:0"):
 tf_embedding = tf.Variable(embedding, trainable = False, name = "embedding")

tf.global_variables_initializer().run()
path = "tensorboard"
saver = tf.train.Saver()
writer = tf.summary.FileWriter(path, sess.graph)
config = projector.ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = "embedding"
embed.metadata_path = "metadata.tsv"
projector.visualize_embeddings(writer, config)
saver.save(sess, path+'/model.ckpt' , global_step=max_size )
</code></pre>

$ tensorboard --logdir="tensorboard" --port=8080

blocks|key|344832|text|这是一个官方指南的链接。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|344833|维兹|offset|length|344834|它说这是最近一次更新的2017年6月。|344835|entityMap|0|LINK|mutability|MUTABLE|url|https://www.tensorflow.org/versions/r1.1/get_started/embedding_viz^0|0|0|2|0|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@]|9|@$D|R|E|S|1|T]]|A|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|O]]]]

Here's a link to an official guide. 

<a href="https://www.tensorflow.org/versions/r1.1/get_started/embedding_viz" rel="nofollow noreferrer">https://www.tensorflow.org/versions/r1.1/get_started/embedding_viz</a>

It says it was last updated June 2017.

I'm looking for a tensorboard embedding example, with iris data for example like the embedding projector <a href="http://projector.tensorflow.org/" rel="noreferrer">http://projector.tensorflow.org/</a>

But unfortunately i couldn't find one. Just a little bit information about how to do it in <a href="https://www.tensorflow.org/how_tos/embedding_viz/" rel="noreferrer">https://www.tensorflow.org/how_tos/embedding_viz/</a>

Does someone knows a basic tutorial for this functionality?

Basics:

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

<pre><code>embedding_var = tf.Variable(....)
</code></pre>

2) Periodically save your embeddings in a LOG_DIR.

3) Associate metadata with your embedding.

TensorBoard Embedding Example?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在寻找一个有虹膜数据的张拉板嵌入示例，比如嵌入投影仪但不幸的是我找不到。关于如何在中做这件事的一些信息。有人知道这个功能的基本教程吗？基本要素：1)设置一个2D张量变量，以容纳嵌入。embedding_var = tf.Variable(....)2)定期将嵌入保存在LOG_DIR中。3)将元数据与嵌入相关联。

问TensorBoard嵌入示例？
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