def search_model(experiment_label, steps, batch_size=32):
""" This is where we put everythin together.
We get the dataset, build the Training and Experiment objects, and run the experiment.
The experiments logs are generated in ~/minos/experiment_label
We use the CpuEnvironment to have the experiment run on the cpu, with 2 parralel processes.
We could use GpuEnvironment to use GPUs, and specify which GPUs to use, and how many tasks
per GPU
"""
batch_iterator, test_batch_iterator, nb_classes = get_reuters_dataset(
batch_size, max_words)
layout = build_layout(max_words, nb_classes)
training = Training(Objective('categorical_crossentropy'),
Optimizer(optimizer='Adam'),
Metric('categorical_accuracy'),
epoch_stopping_condition(), batch_size)
parameters = custom_experiment_parameters()
experiment = Experiment(experiment_label,
layout,
training,
batch_iterator,
test_batch_iterator,
CpuEnvironment(n_jobs=2),
parameters=parameters)
run_ga_search_experiment(experiment,
population_size=100,
generations=steps,
resume=False,
log_level='DEBUG')
def test_ga_search(self):
with tempfile.TemporaryDirectory() as tmp_dir:
epoch = 3
generations = 2
batch_size = 50
batch_iterator, test_batch_iterator, nb_classes = get_reuters_dataset(
batch_size, 1000)
layout = Layout(input_size=1000,
output_size=nb_classes,
output_activation='softmax')
training = Training(
objective=Objective('categorical_crossentropy'),
optimizer=Optimizer(optimizer='Adam'),
metric=Metric('categorical_accuracy'),
stopping=EpochStoppingCondition(epoch),
batch_size=batch_size)
experiment_parameters = ExperimentParameters(
use_default_values=False)
experiment_parameters.layout_parameter('rows', 1)
experiment_parameters.layout_parameter('blocks', 1)
experiment_parameters.layout_parameter('layers', 1)
experiment_parameters.layer_parameter('Dense.output_dim',
int_param(10, 500))
experiment_parameters.all_search_parameters(True)
experiment_label = 'test__reuters_experiment'
experiment = Experiment(experiment_label,
layout,
training,
batch_iterator,
test_batch_iterator,
CpuEnvironment(n_jobs=2, data_dir=tmp_dir),
parameters=experiment_parameters)
run_ga_search_experiment(experiment,
population_size=2,
generations=2)
self.assertTrue(isfile(experiment.get_log_filename()),
'Should have logged')
self.assertTrue(isfile(experiment.get_step_data_filename(0)),
'Should have logged')
self.assertTrue(isfile(experiment.get_step_log_filename(0)),
'Should have logged')
blueprints = load_experiment_blueprints(
experiment_label, 0, Environment(data_dir=tmp_dir))
self.assertTrue(
len(blueprints) > 0, 'Should have saved/loaded blueprints')
model = ModelBuilder().build(blueprints[0], cpu_device())
disable_sysout()
model.fit_generator(
generator=batch_iterator,
samples_per_epoch=batch_iterator.samples_per_epoch,
nb_epoch=5,
validation_data=test_batch_iterator,
nb_val_samples=test_batch_iterator.sample_count)
score = model.evaluate_generator(
test_batch_iterator,
val_samples=test_batch_iterator.sample_count)
self.assertTrue(score[1] > 0, 'Should have valid score')
step, population = load_experiment_checkpoint(experiment)
self.assertEqual(generations - 1, step,
'Should have loaded checkpoint')
self.assertIsNotNone(population, 'Should have loaded checkpoint')
blueprint = load_experiment_best_blueprint(
experiment.label,
environment=CpuEnvironment(n_jobs=2, data_dir=tmp_dir))
model = ModelBuilder().build(blueprint,
cpu_device(),
compile_model=False)
self.assertIsNotNone(
model,
'Should have loaded and built best model from experiment')