def reciprocal(x, name=''):
'''
Computes the element-wise reciprocal of `x`:
Example:
>>> C.eval(C.abs([-1/3, 1/5, -2, 3]))
[array([[ -3., 5., -1/2., 1/3.]])]
Args:
x: numpy array or any :class:`cntk.Function` that outputs a tensor
name (str): the name of the node in the network
Returns:
:class:`cntk.Function`
'''
from cntk import reciprocal
x = sanitize_input(x)
return reciprocal(x, name).output()
def reciprocal(x, name=''):
'''
Computes the element-wise reciprocal of `x`:
Example:
>>> C.eval(C.abs([-1/3, 1/5, -2, 3]))
[array([[ -3., 5., -1/2., 1/3.]])]
Args:
x: numpy array or any :class:`cntk.Function` that outputs a tensor
name (str): the name of the node in the network
Returns:
:class:`cntk.Function`
'''
from cntk import reciprocal
x = sanitize_input(x)
return reciprocal(x, name).output()
def gaussian_mdn_phi(target, mu, sigma, ndim: int):
"""
Calculates phi between the target tensor and the network prediction
Does not assumes independence between components of target.
Arguments:
target: target tensor with shape (ndim, )
mu: means of gaussian mdn with shape (nmix, ndim)
sigma: sigma of gaussian mdn
nmix (int): number of mixtures
ndim (int): number of dimensions in gaussian
Returns:
:class:`~cntk.ops.functions.Function`
"""
if not len(mu.shape) == 2:
raise ValueError("mu {0} must have shape (nmix, ndim)".format(mu.shape))
t = C.expand_dims(target, axis=0)
exp_term = C.exp(C.negate(C.square(C.reduce_l2(t - mu, axis=-1)) / (2 * C.square(sigma))))
factor = C.reciprocal((2 * pi) ** (ndim / 2) * C.pow(sigma, ndim))
return factor * exp_term
def test_Reciprocal(tmpdir):
model = C.reciprocal([-1 / 3, 1 / 5, -2, 3])
verify_no_input(model, tmpdir, 'Reciprocal_0')
def test_Reciprocal(tmpdir, dtype):
with C.default_options(dtype=dtype):
model = C.reciprocal(np.array([-1 / 3, 1 / 5, -2, 3]).astype(dtype))
verify_no_input(model, tmpdir, 'Reciprocal_0')
def test_Reciprocal(tmpdir, dtype):
with C.default_options(dtype = dtype):
model = C.reciprocal(np.array([-1/3, 1/5, -2, 3]).astype(dtype))
verify_no_input(model, tmpdir, 'Reciprocal_0')
def test_reciprocal():
assert_cntk_ngraph_isclose(C.reciprocal([-1 / 3, 1 / 5, -2, 3]))
assert_cntk_ngraph_isclose(C.reciprocal([[-1, 0.5], [-3, 4]]))
assert_cntk_ngraph_isclose(
C.reciprocal([[[1, 0.5], [-3, 0.33]], [[1, -2], [3, 4]]]))
def test_Reciprocal(tmpdir):
model = C.reciprocal([-1/3, 1/5, -2, 3])
verify_no_input(model, tmpdir, 'Reciprocal_0')
