zundoko-flow.py

# coding:utf-8

import tensorflow as tf
from random import choice

zun = -1.0
doko = 1.0
zun_length = 4
zundoko_length = zun_length + 1

def make_zundoko_list(length):
    return [choice([zun, doko]) for _ in range(length)]

def solve_zundoko_list(zundoko_list, index=0, zun_count=0):
    if len(zundoko_list) == 0:
        return index - zun_length
    elif zun_count >= zun_length and zundoko_list[0] == doko:
        return index - zun_length
    else:
        return solve_zundoko_list(zundoko_list[1:], index + 1, zun_count + 1 if zundoko_list[0] == zun else 0)

def make_solved_zundoko_list(length, answer):
    zundoko_list = make_zundoko_list(length)
    while True:
        solved_answer = solve_zundoko_list(zundoko_list)
        if solved_answer >= answer:
            break
        zundoko_list[solved_answer] = doko
    if answer + zundoko_length <= length:
        zundoko_list[answer:answer + zundoko_length] = [zun for _ in range(zun_length)] + [doko]
    return zundoko_list

def dense_to_one_hot(index, num_classes):
    return [(1.0 if i == index else 0.0) for i in range(num_classes)]

list_length = 100
num_classes = list_length - zundoko_length + 1 + 1

zundoko_lists = [make_solved_zundoko_list(list_length, i % num_classes) for i in range(100000)]
zundoko_answers = [dense_to_one_hot(solve_zundoko_list(z), num_classes) for z in zundoko_lists]

test_zundoko_lists = [make_zundoko_list(list_length) for _ in range(1000)]
test_zundoko_answers = [dense_to_one_hot(solve_zundoko_list(z), num_classes) for z in test_zundoko_lists]

x = tf.placeholder(tf.float32, [None, list_length])
x_reshaped = tf.reshape(x, [-1, list_length, 1, 1])
W1 = tf.Variable(tf.truncated_normal([5, 1, 1, 1], stddev=0.1))
b1 = tf.Variable(tf.truncated_normal([1], stddev=0.1))
h1_reshaped = tf.nn.relu(tf.nn.conv2d(x_reshaped, W1, strides=[1, 1, 1, 1], padding='SAME') + b1)
h1 = tf.reshape(h1_reshaped, [-1, list_length])

W2 = tf.Variable(tf.truncated_normal([list_length, num_classes], stddev=0.1))
b2 = tf.Variable(tf.truncated_normal([num_classes], stddev=0.1))
y = tf.nn.softmax(tf.matmul(h1, W2) + b2)

y_ = tf.placeholder(tf.float32, [None, num_classes])

cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))

train_step = tf.train.GradientDescentOptimizer(1e-5).minimize(cross_entropy)

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

for i in range(1000):
    sess.run(train_step, feed_dict={x: zundoko_lists, y_: zundoko_answers})

zundoko_list = make_zundoko_list(list_length)
zundoko_answer = sess.run(answer, feed_dict={x: [zundoko_list]})[0]
zundoko_string_list = ['ズン' if zundoko == zun else 'ドコ' for zundoko in zundoko_list]
zundoko_string_list = zundoko_string_list[:min(zundoko_answer + zundoko_length, len(zundoko_string_list))]
for zundoko_string in zundoko_string_list:
    print(zundoko_string)
if zundoko_answer + zundoko_length == len(zundoko_string_list):
    print('キ・ヨ・シ！')