I’m trying to build a very basic model for Chess. What I’m doing is simply use the mnist tutorial and adapt it for this. I have a large collection of games with the end result for each game (win, loss, or draw). What I have done is take the final board position of each game and treat it as a 8x8 data element (empty square is 0 and each piece gets a different number). There are only 3 labels - 0, 1, and 2 (for loss, win, and draw). I have used about 150000 positions for this test.
When I train this data with the following model:
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=[64, 1]),
tf.keras.layers.Dense(32, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(3, activation=tf.nn.softmax)
])
The accuracy is quite low. It gets up to 0.58 using 5 epochs. Using the same model with mnist data gets close to 100% accuracy).  Are there any issues with this approach? Is there something I’m missing? Any thoughts or advice is greatly appreciated.