def testProjectWeightedSumDtypeBug(self):
# Test that project_sum(TensorTrain, TensorTrain variable, np.array) works.
what = initializers.random_tensor_batch((2, 3, 4), batch_size=3,
dtype=self.dtype)
where = variables.get_variable('a', initializer=what[0])
weights = tf.zeros((3,), dtype=self.dtype)
# Check that it doesn't throw an exception trying to convert weights to
# Variable dtype (float32_ref).
riemannian.project_sum(what, where, weights)
def testProjectSum(self):
# Test projecting a batch of TT-tensors.
tens = initializers.random_tensor_batch((2, 3, 4), batch_size=3)
tangent_tens = initializers.random_tensor((2, 3, 4), 3)
weighted_sum = tens[0] + tens[1] + tens[2]
direct_proj = riemannian.project_sum(weighted_sum, tangent_tens)
actual_proj = riemannian.project_sum(tens, tangent_tens)
with self.test_session() as sess:
res = sess.run((ops.full(direct_proj), ops.full(actual_proj)))
desired_val, actual_val = res
self.assertAllClose(desired_val, actual_val)
def testProjectWeightedSum(self):
# Test projecting a batch of TT-tensors with providing coefs.
tens = initializers.random_tensor_batch((2, 3, 4), 3, batch_size=4)
coef = [0.1, -2, 0, 0.4]
tangent_tens = initializers.random_tensor((2, 3, 4), 4)
weighted_sum = coef[0] * tens[0] + coef[1] * tens[1] + coef[2] * tens[2]
weighted_sum += coef[3] * tens[3]
direct_proj = riemannian.project_sum(weighted_sum, tangent_tens)
actual_proj = riemannian.project_sum(tens, tangent_tens, coef)
with self.test_session() as sess:
res = sess.run((ops.full(direct_proj), ops.full(actual_proj)))
desired_val, actual_val = res
self.assertAllClose(desired_val, actual_val)
def testProjectOnItself(self):
# Projection of X into the tangent space of itself is X: P_x(x) = x.
tens = initializers.random_tensor((2, 3, 4), dtype=self.dtype)
proj = riemannian.project_sum(tens, tens)
with self.test_session() as sess:
actual_val, desired_val = sess.run((ops.full(proj), ops.full(tens)))
self.assertAllClose(desired_val, actual_val)
def testProjectMatrixOnItself(self):
# Project a TT-matrix on itself.
# Projection of X into the tangent space of itself is X: P_x(x) = x.
tt_mat = initializers.random_matrix(((2, 3, 4), (2, 3, 4)),
dtype=self.dtype)
proj = riemannian.project_sum(tt_mat, tt_mat)
actual_val, desired_val = self.evaluate((ops.full(proj), ops.full(tt_mat)))
self.assertAllClose(desired_val, actual_val)
def testCompareProjectSumAndProject(self):
# Compare results of project_sum and project.
tens = initializers.random_tensor_batch((2, 3, 4), 3, batch_size=4)
tangent_tens = initializers.random_tensor((2, 3, 4), 4)
project_sum = riemannian.project_sum(tens, tangent_tens, tf.eye(4))
project = riemannian.project(tens, tangent_tens)
with self.test_session() as sess:
res = sess.run((ops.full(project_sum), ops.full(project)))
project_sum_val, project_val = res
self.assertAllClose(project_sum_val, project_val)
def testCompareProjectSumAndProject(self):
# Compare results of project_sum and project.
tens = initializers.random_tensor_batch((2, 3, 4), 3, batch_size=4,
dtype=self.dtype)
tangent_tens = initializers.random_tensor((2, 3, 4), 4,
dtype=self.dtype)
project_sum = riemannian.project_sum(tens, tangent_tens, np.eye(4))
project = riemannian.project(tens, tangent_tens)
res = self.evaluate((ops.full(project_sum), ops.full(project)))
project_sum_val, project_val = res
self.assertAllClose(project_sum_val, project_val)
def testProjectWeightedSumMultipleOutputs(self):
# Test projecting a batch of TT-tensors with providing weights and outputing
# several TT objects with different weights.
tens = initializers.random_tensor_batch((2, 3, 4), 3, batch_size=4)
np.random.seed(0)
weights = np.random.randn(4, 2).astype(np.float32)
tangent_tens = initializers.random_tensor((2, 3, 4), 4)
weighted_sum_1 = weights[0, 0] * tens[0] + weights[1, 0] * tens[1] +\
weights[2, 0] * tens[2] + weights[3, 0] * tens[3]
weighted_sum_2 = weights[0, 1] * tens[0] + weights[1, 1] * tens[1] +\
weights[2, 1] * tens[2] + weights[3, 1] * tens[3]
direct_proj_1 = riemannian.project_sum(weighted_sum_1, tangent_tens)
direct_proj_2 = riemannian.project_sum(weighted_sum_2, tangent_tens)
direct_proj_1 = shapes.expand_batch_dim(direct_proj_1)
direct_proj_2 = shapes.expand_batch_dim(direct_proj_2)
direct_projs = batch_ops.concat_along_batch_dim((direct_proj_1, direct_proj_2))
actual_proj = riemannian.project_sum(tens, tangent_tens, weights)
with self.test_session() as sess:
res = sess.run((ops.full(direct_projs), ops.full(actual_proj)))
desired_val, actual_val = res
self.assertAllClose(desired_val, actual_val, atol=1e-5, rtol=1e-5)
def testProject(self):
# Compare our projection with the results obtained (and precomputed) from
# tt.riemannian.project which is well tested.
tangent_tens_cores = ([[[-0.42095269, 0.02130842],
[-0.4181081 , 0.42945687],
[ 0.45972439, -0.4525616 ],
[-0.17159869, -0.14505528]]], [[[ 0.23344421],
[ 0.81480049],
[-0.92385135]],
[[-0.19279465],
[ 0.524976 ],
[-0.40149197]]])
convert = lambda t: np.array(t, dtype=self.dtype.as_numpy_dtype)
tangent_tens_cores = list([convert(t) for t in tangent_tens_cores])
tangent_tens = TensorTrain(tangent_tens_cores, (4, 3), (1, 2, 1))
tens_cores = ([[[-1.01761142, 0.36075896, -0.2493624 ],
[-0.99896565, -1.12685474, 1.02832458],
[ 1.08739724, -0.6537435 , 1.99975537],
[ 0.35128005, 0.40395104, -0.16790072]]], [[[ 0.34105142],
[ 0.10371947],
[-1.76464904]],
[[ 0.32639758],
[-1.27843174],
[-1.41590327]],
[[ 0.76616274],
[ 0.6577514 ],
[ 2.13703185]]])
tens_cores = list([convert(t) for t in tens_cores])
tens = TensorTrain(tens_cores, (4, 3), (1, 3, 1))
desired_projection = [[-0.67638254, -1.17163914, 0.29850939],
[-1.66479093, -0.99003251, 2.46629195],
[-0.04847773, -0.72908174, 0.20142675],
[ 0.34431125, -0.20935516, -1.15864246]]
proj = riemannian.project_sum(tens, tangent_tens)
proj_full = ops.full(proj)
with self.test_session() as sess:
proj_v = proj_full.eval()
self.assertAllClose(desired_projection, proj_v)
self.assertEqual(self.dtype.as_numpy_dtype, proj_v.dtype)
