def testReduceSumBatchMultipleWeighted(self):
# Multiple weighted sums of a batch of TT-tensors.
def desired(tt_batch, coef):
res = coef[0] * tt_batch[0]
for i in range(1, tt_batch.batch_size):
res += coef[i] * tt_batch[i]
return res
with self.test_session() as sess:
tt_batch = initializers.random_tensor_batch((4, 3, 5),
tt_rank=2,
batch_size=3)
coef = [[1., 0.1], [0.9, -0.2], [0.3, 0.3]]
coef = np.array(coef).astype(np.float32)
res_actual = ops.full(
approximate.reduce_sum_batch(tt_batch, 6, coef))
res_desired_1 = ops.full(desired(tt_batch, coef[:, 0]))
res_desired_2 = ops.full(desired(tt_batch, coef[:, 1]))
res_desired = tf.stack((res_desired_1, res_desired_2))
res_desired_val, res_actual_val = sess.run(
[res_desired, res_actual])
self.assertAllClose(res_desired_val,
res_actual_val,
atol=1e-5,
rtol=1e-5)
def testReduceSumBatchWeighted(self):
# Weighted sum of a batch of TT-tensors.
def desired(tt_batch, coef):
res = coef[0] * tt_batch[0]
for i in range(1, tt_batch.batch_size):
res += coef[i] * tt_batch[i]
return res
tt_batch = initializers.random_tensor_batch((4, 3, 5),
tt_rank=3,
batch_size=3,
dtype=self.dtype)
res_actual = ops.full(approximate.reduce_sum_batch(tt_batch, 9,
[1.2, -0.2, 1]))
res_desired = ops.full(desired(tt_batch, [1.2, -0.2, 1]))
res_desired_val, res_actual_val = self.evaluate([res_desired, res_actual])
self.assertAllClose(res_desired_val, res_actual_val, atol=1e-5, rtol=1e-5)
def testReduceSumBatch(self):
# Sum a batch of TT-tensors.
def desired(tt_batch):
res = tt_batch[0]
for i in range(1, tt_batch.batch_size):
res += tt_batch[i]
return res
for batch_size in [2, 3, 4, 5]:
tt_batch = initializers.random_tensor_batch((4, 3, 5),
tt_rank=2,
batch_size=batch_size,
dtype=self.dtype)
res_actual = ops.full(approximate.reduce_sum_batch(tt_batch, 10))
res_desired = ops.full(desired(tt_batch))
res_desired_val, res_actual_val = self.evaluate([res_desired, res_actual])
self.assertAllClose(res_desired_val, res_actual_val, atol=1e-5, rtol=1e-5)