experiments/STT-tensorflow
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Format according to code styles and test for complex SVD backprop

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refraction-ray 2019-09-05 17:01:21 +08:00
parent c9b193d1a9
commit 83a540a73a
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GPG Key ID: 78BD9AFA11E05B57
2 changed files with 15 additions and 13 deletions
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2
tensorflow/python/kernel_tests/svd_op_test.py
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@ -406,7 +406,7 @@ if __name__ == "__main__":
_AddTest(SvdGradOpTest, "SvdGrad", name,
_GetSvdGradOpTest(dtype, shape, compute_uv, full_matrices))
# The results are too inacurate for float32.
if dtype == np.float64:
if dtype in (np.float64, np.complex128):
_AddTest(
SvdGradGradOpTest, "SvdGradGrad", name,
_GetSvdGradGradOpTest(dtype, shape, compute_uv,

14
tensorflow/python/ops/linalg_grad.py
View File

@ -416,15 +416,15 @@ def _SvdGrad(op, grad_s, grad_u, grad_v):
# To avoid nan in cases with degenrate sigular values or zero sigular values
# in calculating f and s_inv_mat, we introduce a Lorentz brodening.
def safe_reciprocal(x, epsilon=1E-20):
return x * math_ops.reciprocal(x * x + epsilon)
def _SafeReciprocal(x, epsilon=1E-20):
return x * math_ops.reciprocal(x * x + epsilon)
s_shape = array_ops.shape(s)
f = array_ops.matrix_set_diag(
safe_reciprocal(
_SafeReciprocal(
array_ops.expand_dims(s2, -2) - array_ops.expand_dims(s2, -1)
), array_ops.zeros_like(s))
s_inv_mat = array_ops.matrix_diag(safe_reciprocal(s))
s_inv_mat = array_ops.matrix_diag(_SafeReciprocal(s))
v1 = v[..., :, :m]
grad_v1 = grad_v[..., :, :m]
@ -464,12 +464,14 @@ def _SvdGrad(op, grad_s, grad_u, grad_v):
eye = _linalg.eye(s_shape[-1], batch_shape=s_shape[:-1], dtype=a.dtype)
l = eye * v_gv
term3_nouv = math_ops.matmul(s_inv_mat, _linalg.adjoint(l)-l)
term3 = 1/2. * math_ops.matmul(u, math_ops.matmul(term3_nouv, v1, adjoint_b=True))
term3 = 1/2. * math_ops.matmul(
u, math_ops.matmul(term3_nouv, v1, adjoint_b=True))
grad_a_before_transpose += term3
if use_adjoint:
grad_a = array_ops.matrix_transpose(grad_a_before_transpose, conjugate=True)
grad_a = array_ops.matrix_transpose(
grad_a_before_transpose, conjugate=True)
else:
grad_a = grad_a_before_transpose