In this paper, the evolutionary nerve, one of the widely used artificial intelligence algorithms convolution in convolutional neural networks) you will encode its operation as a function. Convolution process two matrices (filter and data matrix) is an operation performed on it. As a result, a new matrix (result matrix) is formed. Evolution of existing filter coefficients with another data matrix during the convolution process he must enter. During evolution, the filter Matrix corresponds to the data it is multiplied with the elements of its matrix, and the sum of these values is taken. Filter matrix every time a new value is calculated by shifting to the left/down on the data matrix. This value creates an element of the result matrix. During this homework, you must read the data and filter matrix from a file. For example, the file format must be as follows (places in the form# are written for an explanation, normally it should not be in the file). File name data. It has to be txt. 8 # number of rows of the data matrix 4 # Data Matrix column number 0.5 # Data Matrix first value 0.12 # Data Matrix second value . . . 0.92 # data matrix thirty-second value 3 # filter Matrix number of rows/columns 0.5 # filter Matrix first value 0.12 # filter Matrix second value . . . 0.92 # filter Matrix ninth value After the program reads the file, make the convolution process on the matrices and make the result it should print on the screen. The following image shows an example convolution process. As seen in the picture each a 3x3 part of a (filter Matrix size) that is a data matrix (image) at a time) it enters into convolution with the filter Matrix. The filter matrix here as multiplicand in the sari part As Seen [1 0 1; 0 1 0; 1 0 1] dir. Each time the filter shifts to the left and the result a new value of its matrix (convolved feature) is formed. At saga after slipping the filter continues again from the bottom line.

B 0 1 1 0 1 3 1 0 O 1 x1 1 4 0 I OK 1 1 E 1 x1 1 1 0 1 O 1 0 0 0 1 1 1 0 (x 0 1 x1 T Ox 1 WO 1 MD OX Ox 1 0 1 1 0 1 0 0 * 1 0 Ox 1 1 xl 1 MO 1 * x 1 0 0 1 1 0 0 0 1 1 0 0 0 D 1 1 0 Convolved feature Convolved feature Convolved feature 0 1 1 O 0 0 1 1 o 0 1 1 1 O Image Image Image D E F 1 1 1 0 0 4 E 4 1 1 1 0 0 4 E 4 4 1 1 o 4 E 4 1 0 0 1 2 2 0 0 1 I 0 0 *1 0 1 1 0 2 1 1 x1 0 4 2 E Tx T Ox O 1 1 0 0 1 1 1 1 0 1 1 0 x1 0 OK O OK OK O 0 1 1 0 0 MO 0 0 1 0 1 1 OK OK 1 *0 0 TX Convolved feature Convolved feature Convolved feature 0 1 1 O 0 0 0 1 1 o 0 0 1 1 o O Image Image Image G H 1 1 1 0 0 3 1 1 1 0 0 0 3 1 1 1 1 4 3 3 4 0 1 1 1 0 2 2 3 D 1 1 1 0 2 3 3 1 1 1 2 2 3 3 1 1 1 2 0 1 1 1 2 2 0 E 0 o 1 1 0 Ox 2 0 Ex * 1 0 3 0 T 0 0 1 0 1 0 Ox O x0 1 xl 0 o 1 1 1 x1 Ox Convolved feature Convolved feature Convolved feature O 1 0 1 0 1 MO 0 1 0 1 20 0 TX 0 MO TX Image Image Image