def test_train(self):
n_jobs = 2
with Pool(n_jobs) as pool,\
tempfile.TemporaryDirectory() as tmp_dir:
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(10),
batch_size=batch_size)
experiment_parameters = ExperimentParameters(
use_default_values=True)
experiment_parameters.layout_parameter('rows', 1)
experiment_parameters.layout_parameter('blocks', 1)
experiment_parameters.layout_parameter('layers', 1)
experiment = Experiment('test__reuters_experiment',
layout,
training,
batch_iterator,
test_batch_iterator,
CpuEnvironment(n_jobs=1, data_dir=tmp_dir),
parameters=experiment_parameters)
blueprints = [
create_random_blueprint(experiment) for _ in range(n_jobs)
]
pool.map(multiprocess_fit, blueprints)
def custom_experiment_parameters():
""" Here we define the experiment parameters.
We are using use_default_values=True, which will initialize
all the parameters with their default values. These parameters are then fixed
for the duration of the experiment and won't evolve.
That means that we need to manually specify which parametres we want to test,
and the possible values, either intervals or lists of values.
If we want to test all the parameters and possible values, we can
set use_default_values to False. In that case, random values will be generated
and tested during the experiment. We can redefine some parameters if we want to
fix their values.
Reference parameters and default values are defined in minos.model.parameters
We set the rows, blocks and layers parameters to 1 as we have specified a fixed layout.
We also set the 'layout' search parameter to False to disable the layout search
"""
experiment_parameters = ExperimentParameters(use_default_values=True)
experiment_parameters.search_parameter('layout', False)
experiment_parameters.layout_parameter('blocks', 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.layer_parameter('Dense.activation',
string_param(['relu', 'tanh']))
experiment_parameters.layer_parameter('Dropout.p', float_param(0.1, 0.9))
return experiment_parameters
def test_mutate_w_custom_definitions(self):
def custom_activation(x):
return x
register_custom_activation('custom_activation', custom_activation)
register_custom_layer('Dense2', Dense, deepcopy(reference_parameters['layers']['Dense']))
layout = Layout(
input_size=100,
output_size=10,
output_activation='softmax',
block=['Dense', 'Dense2'])
training = Training(
objective=Objective('categorical_crossentropy'),
optimizer=None,
metric=Metric('categorical_accuracy'),
stopping=EpochStoppingCondition(5),
batch_size=250)
experiment_parameters = ExperimentParameters(use_default_values=False)
experiment_parameters.layout_parameter('blocks', int_param(1, 5))
experiment_parameters.layout_parameter('layers', int_param(1, 5))
experiment_parameters.layer_parameter('Dense2.output_dim', int_param(10, 500))
experiment_parameters.layer_parameter('Dropout.p', float_param(0.1, 0.9))
experiment_parameters.all_search_parameters(True)
experiment = Experiment(
'test',
layout,
training,
batch_iterator=None,
test_batch_iterator=None,
environment=None,
parameters=experiment_parameters)
check_experiment_parameters(experiment)
for _ in range(10):
blueprint = create_random_blueprint(experiment)
mutant = mutate_blueprint(
blueprint,
parameters=experiment.parameters,
p_mutate_layout=0,
p_mutate_param=1,
mutate_in_place=False)
for row_idx, row in enumerate(mutant.layout.rows):
for block_idx, block in enumerate(row.blocks):
for layer_idx, layer in enumerate(block.layers):
original_row = blueprint.layout.rows[row_idx]
original_block = original_row.blocks[block_idx]
original_layer = original_block.layers[layer_idx]
for name, value in layer.parameters.items():
self.assertTrue(
value != original_layer.parameters[name],
'Should have mutated parameter')
def test_early_stopping_condition_test(self):
disable_sysout()
with tempfile.TemporaryDirectory() as tmp_dir:
batch_size = 50
min_epoch = 10
max_epoch = 15
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=AccuracyDecreaseStoppingCondition(
metric='categorical_accuracy',
noprogress_count=2,
min_epoch=min_epoch,
max_epoch=max_epoch),
batch_size=batch_size)
experiment_parameters = ExperimentParameters(
use_default_values=True)
experiment_parameters.layout_parameter('rows', 1)
experiment_parameters.layout_parameter('blocks', 1)
experiment_parameters.layout_parameter('layers', 1)
experiment = Experiment('test__reuters_experiment',
layout,
training,
batch_iterator,
test_batch_iterator,
CpuEnvironment(n_jobs=1, data_dir=tmp_dir),
parameters=experiment_parameters)
_assert_valid_training_parameters(experiment)
blueprint = create_random_blueprint(experiment)
trainer = ModelTrainer(batch_iterator, test_batch_iterator)
model, history, _duration = trainer.train(blueprint,
cpu_device(),
save_best_model=False)
self.assertTrue(
len(history.epoch) >= min_epoch,
'Should have trained for at least min epoch')
self.assertTrue(
len(history.epoch) <= max_epoch,
'Should have trained for max epoch')
self.assertIsNotNone(model, 'should have fit the model')
score = model.evaluate_generator(
test_batch_iterator,
val_samples=test_batch_iterator.sample_count)
self.assertIsNotNone(score, 'should have evaluated the model')
def test_custom_parameters(self):
experiment_parameters = ExperimentParameters()
experiment_parameters.layout_parameter('blocks', int_param(1, 10))
param = experiment_parameters.get_layout_parameter('blocks')
self.assertTrue(
1 == param.lo and 10 == param.hi,
'Should have set values')
experiment_parameters.layout_parameter('layers', int_param(1, 3))
param = experiment_parameters.get_layout_parameter('layers')
self.assertTrue(
1 == param.lo and 3 == param.hi,
'Should have set values')
experiment_parameters.layer_parameter('Dense.activation', string_param(['relu', 'tanh']))
param = experiment_parameters.get_layer_parameter('Dense.activation')
self.assertTrue(
'relu' == param.values[0] and 'tanh' == param.values[1],
'Should have set values')
def test_build(self):
layout = Layout(input_size=100,
output_size=10,
output_activation='softmax')
training = Training(objective=Objective('categorical_crossentropy'),
optimizer=None,
metric=Metric('categorical_accuracy'),
stopping=EpochStoppingCondition(10),
batch_size=250)
experiment_parameters = ExperimentParameters(use_default_values=False)
experiment_parameters.layout_parameter('blocks', int_param(1, 5))
experiment_parameters.layout_parameter('layers', int_param(1, 5))
experiment_parameters.layer_parameter('Dense.output_dim',
int_param(10, 500))
experiment_parameters.layer_parameter('Dense.activation',
string_param(['relu', 'tanh']))
experiment_parameters.layer_parameter('Dropout.p',
float_param(0.1, 0.9))
experiment_parameters.all_search_parameters(True)
experiment = Experiment('test',
layout,
training,
batch_iterator=None,
test_batch_iterator=None,
environment=None,
parameters=experiment_parameters)
check_experiment_parameters(experiment)
for _ in range(5):
blueprint1 = create_random_blueprint(experiment)
model = ModelBuilder().build(blueprint1, cpu_device())
self.assertIsNotNone(model, 'Should have built a model')
blueprint2 = create_random_blueprint(experiment)
model = ModelBuilder().build(blueprint2, cpu_device())
self.assertIsNotNone(model, 'Should have built a model')
blueprint3 = mix_blueprints(blueprint1, blueprint2,
experiment_parameters)
model = ModelBuilder().build(blueprint3, cpu_device())
self.assertIsNotNone(model, 'Should have built a model')
blueprint4 = mutate_blueprint(blueprint1,
experiment_parameters,
mutate_in_place=False)
model = ModelBuilder().build(blueprint4, cpu_device())
self.assertIsNotNone(model, 'Should have built a model')
def test_train(self):
disable_sysout()
with tempfile.TemporaryDirectory() as tmp_dir:
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(10),
batch_size=batch_size)
experiment_parameters = ExperimentParameters(
use_default_values=True)
experiment_parameters.layout_parameter('rows', 1)
experiment_parameters.layout_parameter('blocks', 1)
experiment_parameters.layout_parameter('layers', 1)
experiment = Experiment('test__reuters_experiment',
layout,
training,
batch_iterator,
test_batch_iterator,
CpuEnvironment(n_jobs=1, data_dir=tmp_dir),
parameters=experiment_parameters)
blueprint = create_random_blueprint(experiment)
model = ModelBuilder().build(blueprint, default_device())
result = model.fit_generator(
generator=batch_iterator,
samples_per_epoch=batch_iterator.samples_per_epoch,
nb_epoch=10,
validation_data=test_batch_iterator,
nb_val_samples=test_batch_iterator.sample_count)
self.assertIsNotNone(result, 'should have fit the model')
score = model.evaluate_generator(
test_batch_iterator,
val_samples=test_batch_iterator.sample_count)
self.assertIsNotNone(score, 'should have evaluated the model')
def test_model_trainer(self):
disable_sysout()
with tempfile.TemporaryDirectory() as tmp_dir:
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(10),
batch_size=batch_size)
experiment_parameters = ExperimentParameters(
use_default_values=True)
experiment_parameters.layout_parameter('rows', 1)
experiment_parameters.layout_parameter('blocks', 1)
experiment_parameters.layout_parameter('layers', 1)
experiment = Experiment('test__reuters_experiment',
layout,
training,
batch_iterator,
test_batch_iterator,
CpuEnvironment(n_jobs=1, data_dir=tmp_dir),
parameters=experiment_parameters)
blueprint = create_random_blueprint(experiment)
trainer = ModelTrainer(batch_iterator, test_batch_iterator)
model_filename = join(tmp_dir, 'model')
model, history, _duration = trainer.train(
blueprint,
cpu_device(),
save_best_model=True,
model_filename=model_filename)
self.assertIsNotNone(model, 'should have fit the model')
self.assertTrue(isfile(model_filename),
'Should have saved the model')
self.assertIsNotNone(history, 'Should have the training history')
def test_save(self):
disable_sysout()
def custom_activation(x):
return x
register_custom_activation('custom_activation', custom_activation)
register_custom_layer('custom_layer', CustomLayer,
{'output_dim': int_param(1, 100)})
with tempfile.TemporaryDirectory() as tmp_dir:
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(10),
batch_size=batch_size)
experiment_parameters = ExperimentParameters(
use_default_values=True)
experiment_parameters.layout_parameter('rows', 1)
experiment_parameters.layout_parameter('blocks', 1)
experiment_parameters.layout_parameter('layers', 1)
experiment_parameters.layout_parameter('block.layer_type',
'custom_layer')
experiment = Experiment('test__reuters_experiment',
layout,
training,
batch_iterator,
test_batch_iterator,
CpuEnvironment(n_jobs=1, data_dir=tmp_dir),
parameters=experiment_parameters)
blueprint = create_random_blueprint(experiment)
model = ModelBuilder().build(blueprint, default_device())
model.fit_generator(
generator=batch_iterator,
samples_per_epoch=batch_iterator.samples_per_epoch,
nb_epoch=10,
validation_data=test_batch_iterator,
nb_val_samples=test_batch_iterator.sample_count)
filepath = join(tmp_dir, 'model')
model.save(filepath)
model = load_keras_model(filepath)
self.assertIsNotNone(model, 'Should have loaded the model')
def test_build_w_custom_definitions(self):
def custom_activation(x):
return x
register_custom_activation('custom_activation', custom_activation)
register_custom_layer(
'Dense2', Dense, deepcopy(reference_parameters['layers']['Dense']),
True)
layout = Layout(input_size=100,
output_size=10,
output_activation='softmax',
block=['Dense2'])
training = Training(objective=Objective('categorical_crossentropy'),
optimizer=None,
metric=Metric('categorical_accuracy'),
stopping=EpochStoppingCondition(5),
batch_size=250)
experiment_parameters = ExperimentParameters(use_default_values=False)
experiment_parameters.layout_parameter('blocks', int_param(1, 5))
experiment_parameters.layout_parameter('layers', int_param(1, 5))
experiment_parameters.layout_parameter('layer.type',
string_param(['Dense2']))
experiment_parameters.layer_parameter('Dense2.output_dim',
int_param(10, 500))
experiment_parameters.layer_parameter(
'Dense2.activation', string_param(['custom_activation']))
experiment_parameters.layer_parameter('Dropout.p',
float_param(0.1, 0.9))
experiment_parameters.all_search_parameters(True)
experiment = Experiment('test',
layout,
training,
batch_iterator=None,
test_batch_iterator=None,
environment=None,
parameters=experiment_parameters)
check_experiment_parameters(experiment)
for _ in range(5):
blueprint1 = create_random_blueprint(experiment)
for layer in blueprint1.layout.get_layers():
self.assertEqual('Dense2', layer.layer_type,
'Should have used custom layer')
model = ModelBuilder().build(blueprint1, cpu_device())
self.assertIsNotNone(model, 'Should have built a model')
blueprint2 = create_random_blueprint(experiment)
for layer in blueprint2.layout.get_layers():
self.assertEqual('Dense2', layer.layer_type,
'Should have used custom layer')
model = ModelBuilder().build(blueprint2, cpu_device())
self.assertIsNotNone(model, 'Should have built a model')
blueprint3 = mix_blueprints(blueprint1, blueprint2,
experiment_parameters)
for layer in blueprint3.layout.get_layers():
self.assertEqual('Dense2', layer.layer_type,
'Should have used custom layer')
model = ModelBuilder().build(blueprint3, cpu_device())
self.assertIsNotNone(model, 'Should have built a model')
blueprint4 = mutate_blueprint(blueprint1,
experiment_parameters,
mutate_in_place=False)
for layer in blueprint4.layout.get_layers():
self.assertEqual('Dense2', layer.layer_type,
'Should have used custom layer')
model = ModelBuilder().build(blueprint4, cpu_device())
self.assertIsNotNone(model, 'Should have built a model')
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')
