rank•Applying the usual procedure to obtain this in another frame we obtain so that transforms as arank2 tensor.•So I goes to the cabrank, and gets up on the box.•The position of Secretary of State holds Cabinetrank.•This hot work, however, had left the Confederateranksbadly ...
This is what we see in this example, but this idea generalizes. The rank of a tensor tells us how many axes a tensor has, and the length of these axes leads us to the very important concept known as the shape of a tensor.
For the example of the Kerr-NUT-AdS spacetime () with its principal Killing-Yano tensor (), these constants and the constants from the k Killing vectors give D independent constants in involution, making the geodesic motion completely integrable (). The constants of motion are also related to...
"""x_static_shape = x.get_shape()ifx_static_shape.ndimsisnotNoneandx_static_shape.ndims <2:raiseValueError("Expected input tensor %s to haverankat least 2, but saw shape: %s"% (x, x_static_shape)) x_rank = tf.rank(x) x_t = tf.transpose( x, tf.concat( ([ 1,0], tf.ran...
関連するトピック:Plants,Gardeningrank3adjective1if something is rank, it has a very strongunpleasantsmellrank smell/odourthe rank odour of sweat and urine2[only before noun]used toemphasizea bad orundesirablequality類義語totalan example of this government’s rank stupidityThey make us look like...
We use the Lie algebra defined in Example 2 to illustrate the third calling sequence. We calculate the vector in the span of e1, e2, e3 whose adjoint matrix, restricted to e1, e2, e3, e4, has rank 1.alg > Rank1Elements([e1, e2, e3], [e1, e2, ...
Example 2: When `a` and `b` are matrices (order 2), the case `axes = [[1], [0]]` is equivalent to matrix multiplication. Example 3: Suppose that \\(a_{ijk}\\) and \\(b_{lmn}\\) represent two tensors of order 3. Then, `contract(a, b, [[0], [2]])` is the ...
device =local_rank*2+ (iter +local_rank) %2tensor = tensor.cuda(device).type(dtype) multiplied = tensor * size hvd.allreduce_(tensor, average=False) max_difference = tensor.sub(multiplied).max()# Threshold for floating point equality depends on number of# ranks, since we're comparing ...
The current development of LRTR can be classified into two categories: low-rank tensor decomposition (LRTD) and tensor rank minimization. Given a tensor Y∈RI1×I2×⋯×IN, taking Tucker decomposition as an example, the LRTD can decompose Y into a core tensor C∈Rr1×r2×⋯×rN and...
Tensor rank decomposition