def test_ridge_regression_gradient(self):
estimator = ridge.RidgeRegression(l2_reg=.01)
d = 5
input_vals = {"x": np.random.randn(d)}
outcome_vals = {"y": np.array(np.random.randn())}
parameter_vals = {"w": np.random.randn(d), "b":np.array(np.random.randn())}
test_utils.test_ComputationGraphFunction(estimator.graph, input_vals, outcome_vals, parameter_vals)
self.assertTrue(1 == 1)
def test_mlp_regression_gradient(self):
estimator = mlp_regression.MLPRegression()
num_hidden_units = 4
num_ftrs = 5
input_vals = {"x": np.random.randn(num_ftrs)}
outcome_vals = {"y": np.array(np.random.randn())}
parameter_vals = {"W1": np.random.standard_normal((num_hidden_units, num_ftrs)),
"b1": np.random.standard_normal((num_hidden_units)),
"W2": np.random.standard_normal((num_hidden_units)),
"b2": np.array(np.random.randn()) }
max_rel_err = test_utils.test_ComputationGraphFunction(estimator.graph, input_vals, outcome_vals, parameter_vals)
max_allowed_rel_err = 1e-5
self.assertTrue(max_rel_err < max_allowed_rel_err)