def test_updates_algorithm_add_updates():
n = shared_floatx(1)
m = shared_floatx(0)
algorithm = UpdatesAlgorithm(updates=[(n, n + 1)])
algorithm.add_updates([(m, n % 2)])
assert len(algorithm.updates) == 2
algorithm.initialize()
algorithm.process_batch({})
assert_allclose(n.get_value(), 2)
assert_allclose(m.get_value(), 1)
algorithm.process_batch({})
assert_allclose(n.get_value(), 3)
assert_allclose(m.get_value(), 0)
def test_updates_algorithm():
n = shared_floatx(1)
algorithm = UpdatesAlgorithm(updates=[(n, n + 1)])
algorithm.initialize()
algorithm.process_batch({})
assert_allclose(n.get_value(), 2)
algorithm.process_batch({})
assert_allclose(n.get_value(), 3)
def test_training_data_monitoring_updates_algorithm():
features = [
numpy.array(f, dtype=theano.config.floatX)
for f in [[1, 2], [3, 5], [5, 8]]
]
targets = numpy.array([f.sum() for f in features])
dataset = IterableDataset(dict(features=features, targets=targets))
x = tensor.vector('features')
y = tensor.scalar('targets')
m = x.mean().copy(name='features_mean')
t = y.sum().copy(name='targets_sum')
main_loop = MainLoop(
model=None,
data_stream=dataset.get_example_stream(),
algorithm=UpdatesAlgorithm(),
extensions=[
TrainingDataMonitoring([m, t], prefix="train1", after_batch=True)
],
)
main_loop.extensions[0].main_loop = main_loop
assert len(main_loop.algorithm.updates) == 0
main_loop.extensions[0].do('before_training')
assert len(main_loop.algorithm.updates) > 0
def test_updates_algorithm_data():
n = shared_floatx(1)
m = tensor.scalar('m')
algorithm = UpdatesAlgorithm(updates=[(n, m + 1)])
algorithm.initialize()
algorithm.process_batch({'m': 5})
assert_allclose(n.get_value(), 6)
algorithm.process_batch({'m': 3})
assert_allclose(n.get_value(), 4)
def test_updates_algorithm_add_updates():
n = shared_floatx(1)
m = shared_floatx(0)
algorithm = UpdatesAlgorithm(updates=[(n, n + 1)])
algorithm.add_updates([(m, n % 2)])
assert len(algorithm.updates) == 2
algorithm.initialize()
algorithm.process_batch({})
assert_allclose(n.get_value(), 2)
assert_allclose(m.get_value(), 1)
algorithm.process_batch({})
assert_allclose(n.get_value(), 3)
assert_allclose(m.get_value(), 0)
def test_updates_algorithm():
n = shared_floatx(1)
algorithm = UpdatesAlgorithm(updates=[(n, n + 1)])
algorithm.initialize()
algorithm.process_batch({})
assert_allclose(n.get_value(), 2)
algorithm.process_batch({})
assert_allclose(n.get_value(), 3)
def test_updates_algorithm_data():
n = shared_floatx(1)
m = tensor.scalar('m')
algorithm = UpdatesAlgorithm(updates=[(n, m + 1)])
algorithm.initialize()
algorithm.process_batch({'m': 5})
assert_allclose(n.get_value(), 6)
algorithm.process_batch({'m': 3})
assert_allclose(n.get_value(), 4)
