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)