Autoencoder_bits/decoder.py at main · ngmsoftware/Autoencoder_bits · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 20 13:03:16 2018

@author: ninguem
"""

import sys
import os

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

tf.executing_eagerly()

BATCH_SIZE = 16
MAX_EPOCH = 10000


HEX = {'0':[0,0,0,0], '1':[1,0,0,0], '2':[0,1,0,0], '3':[1,1,0,0], '4':[0,0,1,0], '5':[1,0,1,0], '6':[0,1,1,0], '7':[1,1,1,0], '8':[0,0,0,1], '9':[1,0,0,1], 'A':[0,1,0,1], 'B':[1,1,0,1], 'C':[0,0,1,1], 'D':[1,0,1,1], 'E':[0,1,1,1], 'F':[1,1,1,1] }


X_train = np.ndarray((0,28,28,1))
Y_train = np.ndarray((0,4))

files = os.listdir('./digits/imgs2')
for file in files:
    name, ext = file.split('.')
    digit, _ = name.split('-')

    if (ext == 'png'):

        _I = 1.0 - plt.imread('./digits/imgs2/'+file).reshape((1,28,28,1))

        X_train = np.concatenate((X_train, _I), 0)

        Y_train = np.concatenate((Y_train, np.array(HEX[digit]).reshape(1,4)), 0)


compiled = False


NTrain = X_train.shape[0]


modelDecoder = tf.keras.models.Sequential()
modelDecoder.add( tf.keras.layers.Input(4))
modelDecoder.add(tf.keras.layers.Dense(28*28*1, activation='sigmoid'))
modelDecoder.add(tf.keras.layers.Reshape((28, 28, 1)))
modelDecoder.add( tf.keras.layers.Conv2D(16, kernel_size=(8,8), activation='relu', padding='same') )
modelDecoder.add( tf.keras.layers.Conv2D(1, kernel_size=(1,1), activation='sigmoid', padding='same') )


# 8. Compile model
modelDecoder.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])


# 9. Fit model on training data
modelDecoder.fit(Y_train, X_train, batch_size=BATCH_SIZE, epochs=MAX_EPOCH, verbose=1)


for digitHex, digitsBin in zip(HEX.keys(), HEX.values()):

   plt.figure()

   features = np.array(digitsBin).reshape((1,4));

   IHat = modelDecoder.predict(features).reshape((28,28))

   plt.imshow(IHat)

   # plt.subplot(2,2,1)
   # plt.imshow(I)
   # plt.subplot(2,2,2)
   # plt.imshow(IHat);
   # plt.subplot(2,1,2)
   # plt.imshow(features);

   plt.pause(0.01)