Use TensorBoard with Keras Tuner

Question

I ran into an apparent circular dependency trying to use log data for TensorBoard during a hyper-parameter search done with Keras Tuner, for a model built with TF2. The typical

Answer 1

The keras tuner creates a subdir for each run (statement is probably version dependent).

I guess finding the right version mix is of importance.

Here is how it works for me, in jupyterlab.

prerequisite:

1. pip requirements

    keras-tuner==1.0.1
    tensorboard==2.1.1
    tensorflow==2.1.0
    Keras==2.2.4
    jupyterlab==1.1.4

(2.) jupyterlab installed, built and running [standard compile arguments: production:minimize]

Here is the actual code. First i define the log folder and the callback

# run parameter
log_dir = "logs/" + datetime.datetime.now().strftime("%m%d-%H%M")

# training meta
stop_callback = EarlyStopping(
    monitor='loss', patience=1, verbose=0, mode='auto')

hist_callback = tf.keras.callbacks.TensorBoard(
    log_dir=log_dir,
    histogram_freq=1,
    embeddings_freq=1,
    write_graph=True,
    update_freq='batch')

print("log_dir", log_dir)

Then i define my hypermodel, which i do not want to disclose. Afterwards i set up the hyper parameter search

from kerastuner.tuners import Hyperband

hypermodel = get_my_hpyermodel()

tuner = Hyperband(
    hypermodel
    max_epochs=40,
    objective='loss',
    executions_per_trial=5,
    directory=log_dir,
    project_name='test'
)

which i then execute

tuner.search(
    train_data,
    labels,
    epochs=10,
    validation_data=(val_data, val_labels),
    callbacks=[hist_callback],
    use_multiprocessing=True)

tuner.search_space_summary()

While the notebook with this code searches for adequate hyper parameters i control the loss in another notebook. Since tf V2 tensorboard can be called via a magic function

Cell 1

import tensorboard

Cell 2

%load_ext tensorboard

Cell 3

%tensorboard --logdir 'logs/'

Sitenote: Since i run jupyterlab in a docker container i have to specifiy the appropriate address and port for tensorboard and also forward this in the dockerfile.

The result is not really predictable for me... I did not understand yet, when i can expect histograms and distributions in tensorboard. Some runs the loading time seems really excessive... so have patience

Under scalars i find a list of the turns as follows

"logdir"/"model_has"/execution[iter]/[train/validation]

E.g. 0101-1010/bb7981e03d05b05106d8a35923353ec46570e4b6/execution0/train 0101-1010/bb7981e03d05b05106d8a35923353ec46570e4b6/execution0/validation