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)