def _unweighted_moments(self,
x,
axes,
keep_dims=False,
extra_out_grads=None):
weights = constant_op.constant(1, dtype=x.dtype)
if extra_out_grads is not None:
# We want to assert gradients WRT weights as well as X!
extra_out_grads.append(weights)
return nn_impl.weighted_moments(x, axes, weights, keep_dims=keep_dims)
def RunWeightedMomentTest(self,
shape,
weights_shape,
axes,
keep_dims,
dtype,
dynshapes=False):
with self.cached_session() as s:
x_numpy = np.random.normal(size=shape).astype(np.float32)
weights_numpy = np.absolute( # weights must be positive
np.random.normal(size=weights_shape,
loc=1.0).astype(np.float32))
# Expand the numpy version to higher precision
x_numpy = x_numpy.astype(np.float128)
weights_numpy = weights_numpy.astype(np.float128)
x_shape = [None] * len(shape) if dynshapes else shape
weights_shape = ([None] * len(weights_shape)
if dynshapes else weights_shape)
x = array_ops.placeholder(dtype, shape=x_shape)
weights = array_ops.placeholder(dtype, shape=weights_shape)
mean, var = nn_impl.weighted_moments(x,
axes,
weights,
keep_dims=keep_dims)
ax = tuple(axes)
def _np_weighted_sum(v):
return np.sum(weights_numpy * v, axis=ax, keepdims=keep_dims)
weight_sum = _np_weighted_sum(np.ones_like(x_numpy))
expected_mean = _np_weighted_sum(x_numpy) / weight_sum
expected_mean_squared = np.multiply(expected_mean, expected_mean)
expected_x_squared = (
_np_weighted_sum(np.multiply(x_numpy, x_numpy)) / weight_sum)
expected_variance = expected_x_squared - expected_mean_squared
mean_v, var_v = s.run([mean, var],
feed_dict={
x: x_numpy,
weights: weights_numpy
})
self.assertAllCloseAccordingToType(expected_mean, mean_v)
self.assertAllCloseAccordingToType(expected_variance, var_v)
def RunWeightedMomentTest(self,
shape,
weights_shape,
axes,
keep_dims,
dtype,
dynshapes=False):
with self.cached_session() as s:
x_numpy = np.random.normal(size=shape).astype(np.float32)
weights_numpy = np.absolute( # weights must be positive
np.random.normal(
size=weights_shape, loc=1.0).astype(np.float32))
# Expand the numpy version to higher precision
x_numpy = x_numpy.astype(np.float128)
weights_numpy = weights_numpy.astype(np.float128)
x_shape = [None] * len(shape) if dynshapes else shape
weights_shape = ([None] * len(weights_shape) if dynshapes else
weights_shape)
x = array_ops.placeholder(dtype, shape=x_shape)
weights = array_ops.placeholder(dtype, shape=weights_shape)
mean, var = nn_impl.weighted_moments(
x, axes, weights, keep_dims=keep_dims)
ax = tuple(axes)
def _np_weighted_sum(v):
return np.sum(weights_numpy * v, axis=ax, keepdims=keep_dims)
weight_sum = _np_weighted_sum(np.ones_like(x_numpy))
expected_mean = _np_weighted_sum(x_numpy) / weight_sum
expected_mean_squared = np.multiply(expected_mean, expected_mean)
expected_x_squared = (_np_weighted_sum(np.multiply(x_numpy, x_numpy)) /
weight_sum)
expected_variance = expected_x_squared - expected_mean_squared
mean_v, var_v = s.run([mean, var],
feed_dict={x: x_numpy,
weights: weights_numpy})
self.assertAllCloseAccordingToType(expected_mean, mean_v)
self.assertAllCloseAccordingToType(expected_variance, var_v)
def _unweighted_moments(self, x, axes, keep_dims=False, extra_out_grads=None):
weights = constant_op.constant(1, dtype=x.dtype)
if extra_out_grads is not None:
# We want to assert gradients WRT weights as well as X!
extra_out_grads.append(weights)
return nn_impl.weighted_moments(x, axes, weights, keep_dims=keep_dims)
