TensorBoard Embedding Example?

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I'm looking for a tensorboard embedding example, with iris data for example like the embedding projector http://projector.tensorflow.org/

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

Does someone knows a basic tutorial for this functionality?

Basics:

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

embedding_var = tf.Variable(....)

2) Periodically save your embeddings in a LOG_DIR.

3) Associate metadata with your embedding.

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 how-to document.

I’ve uploaded my working solution with the MNIST dataset to my GitHub repo.

Yes, it is broken down into three general steps:

  1. Create metadata for each dimension.
  2. Associate images with each dimension.
  3. Load the data into TensorFlow and save the embeddings in a LOG_DIR.

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.

  1. Preparing the data from the source
  2. Loading the data into a tf.Variable

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 tf.stack 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.

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

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

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))

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 my GitHub.

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 my blog. Best of luck to you, please let me know how it goes. :)

Embedding projector, Publish your embedding visualization and data. If you'd like to share your visualization with the world, follow these simple steps. See this tutorial for more. Hence, in this TensorFlow Embedding tutorial, we saw what Embeddings in TensorFlow are and how to train an Embedding in TensorFlow. Along with this, we saw how one can view the Embeddings with TensorBoard Embedding Projector. Moreover, we saw the example of TensorFlow & TensorBoard embedding. Next up is debugging in TensorFlow. Furthermore, if

I've used FastText's pre-trained word vectors with TensorBoard.

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"))

Then run this command in your terminal:

tensorboard --logdir=log

Word embeddings, Embedding visualisation is a standard feature in Tensorboard. For this example the embeddings are extremely simple: they are the direct  The test data is embedded using the weights of the final dense layer, just before the classification head. This embedding can then be visualized using TensorBoard's Embedding Projector.

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

Sample code and slides for the talk can be found here https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial

Simple Introduction to Tensorboard Embedding Visualisation, Embedding in TensorFlow Tutorial:embedding projector in Tensorflow,​Tensorboard embedding example,TensorFlow embedding lookup  # Load the TensorBoard notebook extension %load_ext tensorboard import tensorflow as tf import datetime # Clear any logs from previous runs !rm -rf ./logs/ Using the MNIST dataset as the example, normalize the data and write a function that creates a simple Keras model for classifying the images into 10 classes.

An issue has been raised in the TensorFlow to GitHub repository: No real code example for using the tensorboard embedding tab #6322 (mirror).

It contains some interesting pointers.


If interested, some code that uses TensorBoard embeddings to display character and word embeddings: https://github.com/Franck-Dernoncourt/NeuroNER

Example:

FYI: How can I select which checkpoint to view in TensorBoard's embeddings tab?

Embedding in TensorFlow, I want a example code to use the embedding projector in tensorboard by tensorflow-2.0. #32879. Open. jiawei6636 opened this issue on Sep  For anyone struggling to get tensorboard embeddings working, I would suggest the standalone embeddings.It has example input files which were a massive help for me. For learning, it is much easier to create embedding data using the final testing code rather than the training code as suggested above.

To take pretrained embeddings and visualize it on tensorboard.

embedding -> trained embedding

metadata.tsv -> metadata information

max_size -> embedding.shape[0]

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 )

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

I want a example code to use the embedding projector in , Add tensorboard embedding visualization in my own web application #2078 Below links have some example usage of Embedding projector. The %tensorboard magic has exactly the same format as the TensorBoard command line invocation, but with a %-sign in front of it. You can also start TensorBoard before training to monitor it in progress: %tensorboard --logdir logs The same TensorBoard backend is reused by issuing the same command.

Add tensorboard embedding visualization in my own web , Embedding Visualization¶. In Tensorflow, data is represented by tensors in our graph. Tensors are representetives for high dimensional data. For example  You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. to refresh your session.

TB Embedding Visualization, This embedding can then be visualized using TensorBoard's Embedding 'test samples') # save class labels to disk to color data points in TensorBoard  An embedding is a dense vector of floating point values (the length of the vector is a parameter you specify). Instead of specifying the values for the embedding manually, they are trainable parameters (weights learned by the model during training, in the same way a model learns weights for a dense layer).

Tensorboard embeddings mnist, of a network. It must specify 3 arguments: It must specify 3 arguments: input_dim: This is the size of the vocabulary in the text data. For example, if your data is integer encoded to values between 0-10, then the size of the vocabulary would be 11 words. TensorBoard is able to read this file and give some insights of the model graph and its performance. Now let’s write a simple TensorFlow program and visualize its computational graph with TensorBoard. Example 1: Let’s create two constants and add them together. Constant tensors can be defined simply by their value:

Comments
  • Thanks @norman_h, i will check your code and come back :). I'm not working with images but with csv text for data classfication.
  • @Patrick then I guess you'll just leave out the lines that deal with the sprites and build your metadata.tsv slightly differently.
  • When I try to run tensorboard with your generated model, metadata etc. nothing shows in the GUI. It's just blank. I'm using TF 0.12.0-rc1. Are you missing the model_checkpoint_path in the projector_config.pbtxt file?
  • Upgrade to TensorFlow 1.0 or try an old commit that works with tf0.12.0 github.com/normanheckscher/mnist-tensorboard-embeddings/tree/…
  • Image is there. Link doesn't 404.
  • To complete the journey, install jupyter-tensorboard to call tensorboard directly from Jupyter Notebook.
  • Could you please provide the URL to the official tutorial?
  • There is no code at the above link.. a few gists...is all I am looking for a working example of Tensorboard embedding visualization with t-sne/PCA that works with TF 1.0 so far no luck..
  • Have updated the link to the source code to use github. Should be easier to navigate.
  • Corresponding Github response github.com/tensorflow/tensorflow/issues/…