matrix-multiplication

问题 I am trying to us tensorflow operations within a keras model and I am quite confused about the mechanism and what Lambda layers do to tf tensors. So this works: a = keras.layers.Input(shape=[1, 2], dtype='float', name='a') s= keras.layers.Lambda(lambda x: tf.transpose(tf.transpose(x)))(a) model = keras.models.Model(inputs=a, outputs=s) but this does not work: a = keras.layers.Input(shape=[1, 2], dtype='float', name='a') s = tf.transpose(tf.transpose(a)) s = keras.layers.Lambda(lambda x: x)(s)

问题 I have a 2D Matrix Multiplication program using the following kernel: __global__ void multKernel(int *a, int *b, int *c, int N) { int column = threadIdx.x + blockDim.x * blockIdx.x; int row = threadIdx.y + blockDim.y * blockIdx.y; int index = row * N + column; if(column < N && row < N) { c[index] = a[index] * b[index]; } } Now, I'd like to create a 3D matrix multiplication kernel, but I'm having trouble finding examples of how do create one (also, I'm terrible at reading mathematical formulae

问题 I have a numerical computation problem which requires solving nonlinear equations (with long integers) in multiple precision. I tried an MPFR C++ wrapper from this link by Pavel: mpfr C++ wrapper by Pavel The wrapper can be downloaded here: mpfrc++-3.5.6.zip However, there is precision loss in the solution when handling very long integers (equations with small integers worked well). I tried three options as in the sample code below: to use the code immediately does not work with "constant

问题 so I am having an issue multiplying two 2d arrays together. I am pretty sure that matrixA and matrixB are being stored properly, they are displayed properly when the program is ran. When I try to multiply the 2 arrays together I am getting a long string of a lot of 1's and 0's. Any idea on what could be the problem? Here is my code: #include <stdio.h> #include<conio.h> #include <stdlib.h> #include <iostream> /* Routines called. */ int loadMatrixFromFile(char *filename, int *data); void

问题 I'm wondering how to get functionality similar to numpy.einsum in Julia. Specifically, I have a 3rd order tensor that I'm looking to multiply by a 2nd tensor (matrix), contracting both of the dimensions to yield a 1st order tensor (vector). Currently, I'm using PyCall so that I can use the numpy.einsum function like so: using PyCall @pyimport numpy as np a = rand(5,4,3) b = rand(5,4) c = np.einsum("ijk,ij", a,b) size(c) == (3,) It feels kind of silly to rely on calling python in order to do

问题 I'm trying to multiply every element in a small matrix (let's say 2x2) with every position in a big matrix (let's say 4x4), element by element. So I want: 1 2 3 4 1 0 3 0 1 0 1 2 3 4 0 0 0 0 0 0 'x' 1 2 3 4 = 1 0 3 0 1 2 3 4 0 0 0 0 The small matrix is applied as many times as it fits, and the multiplication is element by element. I've tried a bunch of loops, but that doesn't feel right in MATLAB, there must be prettier ways of doing it? 回答1: One possibility is to use repmat to repeat the

问题 I'm trying to figure out if there's a native way of obtaining a certain kind of element-wise product of two matrices in Matlab. The product that I'm looking for takes two matrices, A and B say, and returns there product C , whose elements are given by C(i,j,k) = A(i,j)*B(j,k) Naturally, the number of columns of A is assumed be the same as the number of rows of B . Right now, I'm using the following for-loop (assuming size(A,2)==size(B,1) is true). First, I initialize C : C = zeros(size(A,1),

问题 I am multiplying two matrices using two threads (however, the program is written to scale up as well, so I could possibly use three, four, etc threads instead). Each thread calculates/does the work for one row (or column) of the final matrix. If one thread is doing work on a row, the other one(s) should not work on that row. It/they should move on to the next available row. First of all, I am not certain if the way I implemented the problem is correct. If you can see a better way, please let

问题 I'm trying to use matrix algebra with the purpose of manipulating strings. This means being able to multiple matrix-like structures using concatenation and pasting of string or of arrays of strings. I previously tried to implement the thing on R, but it was not possible as matrices can have only one dimensional entries. I hope to be enough language-agnostic and abstract, but I'll use R-like code for sake of clarity. I should make explicit that I don't require real matrices, but matrix-like

问题 I am failing to appropriately work with a Symbolic Matrix in R, with both rSymPy and Ryacas. In Matlab this is easy. I am looking for suggestions how to do this in R and get a similar output as in Matlab . To do this in Matlab I need the 'Symbolic Toolbox'. In this example I wish to generate a Symbolic transition probability matrix "P", which is say 5 x 5, and has elements P11, P12,..., P55. I then want to use this matrix for multiplication (with itself and other matrices) and potentially