How to use the Embedding Projector in Tensorflow 2.0 - python-3.x

Question

With the tf.contrib module gone from Tensorflow, and with tf.train.Saver() also gone, I cannot find a way to store a set of embeddings and their corresponding thumbnails, so that the Tensorboard Projector can read them.
The Tensorboard documentation for Tensorflow 2.0 explains how to create plots and summaries, and how to use the summary tool in general, but nothing about the projector tool. Has anyone found how to store datasets for visualization?
If possible, I would appreciate a (minimal) code example.

Answer 1

It seems there are some issues left in tensorboard. However, there are some
workarounds (for now) for preparing embeddings for projector with tensorflow2:
(bug report at: https://github.com/tensorflow/tensorboard/issues/2471)
tensorflow1 code would look something like that:
embeddings = tf.compat.v1.Variable(latent_data, name='embeddings')
CHECKPOINT_FILE = TENSORBOARD_DIR + '/model.ckpt'
# Write summaries for tensorboard
with tf.compat.v1.Session() as sess:
saver = tf.compat.v1.train.Saver([embeddings])
sess.run(embeddings.initializer)
saver.save(sess, CHECKPOINT_FILE)
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embeddings.name
embedding.metadata_path = TENSORBOARD_METADATA_FILE
projector.visualize_embeddings(tf.summary.FileWriter(TENSORBOARD_DIR), config)
when using eager mode in tensorflow2 this should (?) look somehow like this:
embeddings = tf.Variable(latent_data, name='embeddings')
CHECKPOINT_FILE = TENSORBOARD_DIR + '/model.ckpt'
ckpt = tf.train.Checkpoint(embeddings=embeddings)
ckpt.save(CHECKPOINT_FILE)
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embeddings.name
embedding.metadata_path = TENSORBOARD_METADATA_FILE
writer = tf.summary.create_file_writer(TENSORBOARD_DIR)
projector.visualize_embeddings(writer, config)
however, there are 2 issues:
the writer created with tf.summary.create_file_writer does not have the function get_logdir() required by projector.visualize_embeddings, a simple workaround is to patch the visualize_embeddings function to take the logdir as parameter.
the checkpoint format has changed, when reading the checkpoint with load_checkpoint (which seems to be the tensorboard way of loading the file), the variable names change. e.g. embeddings changes to something like embeddings/.ATTRIBUTES/VARIABLE_VALUE (also there are additional variables in the map extracted by get_variable_to_shape_map()but they are empty anyways).
the second issue was solved with the following quick-and-dirty workaround (and logdir is now a parameter of visualize_embeddings())
embeddings = tf.Variable(latent_data, name='embeddings')
CHECKPOINT_FILE = TENSORBOARD_DIR + '/model.ckpt'
ckpt = tf.train.Checkpoint(embeddings=embeddings)
ckpt.save(CHECKPOINT_FILE)
reader = tf.train.load_checkpoint(TENSORBOARD_DIR)
map = reader.get_variable_to_shape_map()
key_to_use = ""
for key in map:
if "embeddings" in key:
key_to_use = key
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = key_to_use
embedding.metadata_path = TENSORBOARD_METADATA_FILE
writer = tf.summary.create_file_writer(TENSORBOARD_DIR)
projector.visualize_embeddings(writer, config,TENSORBOARD_DIR)
I did not find any examples on how to use tensorflow2 to directly write the embeddings for tensorboard, so I am not sure if this is the right way, but if it is, then those two issues would need to be addressed, and at least for now there is a workaround.

Answer 2

It seems plenty of people as myself are having problems using Tensorboard Projector in TF2.x due to the lack of documentation. I have managed to make it work and in this comment on GitHub I provide some minimal code examples. I know the questions was also about using thumbnails (sprites), but I did not need it and wanted to keep the examples simple, so making sprites work is left as an exercise to the reader.
# Some initial code which is the same for all the variants
import os
import numpy as np
import tensorflow as tf
from tensorboard.plugins import projector
def register_embedding(embedding_tensor_name, meta_data_fname, log_dir):
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embedding_tensor_name
embedding.metadata_path = meta_data_fname
projector.visualize_embeddings(log_dir, config)
def get_random_data(shape=(100,100)):
x = np.random.rand(*shape)
y = np.random.randint(low=0, high=2, size=shape[0])
return x, y
def save_labels_tsv(labels, filepath, log_dir):
with open(os.path.join(log_dir, filepath), 'w') as f:
for label in labels:
f.write('{}\n'.format(label))
LOG_DIR = 'tmp' # Tensorboard log dir
META_DATA_FNAME = 'meta.tsv' # Labels will be stored here
EMBEDDINGS_TENSOR_NAME = 'embeddings'
EMBEDDINGS_FPATH = os.path.join(LOG_DIR, EMBEDDINGS_TENSOR_NAME + '.ckpt')
STEP = 0
x, y = get_random_data((100,100))
register_embedding(EMBEDDINGS_TENSOR_NAME, META_DATA_FNAME, LOG_DIR)
save_labels_tsv(y, META_DATA_FNAME, LOG_DIR)
VARIANT A (Works in TF2.0 and TF2.1, but not in eager mode)
# Size of files created on disk: 163kB
tf.compat.v1.disable_eager_execution()
tensor_embeddings = tf.Variable(x, name=EMBEDDINGS_TENSOR_NAME)
sess = tf.compat.v1.InteractiveSession()
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver()
saver.save(sess, EMBEDDINGS_FPATH, STEP)
sess.close()
VARIANT B (Works in both TF2.0 and TF2.1 in Eager mode)
# Size of files created on disk: 80.5kB
tensor_embeddings = tf.Variable(x, name=EMBEDDINGS_TENSOR_NAME)
saver = tf.compat.v1.train.Saver([tensor_embeddings]) # Must pass list or dict
saver.save(sess=None, global_step=STEP, save_path=EMBEDDINGS_FPATH)
I want to thank other developers for bits of code from their Stack answers, GitHub comments or personal blog posts, which helped me to put these examples together. You are the real MVP.