def test_mutate_layout(self):
layout = Layout(input_size=100,
output_size=10,
output_activation='softmax')
training = Training(objective=None,
optimizer=None,
metric=None,
stopping=None,
batch_size=None)
experiment = Experiment(
'test',
layout,
training,
batch_iterator=None,
test_batch_iterator=None,
environment=None,
parameters=ExperimentParameters(use_default_values=False))
experiment.parameters.all_search_parameters(True)
check_experiment_parameters(experiment)
for _ in range(10):
blueprint1 = create_random_blueprint(experiment)
blueprint2 = create_random_blueprint(experiment)
mutant = mix_blueprints(blueprint1,
blueprint2,
parameters=experiment.parameters,
p_mutate_param=0.1)
blueprints = [blueprint1, blueprint2]
parent_rows = [len(b.layout.rows) for b in blueprints]
self.assertTrue(
len(mutant.layout.rows) in parent_rows,
'Should have used one of the parents')
def test_random_blueprint(self):
layout = Layout(
input_size=100,
output_size=10,
output_activation='softmax')
training = Training(
objective=None,
optimizer=None,
metric=None,
stopping=None,
batch_size=None)
experiment = Experiment(
'test',
layout,
training,
batch_iterator=None,
test_batch_iterator=None,
environment=None,
parameters=ExperimentParameters(use_default_values=False))
experiment.parameters.all_search_parameters(True)
check_experiment_parameters(experiment)
for _ in range(10):
blueprint = create_random_blueprint(experiment)
self.assertIsNotNone(blueprint, 'Should have created a blueprint')
optimizer = blueprint.training.optimizer
self.assertIsNotNone(
optimizer.optimizer,
'Should have created an optimizer')
ref_parameters = experiment.parameters.get_optimizers_parameters()
for name, param in ref_parameters[optimizer.optimizer].items():
self.assertTrue(
is_valid_param_value(param, optimizer.parameters[name]),
'Invalid param value')
self.assertIsNotNone(blueprint.layout, 'Should have created a layout')
rows = len(blueprint.layout.get_rows())
self.assertTrue(
is_valid_param_value(
experiment.parameters.get_layout_parameter('rows'),
rows),
'Invalid value')
for row in blueprint.layout.get_rows():
blocks = len(row.get_blocks())
self.assertTrue(
is_valid_param_value(
experiment.parameters.get_layout_parameter('blocks'),
blocks),
'Invalid value')
for block in row.get_blocks():
layers = len([
l
for l in block.get_layers()
if l.layer_type != 'Merge'])
self.assertTrue(
is_valid_param_value(
experiment.parameters.get_layout_parameter('layers'),
layers),
'Invalid value')
def test_search_parameters(self):
experiment = Experiment(
label='test',
parameters=ExperimentParameters(use_default_values=False))
valid_parameters = True
try:
check_experiment_parameters(experiment)
except InvalidParametersException:
valid_parameters = False
self.assertFalse(valid_parameters, 'Should have raised an exception')
def test_mutate_layout(self):
layout = Layout(
input_size=100,
output_size=10,
output_activation='softmax')
training = Training(
objective=None,
optimizer=None,
metric=None,
stopping=None,
batch_size=None)
experiment = Experiment(
'test',
layout,
training,
batch_iterator=None,
test_batch_iterator=None,
environment=None,
parameters=ExperimentParameters(use_default_values=False))
experiment.parameters.all_search_parameters(True)
check_experiment_parameters(experiment)
for _ in range(10):
blueprint = create_random_blueprint(experiment)
mutant = mutate_blueprint(
blueprint,
parameters=experiment.parameters,
p_mutate_layout=1,
layout_mutation_count=1,
layout_mutables=['rows'],
mutate_in_place=False)
self.assertTrue(
len(mutant.layout.rows) != len(blueprint.layout.rows),
'Should have mutated rows')
mutant = mutate_blueprint(
blueprint,
parameters=experiment.parameters,
p_mutate_layout=1,
layout_mutation_count=1,
layout_mutables=['blocks'],
mutate_in_place=False)
self.assertTrue(
len(mutant.layout.get_blocks()) != len(blueprint.layout.get_blocks()),
'Should have mutated blocks')
mutant = mutate_blueprint(
blueprint,
parameters=experiment.parameters,
p_mutate_layout=1,
layout_mutation_count=1,
layout_mutables=['layers'],
mutate_in_place=False)
self.assertTrue(
len(mutant.layout.get_layers()) != len(blueprint.layout.get_layers()),
'Should have mutated layers')
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_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_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')