Architecture tuning#

Often, the standard parameters are sufficient to get a performing model - see the Advice on architecture tuning.

However, sometimes “you want more”…

  • Given that training is often fast, you can manually run an exhaustive search by fitting models with different architectures.

  • We have also added experimental support for automatic architecture tuning via Keras Tuner for when the search space is too big for an exhaustive search. It is particularly useful to track the tuning on Weights & Biases - see the examples below and experiment tracking.

Manual tuning#

If the search space is small and training fast, it is often best to do an exhaustive search by fitting models with all parameter combinations. It is advisable to run repeated fits with the same combination to be robust to random effects.

import das.train
from itertools import product
from pprint import pprint

nb_convs = [2, 3, 4]
nb_filters = [32, 64, 96]
learning_rates = [0.0001, 0.00001]
repeats = list(range(4))

parameter_combinations = product(nb_convs, nb_filters, learning_rates, repeats)

results = []

for nb_conv, nb_filters, learning_rate, repeat in  parameter_combinations:
    model, params, fit_hist = das.train.train(data_dir='tutorial_dataset.npy', save_dir='res', nb_conv=nb_conv, nb_filters=nb_filters, learning_rate=learning_rate, nb_epoch=20, WANDB_ARGS)
    results.append({'nb_conv': nb_conv,
                    'learning_rate': learning_rate,
                    'training_loss': min(fit_hist.history['loss']),
                    'validation_loss': min(fit_hist.history['val_loss']),})

# plot results

During the training, the results can be tracked on

A model with A TCN blocks, B filters, and a learning rate of C yields the lowest validation loss.

Automatic architecture tuning#

The interface is similar to das.train: das.train_tune.train(data_dir='tutorial_data.npy', save_dir='res', kernel_size=3, tune_config='tune.yml'). There is also a CLI that you can access via das tune (see CLI documentation).

Crucially, it accepts a yaml file with the parameter names and a set of values you want the optimizer to try. For instance, if you want the optimizer to try models with

  • 2, 3, or 3 TCN blocks,

  • 32, 64, or 96 filters, and

  • learning rates of 0.0001 or 0.00001

then the tune.yml file should look like this:

nb_conv: [2, 3, 4]
nb_filters: [32, 64, 96]
learning_rate: [0.0001, 0.00001]

The tuner will then run a bunch of fits to find an optimal parameter combination in the search space defined in the yaml file - see keras tuner for how this works.

import das.train_tune

model, params, tuner = das.train_tune.train(

Will take N minutes, during which results will can be inspected at Link to wandb site for this project.