ORpKTKoVQnFhs / Public_Research (public) (License: MIT) (since 2019-12-01) (hash sha1)
This is a collection of programs related to research on my masters degree.
Clone URLs: https://rocketgit.com/user/ORpKTKoVQnFhs/Public_Research ssh://rocketgit@ssh.rocketgit.com/user/ORpKTKoVQnFhs/Public_Research git://git.rocketgit.com/user/ORpKTKoVQnFhs/Public_Research
00_simslope bite_counter master
List of commits:
Subject Hash Author Date (UTC)
Simslope Initial commit. e9ccd3d49b4b1342341f1866a3f7a941af503b30 Josiah_Mullins_Optiplex_Mini 2019-12-01 21:24:04
Commit e9ccd3d49b4b1342341f1866a3f7a941af503b30 - Simslope Initial commit.
This commit adds the files created by Dr. Hoover and his group.
At the time of this commit they are available at the address
"https://cecas.clemson.edu/~ahoover/deep-learning/".
Author: Josiah_Mullins_Optiplex_Mini
Author date (UTC): 2019-12-01 21:24
Committer name: Josiah_Mullins_Optiplex_Mini
Committer date (UTC): 2019-12-01 21:24
Parent(s):
Signing key:
Tree: 5fc2ec278269bbef9b2f977c5e13d388e3f7ddc0
File Lines added Lines deleted
00_simslope/simslope.c 135 0
00_simslope/simslope0.py 83 0
00_simslope/simslope1.py 57 0
File 00_simslope/simslope.c added (mode: 100644) (index 0000000..f97d055)
1
2 /*
3 ** Generates simulated data of two classes.
4 ** Measurements come from 1 axis.
5 ** Class 0 has a generally downard slope, class 1 has a generally
6 ** upward slope.
7 ** The NN must learn weights that correspond to the difference
8 ** in slope (printing out the weights at end makes this clear).
9 */
10
11 #include <stdio.h>
12 #include <stdlib.h>
13 #include <math.h>
14 #include <time.h>
15
16 static unsigned int rand_x[56],rand_y[256],rand_z; /* used for normal dist */
17 static int rand_j, rand_k; /* used for normal dist */
18
19
20 unsigned urand0 (void)
21 {
22 if (--rand_j == 0) rand_j = 55;
23 if (--rand_k == 0) rand_k = 55;
24 return rand_x[rand_k] += rand_x[rand_j];
25 }
26
27 void init_generators(unsigned seed)
28 {
29 int i;
30
31 /* inits the normal generator */
32 rand_x[1] = 1;
33 if(seed)
34 rand_x[2] = seed;
35 else
36 rand_x[2] = time (NULL);
37 for (i=3; i<56; ++i) rand_x[i] = rand_x[i-1] + rand_x[i-2];
38 rand_j = 24;
39 rand_k = 55;
40 for (i=255; i>=0; --i)
41 urand0 (); //run loop for a while
42 for (i=255; i>=0; --i)
43 rand_y[i] = urand0 ();
44 rand_z = urand0 ();
45
46 /* inits the uniform generator */
47 srand( (unsigned)time( NULL ) );
48 }
49
50 unsigned urand (void)
51 {
52 int i;
53
54 i = rand_z % 256;
55 rand_z = rand_y[i];
56 if (--rand_j == 0) rand_j = 55;
57 if (--rand_k == 0) rand_k = 55;
58 rand_y[i] = rand_x[rand_k] += rand_x[rand_j];
59 return rand_z;
60 }
61
62
63
64
65 /* returns a value from a normal dist with mean=0 stddev=1 */
66
67 double normal_rand (void)
68 {
69 static int flag = 0;
70 static double z, a = 2147483648.0;
71 double v1, v2, s;
72
73 if (flag) {
74 flag = 0;
75 return z;
76 }
77 flag = 1;
78 do {
79 v1 = urand()/a - 1;
80 v2 = urand()/a - 1;
81 }
82 while ((s = v1*v1 + v2*v2) > 1.0);
83 s = sqrt (-2.0 * log(s) / s);
84 z = v1 * s;
85 return v2 * s;
86 }
87
88
89
90 /* returns a value from -4...+4 */
91
92 double uniform_rand (void)
93 {
94 int randgen;
95 double ret;
96
97 randgen=rand();
98 ret=((double)randgen/(double)RAND_MAX*8.0)-4.0;
99 return ret;
100 }
101
102
103 #define DATA 1000
104 #define SAMPLES_PER_DATA 20
105
106 int main()
107
108 {
109 int i,j;
110 int d,slope;
111 double base,n,step;
112
113 init_generators(0);
114
115 for (j=0; j<DATA; j++)
116 {
117 n=normal_rand();
118 if (n < 0.0)
119 slope=-1;
120 else
121 slope=1;
122 n=normal_rand();
123 base=n*100.0+500.0;
124 printf("%d\t%d",(slope == -1 ? 0 : 1),(int)base);
125 for (d=1; d<SAMPLES_PER_DATA; d++)
126 {
127 n=normal_rand();
128 step=n*3.0+(double)slope*2; /* 3.0 => low noise, 10.0 => high noise */
129 base+=step;
130 printf("\t%d",(int)base);
131 }
132 printf("\n");
133 }
134
135 }
File 00_simslope/simslope0.py added (mode: 100644) (index 0000000..ae8e351)
1 #
2 # Normalizes the data; this was the key to getting it to work.
3 # I also taught myself a little python so I could load and
4 # manipulate the data using raw python instead of relying on numpy.
5 #
6 # fixed normalization code to calculate from "data" instead of "d"
7 #
8 # added line to save architecture, and adjusted architecture so
9 # that it will save/load correct (see README.txt)
10 #
11
12 # TensorFlow, keras, numpy
13 import tensorflow as tf
14 from tensorflow import keras
15 import numpy as np
16
17 # open file
18 f=open('simslope.txt','r')
19 d=[[int(x) for x in line.split()] for line in f]
20 f.close()
21
22 # copy to classes and data
23 classes=[]
24 data=[]
25
26 for a in range(0,len(d)):
27 classes.append(d[a][0])
28 row=[]
29 for b in range(1,len(d[a])):
30 row.append(d[a][b])
31 data.append(row)
32
33 #print(classes)
34 #print(data)
35
36 # normalize each row of data
37 normdata=[]
38 for a in range(0,len(data)):
39 norm=[]
40 s=min(data[a])
41 t=max(data[a])
42 for b in range(0,len(data[a])):
43 norm.append((float(data[a][b])-float(s))/(float(t)-float(s)))
44 normdata.append(norm)
45
46 #print(normdata)
47
48 classes=np.array(classes)
49 data=np.array(normdata)
50
51 print("data has shape", data.shape)
52 print("classes has shape ", classes.shape)
53
54 # convert classes to one hot vectors (each vector position is a class)
55 # don't need this, but left it in here in case I ever do
56 # i.e. convert classes like 1 2 3 4 to [1 0 0 0] [0 1 0 0] [0 0 1 0] [0 0 0 1]
57 # vect_classes = keras.utils.to_categorical(classes, num_classes=None)
58
59 model = keras.Sequential([
60 # cannot have InputLayer() or network will not save/load (bug in TF)
61 # instead, specify input_shape in first real layer
62 # keras.layers.InputLayer(input_shape=(20,)),
63 keras.layers.Dense(2, input_shape=(20,), activation='sigmoid')
64 ])
65
66 model.compile(optimizer='adam',
67 loss='sparse_categorical_crossentropy',
68 metrics=['accuracy'])
69
70 print("Training")
71 metrics = model.fit(data, classes, epochs=100, verbose=2,
72 validation_split=0.2)
73 print("Max value: ", max(metrics.history['acc']), " at epoch", np.argmax(metrics.history['acc']))
74
75 print("Testing")
76 test_loss, test_acc = model.evaluate(data, classes)
77 print('Test accuracy:', test_acc)
78
79 w=model.get_weights()
80 print(w)
81
82 tf.keras.models.save_model(model,'simslope.h5',True,True)
83
File 00_simslope/simslope1.py added (mode: 100644) (index 0000000..6e36081)
1 #
2 # Loads model from saved file, then classifies data
3 #
4
5 # TensorFlow, keras, np
6 import tensorflow as tf
7 from tensorflow import keras
8 import numpy as np
9
10 # open file
11 f=open('simslope.txt','r')
12 d=[[int(x) for x in line.split()] for line in f]
13 f.close()
14
15 # copy to classes and data
16 classes=[]
17 data=[]
18
19 for a in range(0,len(d)):
20 classes.append(d[a][0])
21 row=[]
22 for b in range(1,len(d[a])):
23 row.append(d[a][b])
24 data.append(row)
25
26 #print(classes)
27 #print(data)
28
29 # normalize each row of data
30 normdata=[]
31 for a in range(0,len(data)):
32 norm=[]
33 s=min(data[a])
34 t=max(data[a])
35 for b in range(0,len(data[a])):
36 norm.append((float(data[a][b])-float(s))/(float(t)-float(s)))
37 normdata.append(norm)
38
39 #print(normdata)
40
41 classes=np.array(classes)
42 data=np.array(normdata)
43
44 print("data has shape", data.shape)
45 print("classes has shape ", classes.shape)
46
47 model=keras.models.load_model('simslope.h5')
48 model.summary()
49
50 print("Testing")
51 test_loss, test_acc = model.evaluate(data, classes)
52 print('Test accuracy:', test_acc)
53
54 w=model.get_weights()
55 print(w)
56
57
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