def _MulGrad(op, grad):
"""The gradient of scalar multiplication."""
x = op.inputs[0]
y = op.inputs[1]
# pylint: disable=protected-access
if (isinstance(grad, ops.Tensor) and
_ShapesFullySpecifiedAndEqual(x, y, grad) and
grad.dtype in (dtypes.int32, dtypes.float32)):
return gen_math_ops._mul(grad, y), gen_math_ops._mul(grad, x)
assert x.dtype.base_dtype == y.dtype.base_dtype, (x.dtype, " vs. ", y.dtype)
sx = array_ops.shape(x)
sy = array_ops.shape(y)
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
# pylint: enable=protected-access
x = math_ops.conj(x)
y = math_ops.conj(y)
return (array_ops.reshape(math_ops.reduce_sum(grad * y, rx), sx),
array_ops.reshape(math_ops.reduce_sum(x * grad, ry), sy))
def _MulGrad(op, grad):
"""The gradient of scalar multiplication."""
x = op.inputs[0]
y = op.inputs[1]
# pylint: disable=protected-access
if (isinstance(grad, ops.Tensor) and
_ShapesFullySpecifiedAndEqual(x, y, grad) and
grad.dtype in (dtypes.int32, dtypes.float32)):
return gen_math_ops._mul(grad, y), gen_math_ops._mul(grad, x)
assert x.dtype.base_dtype == y.dtype.base_dtype, (x.dtype, " vs. ", y.dtype)
sx = array_ops.shape(x)
sy = array_ops.shape(y)
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
# pylint: enable=protected-access
x = math_ops.conj(x)
y = math_ops.conj(y)
return (array_ops.reshape(math_ops.reduce_sum(grad * y, rx), sx),
array_ops.reshape(math_ops.reduce_sum(x * grad, ry), sy))