def testTTTensorSimple(self):
# Test that a tensor of ones and of zeros can be converted into TT with
# TT-rank 1.
shape = (2, 1, 4, 3)
tens_arr = (np.zeros(shape).astype(np.float32),
np.ones(shape).astype(np.float32))
for tens in tens_arr:
tf_tens = tf.constant(tens)
tt_tens = decompositions.to_tt_tensor(tf_tens, max_tt_rank=1)
with self.test_session():
self.assertAllClose(tens, ops.full(tt_tens).eval())
dynamic_tt_ranks = shapes.tt_ranks(tt_tens).eval()
static_tt_ranks = tt_tens.get_tt_ranks().as_list()
self.assertAllEqual(dynamic_tt_ranks, static_tt_ranks)
# Try to decompose the same tensor with unknown shape.
tf_tens_pl = tf.placeholder(tf.float32,
(None, None, None, None))
tt_tens = decompositions.to_tt_tensor(tf_tens_pl,
max_tt_rank=1)
tt_val = ops.full(tt_tens).eval({tf_tens_pl: tens})
self.assertAllClose(tens, tt_val)
dynamic_tt_ranks = shapes.tt_ranks(tt_tens).eval(
{tf_tens_pl: tens})
self.assertAllEqual(dynamic_tt_ranks, static_tt_ranks)
def testTTCompositeRankTensor(self):
# Test if a composite rank (list of ranks) can be used for decomposition
# for tensor.
np.random.seed(1)
np_tensor = np.random.rand(2, 3, 3, 1).astype(self.dtype.as_numpy_dtype)
tf_tensor = tf.constant(np_tensor)
tt_ranks = [1, 2, 3, 3, 1]
tt_tensor = decompositions.to_tt_tensor(tf_tensor, max_tt_rank=tt_ranks)
self.assertAllClose(np_tensor, self.evaluate(ops.full(tt_tensor)))
def testTTTensor(self): shape = (2, 1, 4, 3) np.random.seed(1) tens = np.random.rand(*shape).astype(self.dtype.as_numpy_dtype) tf_tens = tf.constant(tens) tt_tens = decompositions.to_tt_tensor(tf_tens, max_tt_rank=3) self.assertAllClose(tens, self.evaluate(ops.full(tt_tens))) dynamic_tt_ranks = self.evaluate(shapes.tt_ranks(tt_tens)) static_tt_ranks = tt_tens.get_tt_ranks().as_list() self.assertAllEqual(dynamic_tt_ranks, static_tt_ranks)
def testTTTensor(self):
shape = (2, 1, 4, 3)
np.random.seed(1)
tens = np.random.rand(*shape).astype(np.float32)
tf_tens = tf.constant(tens)
tt_tens = decompositions.to_tt_tensor(tf_tens, max_tt_rank=3)
with self.test_session():
self.assertAllClose(tens, ops.full(tt_tens).eval())
dynamic_tt_ranks = shapes.tt_ranks(tt_tens).eval()
static_tt_ranks = tt_tens.get_tt_ranks().as_list()
self.assertAllEqual(dynamic_tt_ranks, static_tt_ranks)
# Try to decompose the same tensor with unknown shape.
tf_tens_pl = tf.placeholder(tf.float32, (None, None, 4, None))
tt_tens = decompositions.to_tt_tensor(tf_tens_pl, max_tt_rank=3)
tt_val = ops.full(tt_tens).eval({tf_tens_pl: tens})
self.assertAllClose(tens, tt_val)
dynamic_tt_ranks = shapes.tt_ranks(tt_tens).eval(
{tf_tens_pl: tens})
self.assertAllEqual(dynamic_tt_ranks, static_tt_ranks)
def testTTTensorSimple(self):
# Test that a tensor of ones and of zeros can be converted into TT with
# TT-rank 1.
shape = (2, 1, 4, 3)
tens_arr = (np.zeros(shape).astype(self.dtype.as_numpy_dtype),
np.ones(shape).astype(self.dtype.as_numpy_dtype))
for tens in tens_arr:
tf_tens = tf.constant(tens)
tt_tens = decompositions.to_tt_tensor(tf_tens, max_tt_rank=1)
self.assertAllClose(tens, self.evaluate(ops.full(tt_tens)))
dynamic_tt_ranks = self.evaluate(shapes.tt_ranks(tt_tens))
static_tt_ranks = tt_tens.get_tt_ranks().as_list()
self.assertAllEqual(dynamic_tt_ranks, static_tt_ranks)