srch

a rank 1 tensor T(1). Finally, the third term is the symmetrized product of two vectors, with the extra condition that it is made traceless. Symmetrizing gives us six components, but making it also traceless leaves five independent 3

I will elaborate the answer I gave in a comment. As mentioned the trace of the stress tensor in a QFT is related to the $\beta$ function by $$T_\mu^\mu \sim ...

Dec 15, 2017 · Rank-1 tensors are always well-conditioned. The class of weak 3-orthogonal tensors, which includes orthogonally decomposable tensors, contains both well-conditioned and ill-conditioned problems. Numerical experiments confirm that the condition number yields a good estimate of the magnitude of the change of the parameters when the tensor is ...

torch.where. Return a tensor of elements selected from either x or y, depending on condition. The operation is defined as: The tensors condition, x, y must be broadcastable. Currently valid scalar and tensor combination are 1. Scalar of floating dtype and torch.double 2. Scalar of integral dtype and torch.long 3.

tf.cond supports nested structures as implemented in tensorflow.python.util.nest. Both true_fn and false_fn must return the same (possibly nested) value …

TypeError: 'Tensor' object does not support item assignment The condition in the square brackets should be arbitrary as in a[a<1] = 0. Is there a way to realize this "conditional assignment" (for lack of a better name) in tensorflow?

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A tensor is a linear vector valued function defined on the set of all ... The eigenvalues of a second order tensor are computed using the condition det (S ...

Returns elements chosen from x or y depending on a boolean tensor condition, or the indices of entries satisfying the condition. The input tensors condition , x ...

true_fn and false_fn both return lists of output tensors. true_fn and ... by storing a reference to a branch tensor in the python state.

15.12.2017 · Rank-1 tensors are always well-conditioned. The class of weak 3-orthogonal tensors, which includes orthogonally decomposable tensors, contains both well-conditioned and ill-conditioned problems.

Realizability conditions for the components of this tensor are derived, which hold if and only if the filter function is positive. The spectral cut-off, one of ...

21.01.2022 · If you are using the old version of TensorFlow 1.x then you can easily use the eval () or session () function. In this example, we have created the tensorproto and it is an object that has certain types and shapes and it will check the condition if the shape value is none then it specifies the numpy array. Syntax:

May 12, 2020 · HI, torch uses same convention as numpy such for finding values or indices of particular tensor regarding specific condition. torch.where and torch.nonzero. What you can do is to apply your condition and get a binary mask of indices that match the condition and find the indices using torch.nonzero().

The tensors condition , x , y must be broadcastable. Note. Currently valid scalar and tensor combination are 1. Scalar of floating dtype and torch.double 2.

Function 3 - torch.where(). Return a tensor of elements selected from either x or y, depending on condition. if x then x otherwise y.

import tensorflow as tf conditionval = 1 init_a = tf.constant ( [1, 2, 3, 1], dtype=tf.int32, name='init_a') a = tf.variable (init_a, dtype=tf.int32, name='a') target = tf.fill (a.get_shape (), conditionval, name='target') init = tf.initialize_all_variables () condition = tf.not_equal (a, target) defaultvalues = tf.zeros (a.get_shape (), …

For a rather general class of differential models, a simple condition is given which is sufficient to prove that such tensors are positive definite.

The condition in the square brackets should be arbitrary as in a[a<1] = 0 . Is there a way to realize this "conditional assignment" (for lack of ...

The tensors condition, x, y must be broadcastable. Note Currently valid scalar and tensor combination are 1. Scalar of floating dtype and torch.double 2. Scalar of integral dtype and torch.long 3. Scalar of complex dtype and torch.complex128 Parameters condition ( BoolTensor) – When True (nonzero), yield x, otherwise yield y