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))