tensorflowで九九

概要

tensorflowで九九、やってみた。

結果

0.395087
0.0249247
0.00668623
0.00196402
0.000861049
0.000167826
0.000323401
4.22531e-05
1.31142e-05
0.000113024
      1   2   3   4   5   6   7   8   9
  1   1   2   3   4   5   6   7   8   9
  2   2   4   6   8  10  12  14  16  18
  3   3   6   9  12  15  18  21  24  27
  4   4   8  12  16  20  24  28  32  36
  5   5  10  15  20  25  30  35  40  45
  6   6  12  18  24  30  36  42  48  54
  7   7  14  21  28  35  42  49  56  63
  8   8  16  24  32  40  48  56  64  72
  9   9  18  27  36  45  54  63  72  81

サンプルコード

import numpy as np
import tensorflow as tf
#kuku

def in_encode(i, j):
    k = j * 16 + i
    return np.array([k >> d & 1 for d in range(8)])

def out_encode(i, j):
    k = j * i
    return np.array([k >> d & 1 for d in range(7)])

def decode(p):
    f = 0
    if p[0] > 0.5:
        f += 1
    if p[1] > 0.5:
        f += 2
    if p[2] > 0.5:
        f += 4
    if p[3] > 0.5:
        f += 8
    if p[4] > 0.5:
        f += 16
    if p[5] > 0.5:
        f += 32
    if p[6] > 0.5:
        f += 64
    return f

trX = np.array([in_encode(i, j) for i in range(1, 10) for j in range(1, 10)], dtype = np.float32)
trY = np.array([out_encode(i, j) for i in range(1, 10) for j in range(1, 10)], dtype = np.float32)

g = tf.Graph()

with g.as_default():
    x = tf.placeholder(tf.float32, shape = (None, 8))
    h1 = tf.layers.dense(x, 96, activation = tf.nn.relu)
    y_answer = tf.placeholder(tf.float32, shape = (None, 7))
    y_pred = tf.layers.dense(h1, units = 7)
    loss = tf.losses.mean_squared_error(labels = y_answer, predictions = y_pred)
    optimizer = tf.train.AdamOptimizer(0.01)
    train = optimizer.minimize(loss)

with tf.Session(graph = g) as session:
    session.run(tf.global_variables_initializer())
    dic = { 
        x: trX, 
        y_answer: trY
    }
    for i in range(1000):
        _, loss_value = session.run((train, loss), dic)
        if i % 100 == 0:
            print (loss_value)
    #print ('prediction:', session.run(y_pred, dic))
    y = session.run(y_pred, dic)
    p = '    '
    j = 1
    for i in range(1, 10):
        p += '%3d ' % (i * j)
    p += '\n'
    for j in range(1, 10):
        p += '%3d ' % (j)
        for i in range(1, 10):
            g = y[(i - 1) * 9 + (j - 1)]
            k = decode(g)
            p += '%3d ' % (k)
        p += '\n'
    print (p)

以上。