def test_branin_keras_tf_impl():
# Optimal parameter 1
x = [-np.pi, 12.275]
params = data.OrderedDict()
for i, xx in enumerate(x):
params['h%d' % i] = xx
loss = evaluation_branin_keras_tf(None, 0, params)
print(loss)
assert np.allclose(loss, 0.397887)
# Optimal parameter 2
x = [np.pi, 2.275]
params = data.OrderedDict()
for i, xx in enumerate(x):
params['h%d' % i] = xx
loss = evaluation_branin_keras_tf(None, 0, params)
assert np.allclose(loss, 0.397887)
# Optimal parameter 3
x = [9.42478, 2.475]
params = data.OrderedDict()
for i, xx in enumerate(x):
params['h%d' % i] = xx
loss = evaluation_branin_keras_tf(None, 0, params)
assert np.allclose(loss, 0.397887)
def test_hartmann6_keras_tf_impl():
# Optimal _parameters
x = [0.20169, 0.15001, 0.476874, 0.275332, 0.311652, 0.6573]
params = data.OrderedDict()
for i, xx in enumerate(x):
params['h%d' % i] = xx
loss = evaluation_hartmann6_keras_tf(None, 0, params)
assert np.allclose(loss, -3.32237)
