def testGain(self):
shape = (10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init_default = init_ops_v2.Identity()
init_custom = init_ops_v2.Identity(gain=0.9)
with test_util.use_gpu():
self.assertAllClose(self.evaluate(init_default(shape, dtype=dtype)),
np.eye(*shape))
with test_util.use_gpu():
self.assertAllClose(self.evaluate(init_custom(shape, dtype=dtype)),
np.eye(*shape) * 0.9)
def testRange(self):
with self.assertRaises(ValueError):
shape = (3, 4, 5)
self._range_test(init_ops_v2.Identity(),
shape=shape,
target_mean=1. / shape[0],
target_max=1.)
shape = (3, 3)
self._range_test(init_ops_v2.Identity(),
shape=shape,
target_mean=1. / shape[0],
target_max=1.)
def testPartition(self):
init = init_ops_v2.Identity()
with self.assertRaisesWithLiteralMatch(
ValueError,
r"Identity initializer doesn't support partition-related arguments"
):
init((4, 2), dtype=dtypes.float32, partition_shape=(2, 2))
def create_vars():
if not collection:
identity = init_ops_v2.Identity()
v1 = variables.Variable([[1., 0.], [0., 1.]], dtype=dtypes.float32)
v2 = variables.Variable(lambda: identity((2, 2), dtypes.float32))
v3 = variables.Variable(
lambda: identity((2, 2), dtypes.float32),
dtype=dtypes.float32,
shape=(2, 2))
collection.extend([v1, v2, v3])
def testNonSquare(self):
init = init_ops_v2.Identity()
shape = (10, 5)
with test_util.use_gpu():
self.assertAllClose(self.evaluate(init(shape)), np.eye(*shape))
def testInvalidShape(self):
init = init_ops_v2.Identity()
with test_util.use_gpu():
self.assertRaises(ValueError, init, shape=[5, 7, 7])
self.assertRaises(ValueError, init, shape=[5])
self.assertRaises(ValueError, init, shape=[])
def testInvalidDataType(self): init = init_ops_v2.Identity() self.assertRaises(ValueError, init, shape=[10, 5], dtype=dtypes.int32)
