def _check_get_obj_equals_get_obj_jac(make_model_func):
attrs = random.randint(1, 10)
outs = random.randint(1, 10)
inp_matrix, tar_matrix = datasets.get_random_regression(10, attrs, outs)
model = make_model_func(attrs, outs)
flat_weights = model._weight_matrix.ravel()
assert model._get_obj(flat_weights, inp_matrix,
tar_matrix) == model._get_obj_jac(
flat_weights, inp_matrix, tar_matrix)[0]
def _check_jacobian(make_model_func):
attrs = random.randint(1, 10)
outs = random.randint(1, 10)
model = make_model_func(attrs, random.randint(1, 10), outs)
inp_matrix, tar_matrix = datasets.get_random_regression(10, attrs, outs)
# Test jacobian of error function
f = lambda xk: mlp._mlp_obj(model, inp_matrix, tar_matrix, xk)
df = lambda xk: mlp._mlp_obj_jac(model, inp_matrix, tar_matrix, xk)[1]
helpers.check_gradient(
f, df, inputs=mlp._flatten(model._weight_matrices), f_shape='scalar')
def test_rbf_obj_and_obj_jac_match():
"""obj and obj_jac functions should return the same obj value."""
attrs = random.randint(1, 10)
outs = random.randint(1, 10)
model = rbf.RBF(attrs, random.randint(1, 10), outs)
dataset = datasets.get_random_regression(10, attrs, outs)
# Don't use exactly the same parameters, to ensure obj functions are actually
# using the given parameters
parameters = random.uniform(-1.0, 1.0) * model._weight_matrix.ravel()
assert helpers.approx_equal(
model._get_obj(parameters, dataset[0], dataset[1]),
model._get_obj_jac(parameters, dataset[0], dataset[1])[0])
def _check_jacobian(make_model_func):
attrs = random.randint(1, 10)
outs = random.randint(1, 10)
model = make_model_func(attrs, outs)
inp_matrix, tar_matrix = datasets.get_random_regression(10, attrs, outs)
# Test jacobian of error function
f = lambda xk: model._get_obj(xk, inp_matrix, tar_matrix)
df = lambda xk: model._get_obj_jac(xk, inp_matrix, tar_matrix)[1]
helpers.check_gradient(f,
df,
inputs=model._weight_matrix.ravel(),
f_shape='scalar')
def _check_jacobian(make_model_func):
attrs = random.randint(1, 10)
outs = random.randint(1, 10)
model = make_model_func(attrs, random.randint(1, 10), outs)
inp_matrix, tar_matrix = datasets.get_random_regression(random.randint(1, 10), attrs, outs)
# Test jacobian of error function
f = lambda xk: model._get_obj(xk, inp_matrix, tar_matrix)
df = lambda xk: model._get_obj_jac(xk, inp_matrix, tar_matrix)[1]
helpers.check_gradient(
f,
df,
f_arg_tensor=mlp._flatten(model._bias_vec, model._weight_matrices),
f_shape='scalar')
def _check_obj_and_obj_jac_match(make_model_func, classification=False):
"""obj and obj_jac functions should return the same obj value."""
attrs = random.randint(1, 10)
outs = random.randint(1, 10)
model = make_model_func(attrs, random.randint(1, 10), outs)
if classification:
dataset = datasets.get_random_classification(10, attrs, outs)
else:
dataset = datasets.get_random_regression(10, attrs, outs)
# Don't use exactly the same parameters, to ensure obj functions are actually
# using the given parameters
parameters = random.uniform(-1.0,
1.0) * mlp._flatten(model._weight_matrices)
assert helpers.approx_equal(
mlp._mlp_obj(model, dataset[0], dataset[1], parameters),
mlp._mlp_obj_jac(model, dataset[0], dataset[1], parameters)[0])
