def test_ext_lambdafunc(tmpdir):
dim = 4
class CallbackCounter(object):
def __init__(self):
self.count = 0
def inc(self, arg):
self.count += 1
cb = CallbackCounter()
p = C.parameter(shape=(dim,), init=1)
i = C.input_variable(dim, needs_gradient=True, name='i_var')
k = i * p
m = LambdaFunc(k,
when=lambda arg: np.sum(arg) > 1,
execute=cb.inc)
m = C.user_function(m)
z0 = m + 0
filepath = str(tmpdir / 'test_ext_lambdafunc.dat')
z0.save(filepath)
Function.register_udf_deserialize_callback('conditional_exec_lambda',
lambda x, *unused: LambdaFunc(x, when=lambda arg: np.sum(arg) > 1, execute=cb.inc))
z = Function.load(filepath)
momentum_time_constant = C.momentum_as_time_constant_schedule(1100)
lr_per_sample = C.learning_rate_schedule(0.007, C.UnitType.sample)
trainer = C.Trainer(z, (z + 0, z + 0), [C.momentum_sgd(z.parameters,
lr_per_sample,
momentum_time_constant,
True)])
i = 0
input_data = 0.1 * np.ones(dim)
trainer.train_minibatch([input_data])
assert cb.count == 0
input_data = 0.3 * np.ones(dim)
trainer.train_minibatch([input_data])
assert cb.count == 1
def test_ext_lambdafunc(tmpdir):
dim = 4
class CallbackCounter(object):
def __init__(self):
self.count = 0
def inc(self, arg):
self.count += 1
cb = CallbackCounter()
p = C.parameter(shape=(dim,), init=1)
i = C.input_variable(dim, needs_gradient=True, name='i_var')
k = i * p
m = LambdaFunc(k,
when=lambda arg: np.sum(arg) > 1,
execute=cb.inc)
m = C.user_function(m)
z0 = m + 0
filepath = str(tmpdir / 'test_ext_lambdafunc.dat')
z0.save(filepath)
Function.register_udf_deserialize_callback('conditional_exec_lambda',
lambda x, *unused: LambdaFunc(x, when=lambda arg: np.sum(arg) > 1, execute=cb.inc))
z = Function.load(filepath)
momentum_time_constant = C.momentum_as_time_constant_schedule(1100)
lr_per_sample = C.learning_parameter_schedule(0.007, minibatch_size = 1)
trainer = C.Trainer(z, (z + 0, z + 0), [C.momentum_sgd(z.parameters,
lr_per_sample,
momentum_time_constant,
True)])
i = 0
input_data = 0.1 * np.ones(dim)
trainer.train_minibatch([input_data])
assert cb.count == 0
input_data = 0.3 * np.ones(dim)
trainer.train_minibatch([input_data])
assert cb.count == 1
def test_override_deserialize(tmpdir):
dev, w_value, c1_value, c2_value, op = build_test_function()
filepath = str(tmpdir / 'test_override_deserialize.dat')
op.save(filepath)
Function.register_udf_deserialize_callback(MyPlus._op_name(),
lambda *x: MyPlusPlus(*x))
op_reloaded = Function.load(filepath, device=dev)
np.random.seed(1)
for i in range(5):
x_value = np.random.random((2, 2)).astype(np.float32)
x_data = C.NDArrayView.from_dense(x_value, device=dev)
result = op_reloaded.eval({op_reloaded.arguments[0]: x_data}, device=dev)
expected = 2 * (np.matmul(2 * (x_value + c1_value), w_value) + c2_value)
assert np.allclose(result, expected)
def test_override_deserialize(tmpdir):
dev, w_value, c1_value, c2_value, op = build_test_function()
filepath = str(tmpdir / 'test_override_deserialize.dat')
op.save(filepath)
Function.register_udf_deserialize_callback(MyPlus._op_name(),
lambda *x: MyPlusPlus(*x))
op_reloaded = Function.load(filepath, device=dev)
np.random.seed(1)
for i in range(5):
x_value = np.random.random((2, 2)).astype(np.float32)
x_data = C.NDArrayView.from_dense(x_value, device=dev)
result = op_reloaded.eval({op_reloaded.arguments[0]: x_data}, device=dev)
expected = 2 * (np.matmul(2 * (x_value + c1_value), w_value) + c2_value)
assert np.allclose(result, expected)