Pythonでニューラルネット（AutoEncoderで「要約/次元圧縮」してみる） #Python3

　前回記事
で作った、ニューラルネットで、AutoEncoderを試してみる。

ここで言うAutoEncoderは、入力と出力が同じ値となる、ニューラルネット。

８個の入力　→　３個の隠れ層　→　８個の出力

で、00000001、00000010、00000100、・・・10000000を学習させる。

以下結果。
左のブロックが入力、中が隠れ層、右が出力。
（学習の結果入力と出力が一致している）

[入力]                     [隠れ層]              [出力]
0  0  0  0  0  0  0  1  - 0.00  0.99  0.01   - 0.00  0.00  0.00  0.00  0.00  0.00  0.00  1.00   + 
0  0  0  0  0  0  1  0  - 0.99  0.97  0.00   - 0.00  0.00  0.00  0.00  0.00  0.00  1.00  0.00   + 
0  0  0  0  0  1  0  0  - 0.00  0.91  1.00   - 0.00  0.00  0.00  0.00  0.00  1.00  0.00  0.00   + 
0  0  0  0  1  0  0  0  - 0.00  0.02  0.00   - 0.00  0.00  0.00  0.00  1.00  0.00  0.00  0.00   + 
0  0  0  1  0  0  0  0  - 0.99  0.00  0.98   - 0.00  0.00  0.00  1.00  0.00  0.00  0.00  0.00   + 
0  0  1  0  0  0  0  0  - 0.90  0.01  0.00   - 0.00  0.00  1.00  0.00  0.00  0.00  0.00  0.00   + 
0  1  0  0  0  0  0  0  - 0.00  0.00  0.86   - 0.00  1.00  0.00  0.00  0.00  0.00  0.00  0.00   + 
1  0  0  0  0  0  0  0  - 1.00  1.00  1.00   - 1.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00   +

★ここで重要なのが、隠れ層。
８種の入力に対し、以下のような値を持っている。
これはあたかも、２進法の0〜７（２の３乗（＝８））の表現を獲得しているよう。
→　AutoEncoderによる学習後、隠れ層を取り出すことで、
「要約/次元圧縮」が可能になることの一例。

※以下ソース（python3）

autoencoder.py

import random
import math

iINPUT = 8
iHIDDEN = 3
iOUTPUT = 8

iPR = 5000
iMAX_T = 500000
dETA = 2.5
dEPS = 0.000005
dALPHA = 0.92
dBETA = 0.35
dW0 = 0.9

xi = [0 for i in range(iINPUT +1)] 
v =  [0 for i in range(iHIDDEN+1)] 
o =  [0 for i in range(iOUTPUT)]
zeta =  [0 for i in range(iOUTPUT)] 

#------------
def arand():
    r = random.random()
    r = r * 2 *dW0 - dW0
    return r
#------------

w1 = [[arand() for i in range(iINPUT+1)]  for i in range(iHIDDEN)]
w2 = [[arand() for i in range(iHIDDEN+1)] for i in range(iOUTPUT)]

d_w1 = [[0 for i in range(iINPUT+1)]  for i in range(iHIDDEN)]
d_w2 = [[0 for i in range(iHIDDEN+1)] for i in range(iOUTPUT)]

pre_dw1 = [[0 for i in range(iINPUT+1)]  for i in range(iHIDDEN)]
pre_dw2 = [[0 for i in range(iHIDDEN+1)] for i in range(iOUTPUT)]

iPATTERNz =8

#入力する値              
data =  [[0,0,0,0,0,0,0,1],\
         [0,0,0,0,0,0,1,0],\
         [0,0,0,0,0,1,0,0],\
         [0,0,0,0,1,0,0,0],\
         [0,0,0,1,0,0,0,0],\
         [0,0,1,0,0,0,0,0],\
         [0,1,0,0,0,0,0,0],\
         [1,0,0,0,0,0,0,0]]
for i in range(iPATTERNz):
    data[i].append(1)

#教師信号（所謂、模範解答）
t_data = data

iPATTERNo =8

#お試し用の問題
d_data =  [[0,0,0,0,0,0,0,1],\
           [0,0,0,0,0,0,1,0],\
           [0,0,0,0,0,1,0,0],\
           [0,0,0,0,1,0,0,0],\
           [0,0,0,1,0,0,0,0],\
           [0,0,1,0,0,0,0,0],\
           [0,1,0,0,0,0,0,0],\
           [1,0,0,0,0,0,0,0]]
for i in range(iPATTERNo):
    d_data[i].append(1)

##-----------------------------

def dw_init():
    global d_w1
    global d_w2
    global pre_dw1
    global pre_dw2
    pre_dw1 = d_w1
    pre_dw2 = d_w2
    d_w1 = [[0 for i in range(iINPUT+1)]  for i in range(iHIDDEN)]
    d_w2 = [[0 for i in range(iHIDDEN+1)] for i in range(iOUTPUT)]

def sigmoid(u):
    return  1.0 / (1.0 +  math.exp(-dBETA * u));

def xi_set(p):
    global xi
    global zeta
    xi = data[p]
    zeta = t_data[p]

def forward():
    global xi
    global w1
    global v
    global w2
    global o
    for j in range(iHIDDEN):
        sm = 0
        for k in range(iINPUT+1):
            sm += xi[k] * w1[j][k]
        v[j] = sigmoid(sm)
    v[iHIDDEN] = 1.0
    for j2 in range(iOUTPUT):
        sm = 0
        for j in range(iHIDDEN+1):
            sm += v[j] * w2[j2][j]
        o[j2] = sigmoid(sm)

def backward():
    global o
    global zeta
    global v
    global xi
    global d_w1
    global d_w2
    global w2

    delta2 = [0 for i in range(iOUTPUT)]
    delta1 = [0 for i in range(iHIDDEN+1)]
    for i in range(iOUTPUT):
        delta2[i] = dBETA * o[i] * (1-o[i]) * (zeta[i]-o[i])
    for j in range(iHIDDEN):
        sm = 0
        for j2 in range(iOUTPUT):
            sm = sm + w2[j2][j] * delta2[j2]
            delta1[j] = dBETA * v[j] * (1-v[j]) * sm
    for j2 in range(iOUTPUT):
        for j in range(iHIDDEN+1):
            d_w2[j2][j] = d_w2[j2][j] + delta2[j2] * v[j]
    for j in range(iHIDDEN):
        for k in range(iINPUT+1):
            d_w1[j][k] = d_w1[j][k] + delta1[j] * xi[k]

def w_modify():
    for j2 in range(iOUTPUT):
        for j in range(iHIDDEN+1):
            d_w2[j2][j] = dALPHA * dETA * d_w2[j2][j] + dALPHA * pre_dw2[j2][j]
            w2[j2][j] = w2[j2][j] + d_w2[j2][j]
    for j in range(iHIDDEN):
        for k in range(iINPUT+1):
            d_w1[j][k] = dALPHA * dETA * d_w1[j][k] + dALPHA * pre_dw1[j][k]
            w1[j][k] = w1[j][k] + d_w1[j][k]

def calc_error():
    e = 0.0
    for i in range(iOUTPUT):
        e = e +(zeta[i]-o[i])*(zeta[i]-o[i])
    return e

def back_propagation_main():
    global zeta
    e = 0.0
    esum = 0.0
    for t in range(iMAX_T):
        dw_init()
        esum = 0.0
        for p in range(iPATTERNz):
            xi_set(p)
            forward()
            backward()
            esum = esum + calc_error()
        w_modify()
        e = esum #/ (iOUTPUT * iPATTERNz)
        if t % iPR ==0:
            print(t, " / ", iMAX_T, " / ", "{0:.7f}".format(e))
        if(e<dEPS):
            break

def tryTest():
    global xi
    global o
    print("-------------------")
    for p in range(iPATTERNo):
        xi = d_data[p]
        forward()
        xi.pop()

        for i in range(iINPUT):
            print(xi[i]," ", end="")
        print("- ", end="")
        for i in range(iHIDDEN):
            print("{0:.2f}".format(v[i])," ", end="")
        print(" - ", end="")
        for i in range(iOUTPUT):
            print("{0:.2f}".format(o[i])," ", end="")
        print(" + ")

##-----------------------------


print ("---- Start ----")
back_propagation_main()
tryTest()
print("---- End ----")