Learning tic-tac-toe

Recipe 41

What and how do you learn in the first place? ?? : angel:

The learner is defined by 9 (model variables and operations ...) ~ 11 (loss function ...). 4 (using coordinate transformation ...) ~ 8 (one from the training set ...) is the preparation and making the training set (Isn't it too long and complicated to prepare?) Does base_tic_tac_toe_moves.csv on github omit the symmetric aspect?

In summary, the learner itself is normal,

Input x: Phase information (one-dimensional list with 9 elements) layer1 = sigmoid(A1x+b1) layer2 = A2layer1+b2 Teacher label y: Best move (any of integers 0-9) (How do you define that best move (unverified)) loss: hoge entropy of layer2 and y

Actually, some data is put into the learner as a set (it feels like that)

Mal

board[::-1]

Sort the list in reverse order. Reference 1: baby:

list(zip(*[board[6:9], board[3:6], board[0:3]]))

Nanikore is long. Also, isn't "\ *" used for variable length arguments? ?? : construction_worker: Reference 2 ~ 4 It seems that a is expanded by adding an asterisk "\ *" like * a.

I tried various things (I think the Lord could understand this)

Let's take a look at the list (zip (* [board [6: 9], board [3: 6], board [0: 3]])) step by step.

To make it easier to see (... ☆)

def print_board(board): #The argument is a one-dimensional list
    print(' ' + str(board[0]) + ' | ' + str(board[1]) + ' | ' + str(board[2]))
    #print('__________')
    print(' ' + str(board[3]) + ' | ' + str(board[4]) + ' | ' + str(board[5]))
    #print('__________')
    print(' ' + str(board[6]) + ' | ' + str(board[7]) + ' | ' + str(board[8]))
    print('\n')

def make_board(board_2d): #Convert a two-dimensional list to one-dimensional
    return [value for item in board_2d for value in item]
    '''
    board_1d = []
    for item in board_2d:
        for value in item:
            board_1d.append(value)
    return board_1d
    '''

return [value for item in board_2d for value in item]

See ☆ above: man_with_turban: Can be rewritten with a for statement (although it will be longer)

[PYTHON] TensorFlow Machine Learning Cookbook Chapter 6 (or rather, tic-tac-toe)

reference