def test_sparsemax_layer_against_numpy(self, dtype=None): """check sparsemax kernel against numpy.""" random = np.random.RandomState(1) z = random.uniform(low=-3, high=3, size=(test_obs, 10)).astype(dtype) test_utils.layer_test(layer_cls=Sparsemax, input_data=z, expected_output=_np_sparsemax(z).astype(dtype))
def test_unknown(self): inputs = np.random.random((5, 4, 2, 18)).astype('float32') test_utils.layer_test(Maxout, kwargs={'num_units': 3}, input_shape=(5, 4, 2, None), input_data=inputs) test_utils.layer_test(Maxout, kwargs={'num_units': 3}, input_shape=(None, None, None, None), input_data=inputs)
def test_nchw(self): test_utils.layer_test(Maxout, kwargs={ 'num_units': 4, 'axis': 1 }, input_shape=(2, 20, 3, 6)) test_utils.layer_test(Maxout, kwargs={ 'num_units': 4, 'axis': -3 }, input_shape=(2, 20, 3, 6))
def test_weightnorm_keras(self): input_data = np.random.random((10, 3, 4)).astype(np.float32) outputs = test_utils.layer_test(wrappers.WeightNormalization, kwargs={ 'layer': tf.keras.layers.Dense(2), 'input_shape': (3, 4) }, input_data=input_data)
def testPoincareNormalizeDimArray(self): x_shape = [20, 7, 3] epsilon = 1e-5 tol = 1e-6 np.random.seed(1) inputs = np.random.random_sample(x_shape).astype(np.float32) dim = [1, 2] outputs_expected = self._PoincareNormalize(inputs, dim, epsilon) outputs = test_utils.layer_test( PoincareNormalize, kwargs={ 'axis': dim, 'epsilon': epsilon }, input_data=inputs, expected_output=outputs_expected) for y in outputs_expected, outputs: norm = np.linalg.norm(y, axis=tuple(dim)) self.assertLessEqual(norm.max(), 1. - epsilon + tol)
def test_invalid_shape(self): with self.assertRaisesRegexp(ValueError, r'number of features'): test_utils.layer_test(Maxout, kwargs={'num_units': 3}, input_shape=(5, 4, 2, 7))
def test_simple(self): test_utils.layer_test(Maxout, kwargs={'num_units': 3}, input_shape=(5, 4, 2, 18))