Keras 损失值非常高且不减少

Question

Firstly, I know that similar questions have been asked before, but mainly for classification problems. Mine is a regression-style problem.

I am trying to train a neural.network using keras to evaluate chess positions using stockfish evaluations. The input is boards in a (12,8,8) array (representing piece placement for each individual piece) and output is the evaluation in pawns. When training, the loss stagnates at around 500,000-600,000. I have a little over 12 million boards + evaluations and I train on all the data at once. The loss function is MSE.

This is my current code:

model = Sequential()
model.add(Dense(16, activation = "relu", input_shape = (12, 8, 8)))
model.add(Dropout(0.2))
model.add(Dense(16, activation = "relu"))
model.add(Dense(10, activation = "relu"))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(1, activation = "linear"))
model.compile(optimizer = "adam", loss = "mean_squared_error", metrics = ["mse"])
model.summary()
# model = load_model("model.h5")

boards = np.load("boards.npy")
evals = np.load("evals.npy")
perf = model.fit(boards, evals, epochs = 10).history
model.save("model.h5")
plt.figure(dpi = 600)
plt.title("Loss")
plt.plot(perf["loss"])
plt.show()

This is the output of a previous epoch:

145856/398997 [=========>....................] - ETA: 26:23 - loss: 593797.4375 - mse: 593797.4375

The loss will remain at 570,000-580,000 upon further fitting, which is not ideal. The loss should decrease by a few more orders of magnitude if I am not wrong.

What is the problem and how can I fix it to make the model learn better?

Answer 1

I would suspect that your evaluation data contains very big values, like 100000 pawns if one of sides forcefully wins. Than, if your model predicts something like 0 in the same position, then squared error is very high and this pushes MSE high as well. You might want to check your evaluation data and ensure they are in some limited range like [-20..20].

Furthermore, evaluating a chess position is a very complex problem. It looks like your model has too few parameters for the task. Possible improvements:

• Increase the numbers of neurons in your dense layers (say to 300, 200, 100).
• Increase the numbers of hidden layers (say to 10).
• Use convolutional layers.

Besides this, you might want to create a simple "baseline model" to better evaluate the performance of your neural.network. This baseline model could be just a python function, which runs on input data and does position evaluation based on material counting (like bishop - 3 pawns, rook - 5 etc.) Than you can run this function on your dataset and see MSE for it. If your neural.network produces a smaller MSE than this baseline model, than it is really learning some useful patterns.

I also recommend the following book: "Neural Networks For Chess: The magic of deep and reinforcement learning revealed" by Dominik Klein. The book contains a description of.network architecture used in AlphaZero chess engine and a neural.network used in Stockfish.