Row 2136

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I've been working on building a Sudoku Solver AI. The goal is to take an unsolved Sudoku board (represented as a 1D array of length 81) as input and return a solved board (also a 1D array of length 81) as output. However, I'm encountering some issues. Here's my code:

import tensorflow as tf import numpy as np from sklearn.model_selection import train_test_split model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(81, activation="relu")) model.add(tf.keras.layers.Dense(128, activation="relu")) model.add(tf.keras.layers.Dense(128, activation="relu")) model.add(tf.keras.layers.Dense(128, activation="relu")) model.add(tf.keras.layers.Dense(81)) model.compile(optimizer="adam", loss="mse", metrics="accuracy") model = tf.keras.models.load_model("sodoku_1m_10e_adam_mse.h5") """ Soduko training data """ quizzes = np.zeros((1000000, 81), np.int32) solutions = np.zeros((1000000, 81), np.int32) for i, line in enumerate(open('sudoku.csv', 'r').read().splitlines()[1:]): quiz, solution = line.split(",") for j, q_s in enumerate(zip(quiz, solution)): q, s = q_s quizzes[i, j] = q solutions[i, j] = s quizzes = quizzes.reshape((-1, 81)) solutions = solutions.reshape((-1, 81)) x_train, x_test, y_train, y_test = train_test_split(quizzes, solutions, test_size=0.2, random_state=42) def train(model): model.fit(x_train, y_train, batch_size=32, epochs=10) def test(model): loss, accuracy = model.evaluate(x_test, y_test) print("LOSS: ", loss) print("ACCURACY: ", accuracy) def make_move(input_board): input_data = np.array(input_board).reshape(1, -1) output_data = model.predict(input_data) output_board = output_data[0] output_board = output_data[0] output_board = np.round(output_board).clip(1, 9) output_board = output_board.astype(int) return output_board

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I trained the model using the train() function, then tested it with the test() function. I thought the make\_move() function would output a solved board, but instead, I'm getting random floats. I then modified the function to output integers between 1 and 9, but the output still seems random. I realized that I haven't explicitly implemented the rules of Sudoku in any way, so even if the output was in the correct format, it might not be a valid solution. I'm not sure how to implement these rules besides repeatedly rejecting invalid boards until a valid one is generated, which doesn't seem efficient.

So the question is: What is wrong with this code? What do I need to do to fix it and make it properly solve sodoku puzzles?

FieldValue
text I've been working on building a Sudoku Solver AI. The goal is to take an unsolved Sudoku board (represented as a 1D array of length 81) as input and return a solved board (also a 1D array of length 81) as output. However, I'm encountering some issues. Here's my code: import tensorflow as tf import numpy as np from sklearn.model_selection import train_test_split model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(81, activation="relu")) model.ad…
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2023-06-29
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{
  "text": "I've been working on building a Sudoku Solver AI. The goal is to take an unsolved Sudoku board (represented as a 1D array of length 81) as input and return a solved board (also a 1D array of length 81) as output. However, I'm encountering some issues. Here's my code:\n\n    import tensorflow as tf\n    import numpy as np\n    from sklearn.model_selection import train_test_split\n    \n    \n    model = tf.keras.models.Sequential()\n    model.add(tf.keras.layers.Dense(81, activation=\"relu\"))\n    model.add(tf.keras.layers.Dense(128, activation=\"relu\"))\n    model.add(tf.keras.layers.Dense(128, activation=\"relu\"))\n    model.add(tf.keras.layers.Dense(128, activation=\"relu\"))\n    model.add(tf.keras.layers.Dense(81))\n    \n    model.compile(optimizer=\"adam\", loss=\"mse\", metrics=\"accuracy\")\n    \n    model = tf.keras.models.load_model(\"sodoku_1m_10e_adam_mse.h5\")\n    \n    \"\"\"\n    Soduko training data\n    \"\"\"\n    quizzes = np.zeros((1000000, 81), np.int32)\n    solutions = np.zeros((1000000, 81), np.int32)\n    for i, line in enumerate(open('sudoku.csv', 'r').read().splitlines()[1:]):\n        quiz, solution = line.split(\",\")\n        for j, q_s in enumerate(zip(quiz, solution)):\n            q, s = q_s\n            quizzes[i, j] = q\n            solutions[i, j] = s\n    quizzes = quizzes.reshape((-1, 81))\n    solutions = solutions.reshape((-1, 81))\n    \n    x_train, x_test, y_train, y_test = train_test_split(quizzes, solutions, test_size=0.2, random_state=42)\n    \n    \n    \n    def train(model):\n        model.fit(x_train, y_train, batch_size=32, epochs=10)\n    \n    \n    def test(model):\n        loss, accuracy = model.evaluate(x_test, y_test)\n        print(\"LOSS: \", loss)\n        print(\"ACCURACY: \", accuracy)\n    \n    \n    \n    \n    def make_move(input_board):\n        input_data = np.array(input_board).reshape(1, -1)\n    \n        output_data = model.predict(input_data)\n    \n        output_board = output_data[0]\n    \n        output_board = output_data[0]\n    \n        output_board = np.round(output_board).clip(1, 9)\n    \n        output_board = output_board.astype(int)\n    \n        return output_board\n\n​\n\nI trained the model using the train() function, then tested it with the test() function. I thought the make\\_move() function would output a solved board, but instead, I'm getting random floats. I then modified the function to output integers between 1 and 9, but the output still seems random. I realized that I haven't explicitly implemented the rules of Sudoku in any way, so even if the output was in the correct format, it might not be a valid solution. I'm not sure how to implement these rules besides repeatedly rejecting invalid boards until a valid one is generated, which doesn't seem efficient.\n\nSo the question is: What is wrong with this code? What do I need to do to fix it and make it properly solve sodoku puzzles?",
  "label": "r/tensorflow",
  "dataType": "post",
  "communityName": "r/tensorflow",
  "datetime": "2023-06-29",
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}
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