def test_setitem(self):
# Single integer index.
a = np_array_ops.array([1., 2., 3.])
b = np_array_ops.array(5.)
c = np_array_ops.array(10.)
tensors = [arr.data for arr in [a, b, c]]
with backprop.GradientTape() as g:
g.watch(tensors)
a[1] = b + c
loss = np_array_ops.sum(a)
gradients = g.gradient(loss.data, tensors)
self.assertSequenceEqual(
np_array_ops.array(gradients[0]).tolist(), [1., 0., 1.])
self.assertEqual(np_array_ops.array(gradients[1]).tolist(), 1.)
self.assertEqual(np_array_ops.array(gradients[2]).tolist(), 1.)
# Tuple index.
a = np_array_ops.array([[[1., 2.], [3., 4.]],
[[5., 6.], [7., 8.]]]) # 2x2x2 array.
b = np_array_ops.array([10., 11.])
tensors = [arr.data for arr in [a, b]]
with backprop.GradientTape() as g:
g.watch(tensors)
a[(1, 0)] = b
loss = np_array_ops.sum(a)
gradients = g.gradient(loss.data, tensors)
self.assertSequenceEqual(
np_array_ops.array(gradients[0]).tolist(),
[[[1., 1.], [1., 1.]], [[0., 0.], [1., 1.]]])
self.assertEqual(np_array_ops.array(gradients[1]).tolist(), [1., 1.])
def nanmean(a, axis=None, dtype=None, keepdims=None): # pylint: disable=missing-docstring
a = np_array_ops.array(a)
if np.issubdtype(a.dtype, np.bool_) or np.issubdtype(a.dtype, np.integer):
return np_array_ops.mean(a, axis=axis, dtype=dtype, keepdims=keepdims)
nan_mask = logical_not(isnan(a))
if dtype is None:
dtype = a.dtype
normalizer = np_array_ops.sum(
nan_mask, axis=axis, dtype=dtype, keepdims=keepdims)
return nansum(a, axis=axis, dtype=dtype, keepdims=keepdims) / normalizer
def trace(a, offset=0, axis1=0, axis2=1, dtype=None): # pylint: disable=missing-docstring
if dtype:
dtype = np_utils.result_type(dtype)
a = np_array_ops.asarray(a, dtype)
if offset == 0:
a_shape = a.shape
if a_shape.rank is not None:
rank = len(a_shape)
if (axis1 == -2 or axis1 == rank - 2) and (axis2 == -1
or axis2 == rank - 1):
return math_ops.trace(a)
a = np_array_ops.diagonal(a, offset, axis1, axis2)
return np_array_ops.sum(a, -1, dtype)
