Tensorboard callback doesn't work when calling

Question

I am new to Tensorflow and Keras. I just started beginning my Deep learning Journey. I installed Tensorflow 2.4.3 as well as Keras. I was learning Tensorboard. I created a model for imdb dataset as follows

import tensorflow as tf
import keras
from tensorflow.keras import *
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing import sequence

## model making
max_features = 2000
max_len = 500
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
model = models.Sequential()
model.add(layers.Embedding(max_features, 128,
input_length=max_len,
name='embed'))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))
model.summary()
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])

I used the tensorboard callback here.

callbacks = [
keras.callbacks.TensorBoard(
log_dir='my_log_dir',
histogram_freq=1,
embeddings_freq=1,
)
]
history = model.fit(x_train, y_train,
epochs=3,
batch_size=128,
validation_split=0.2,
callbacks=callbacks)

Then I got the following warning.

C:\Users\ktripat\Anaconda3\envs\tf2\lib\site-packages\keras\callbacks\tensorboard_v2.py:102: UserWarning: The TensorBoard callback does not support embeddings display when using TensorFlow 2.0. Embeddings-related arguments are ignored.
  warnings.warn('The TensorBoard callback does not support.'

Please find any solution if you guys have any. Thank you in advance!

Answer 1

You will need to follow this guide. It describes how to save the weights of your embedding layer in a way that you can visualize it in TensorBoard:

# Set up a logs directory, so Tensorboard knows where to look for files.
log_dir='/logs/imdb-example/'
if not os.path.exists(log_dir):
    os.makedirs(log_dir)

# Save Labels separately on a line-by-line manner.
with open(os.path.join(log_dir, 'metadata.tsv'), "w") as f:
  for subwords in encoder.subwords:
    f.write("{}\n".format(subwords))
  # Fill in the rest of the labels with "unknown".
  for unknown in range(1, encoder.vocab_size - len(encoder.subwords)):
    f.write("unknown #{}\n".format(unknown))


# Save the weights we want to analyze as a variable. Note that the first
# value represents any unknown word, which is not in the metadata, here
# we will remove this value.
weights = tf.Variable(model.layers[0].get_weights()[0][1:])
# Create a checkpoint from embedding, the filename and key are the
# name of the tensor.
checkpoint = tf.train.Checkpoint(embedding=weights)
checkpoint.save(os.path.join(log_dir, "embedding.ckpt"))

# Set up config.
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
# The name of the tensor will be suffixed by `/.ATTRIBUTES/VARIABLE_VALUE`.
embedding.tensor_name = "embedding/.ATTRIBUTES/VARIABLE_VALUE"
embedding.metadata_path = 'metadata.tsv'
projector.visualize_embeddings(log_dir, config)

If you want to visualize during training, you can call this code in a save callback during training every X episodes using this.