Please be reassured that all Sudoku puzzles published in the Times are always solvable by logical means. So, as we increase the difficulty Super Fiendish, the time has come to present the essential techniques for solving the most difficult Sudoku puzzles. Many have welcomed the new challenge but some have asked for help. In November 2006, The Times introduced a new extreme level of Sudoku giving players a more difficult level of logic: Super Fiendish. Here, there are no dense layers and conv layers are heavy(filters).Advanced solving techniques As seen on Times Online in February 2007 The model was trained using Adam optimizer with a learning rate 0.001 and SCCE loss function. Here, most of the parameters are contributed by dense layers and conv layers are light weight.īatch_normalization (BatchNo (None, 9, 9, 81) 324īatch_normalization_1 (Batch (None, 9, 9, 81) 324īatch_normalization_2 (Batch (None, 9, 9, 81) 324Ĭonv2d_3 (Conv2D) (None, 9, 9, 162) 13284īatch_normalization_3 (Batch (None, 9, 9, 162) 648 The following sections shows the overall summary of the model and their training results. Max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64) 0Ī heavy dense and heavy conv model was trained using the the same dataset. The model was trained using Adam optimizer with a learning rate 0.001 ~ 0.000001 and SCCE loss function.Ĭonv2d_5 (Conv2D) (None, 28, 28, 64) 18496 The puzzle image should not contain marks, stains or unnecessary patterns.The puzzle should be in printed format eg.: paper or screen.It should be a close-up image of the puzzle from a flat surface.The images should not be blurry or shaky.The input puzzle should be a grayscale or rgb image.The algoritm takes N iterations for solving the entire puzzle, where N represenets the number of unfilled positions. Repeat the iterations with modified input(i.e 'puzzle'), until all elements are filled (ie.Now, find the maximum element(single) in 'maxp' and set the corresponding position of input with corresponding values from current output.For each filled(non-zero) element in input array we set corresponding probability in 'maxp' o -1.For each such output array, 'maxp'(9x9) contains the corresponding probability values.Each iteration produces an output array of 9x9 with numbers 1.9 (i.e 'out').Zeros represents the blank spaces in the original puzzle.The input is a sudoku matrix of 9x9 with numbers 0.9 as input(i.e 'puzzle').So, we follow a iterative approach of feeding the partial solution of one iteration as input to next iteration. The numpy dataset used for training was created by combining the following two datasets in csv formats.Ī single iteration of the model, as such does not seem to produce correct results for all the positions in the input. The inputs for this model contains 9x9 arrays of integers representing the puzzles, such that zeros represent the unfilled positions. The sudoku solver model was trained using a dataset of 10 million puzzles. The digit recognition model was trained using the entire SVHN dataset(train, test and extra) in grayscale mode. The input sudoku puzzles are assumed to be images of printed version of the puzzle. We use tensorflow-keras library for training(prediction) the neural network and opencv library for image processing. Verify the solution and plot the resuts on the input image.Predict solution using neural network in an iterative manner.Predict numbers from image crops using neural network.Crop ROI's containing digits from the grid.Preprocess the input image and remove the background.The pipeline for the solution consists of the following steps. Here our input is an image of sudoku puzzle and we need to produce a corresponding output image by filling the remaining positions of the input. We have to fill up the remaining positions such that each row, columns and 3x3 sub grids contains numbers 1.9, without repeatation. The classic sudoku is a number placing puzzle game with a grid of 9 rows and 9 columns, partly filled with numbers 1.9. Solving Sudoku Puzzle Using Neural Network Solving Sudoku Puzzles With Computer Vision And Neural Networks
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