So, as I was suspecting, the tensor trace may not be terribly useful in ML, at least, for tensors with more than 2 dimensions.īy the way, in the paper A Survey on Tensor Techniques and Applications in Machine Learning (2019) Yuwang Ji et al. If you executed the following code, you should get an error that tells you that's not possible.
In fact, PyTorch does not seem to implement the trace for tensors, but only matrices.
I think this definition is easy to understand, but I don't remember having ever used it, but I could be wrong. They give these examples x = tf.constant(, ]) So, essentially, for each most inner 2d matrix in this tensor, you compute the trace (for that matrix), then return the result as another tensor, which has 2 fewer dimensions than the original tensor (because a matrix has 2 dimensions, and, by computing the tensor of a matrix, you reduce a matrix to a number, which is a 0-dimensional tensor). If x is of rank k with shape, then output is a tensor of rank k-2 with dimensions where output = trace(x). Trace(x) returns the sum along the main diagonal of each inner-most matrix in x. For completeness, let me write here their definition of the trace.
Define itrace archive#
The first definition is provided by the TensorFlow implementation of the trace of a tensor. LKML Archive on help / color / mirror / Atom feed Make perf script easier to use for itrace 22:01 Andi Kleen 22:01 PATCH v2 01/10 perf tools: Report itrace options in help Andi Kleen (11 more replies) 0 siblings, 12 replies 20+ messages in thread From: Andi Kleen 22:01 UTC ( / raw) To: acme +Cc: jolsa, linux-kernel. I found at least 2 (different) definitions of tensor trace in machine learning. I don't know if that definition is consistent with the definition(s)/implementation(s) of trace used in machine learning. The concept of trace, in mathematics, is apparently known as tensor contraction. * Addressing mode handling.The concepts of trace and tensor also appear in other contexts outside of machine learning (ML), like quantum computing, so an answer to your question may be given independently of ML, but that may not be useful, as these concepts may be defined and implemented differently in the context of ML, which seems to be the case. * allows separate handling of addressing mode and actual operation, saving * I break instructions up according to the writeup there. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES LOSS OF USE, DATA, OR PROFITS OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Define itrace software#
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Define itrace code#
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