def L_op(self, inputs, outputs, grads):
(x, ) = inputs
(gz, ) = grads
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=config.floatX)]
else:
return [x.zeros_like()]
cst = np.asarray(2.0 / np.sqrt(np.pi),
dtype=upcast(x.type.dtype, gz.type.dtype))
return (-gz * cst * exp(-x * x), )
grad_rtol=div_grad_rtol,
inplace=True,
)
TestInvInplaceBroadcast = makeBroadcastTester(
op=inv_inplace,
expected=lambda x: _numpy_true_div(np.int8(1), x),
good=_good_inv_inplace,
bad_runtime=_bad_runtime_inv,
grad_rtol=div_grad_rtol,
inplace=True,
)
TestModInplaceBroadcast = makeBroadcastTester(
op=mod_inplace,
expected=lambda x, y: np.asarray(x % y, dtype=upcast(x.dtype, y.dtype)),
good=copymod(_good_broadcast_div_mod_normal_float_inplace,
["complex1", "complex2"]),
grad_eps=1e-5,
inplace=True,
)
TestPowInplaceBroadcast = makeBroadcastTester(
op=pow_inplace,
expected=lambda x, y: x**y,
good=_good_broadcast_pow_normal_float_pow,
inplace=True,
mode=ignore_isfinite_mode,
)
TestNegInplaceBroadcast = makeBroadcastTester(
