Solving Sudoku with Convolution Neural Network Keras

Then we apply softmax function on the final scores to convert them into probabilities. And the data is classified into a class that has the highest probability value(refer to the following image). But in sudoku, the scenario is different. We have to get 81 numbers for each position in the sudoku game, not just one. And we have a total of 9 classes for each number because a number can fall in a range of 1 to 9. To comply with this design, our network should output (81*9) numbers.