forked from mathur/mushroom_classifier
/
app.py
176 lines (141 loc) · 4.89 KB
/
app.py
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
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
from config import NUM_TRAINING_ITERATIONS, CONVERGENCE_THRESHOLD
from formulas import err
from models import Layer, cfile
f = None
curr_point = 0
target = []
attrs = []
total_runs = 0
data = None
num_incorrect = 0
prev_sample_err = 0
curr_sample_err = 0
def parse_data(fname):
# reset all the data
global curr_point
global total_runs
global target
global attrs
global num_incorrect
global prev_sample_err
global curr_sample_err
global data
global f
curr_point = 0
total_runs = 0
target = []
attrs = []
num_incorrect = 0
prev_sample_err = 0
curr_sample_err = 0
# set the proper data file
data_file = 'err.txt'
if fname == 'mushroom-training.txt':
data_file = 'training_err.txt'
elif fname == 'mushroom-testing.txt':
data_file = 'testing_err.txt'
# clear the file
open(data_file, 'w+').close()
# open the data file for logging
data = cfile(data_file, 'w')
f = open(fname, 'r').readlines()
for row in f:
row = [x.strip() for x in row.split(',')]
row = [int(num) for num in row]
target.append(int(row[0]))
attrs.append(row[1:])
if __name__ == '__main__':
print "Parsing the training dataset..."
# parse the training dataset and store its information into globals
parse_data('mushroom-training.txt')
# set up the layers to be used
x = Layer(6, attrs[curr_point], 1)
y = Layer(1, x.layer_out, 2)
print "Begining training the neural network:"
# iterate through to train the neural network
while total_runs < NUM_TRAINING_ITERATIONS:
# set the new input values
x.input_vals = attrs[curr_point]
# set up the first layer and evaluate it
x.input_vals = attrs[curr_point]
x.eval()
# set up the second layer and evaluate it
y.input_vals = x.layer_out
y.eval()
# backpropogate
y.backprop(target[curr_point])
x.backprop(y)
# get the current error
curr_err = err(y.layer_out[0], target[curr_point])
# round up and down to check err
if y.layer_out[0] >= 0.5:
temp = 1
else:
temp = 0
# increment the number incorrect if its wrong
if(temp != target[curr_point]):
num_incorrect += 1
# check to see if we have converged
if total_runs % 100 == 0:
prev_sample_err = curr_sample_err
curr_sample_err = curr_err
if abs(prev_sample_err - curr_sample_err) < CONVERGENCE_THRESHOLD:
print "Data has converged at the " + str(total_runs) + "th run."
break;
# print information about the current iteration
print "Current iteration: " + str(total_runs)
print "Current error: " + str(curr_err) + "\n"
data.w(curr_err)
# iterate
total_runs += 1
curr_point += 1
if curr_point >= len(f):
curr_point = 0
# close the file
data.close()
print "Neural network is done training! Hit enter to test it."
print "Error percentage on testing set: " + str(float(num_incorrect)/NUM_TRAINING_ITERATIONS)
raw_input()
print "Begin testing the neural network:"
# parse the testing data and store its information into globals
parse_data('mushroom-testing.txt')
# iterate through to test the neural network
while curr_point < len(f):
# set the new input values
x.input_vals = attrs[curr_point]
# set up the first layer and evaluate it
x.input_vals = attrs[curr_point]
x.eval()
# set up the second layer and evaluate it
y.input_vals = x.layer_out
y.eval()
# backpropogate
y.backprop(target[curr_point])
x.backprop(y)
# get the current error
curr_err = err(y.layer_out[0], target[curr_point])
# round up and down to check err
if y.layer_out[0] >= 0.5:
temp = 1
else:
temp = 0
# increment the number incorrect if its wrong
if(temp != target[curr_point]):
num_incorrect += 1
# check to see if we have converged
if total_runs % 100 == 0:
prev_sample_err = curr_sample_err
curr_sample_err = curr_err
if abs(prev_sample_err - curr_sample_err) < CONVERGENCE_THRESHOLD:
print "Data has converged at the " + str(total_runs) + "th run."
break;
# print information about the current iteration
print "Current iteration: " + str(total_runs)
print "Current Error: " + str(curr_err) + "\n"
data.w(curr_err)
# iterate
total_runs += 1
curr_point += 1
data.close()
print "Testing done! Check out the generated output files ('testing_err.txt' and 'training_err.txt')"
print "Error percentage on training set: " + str(float(num_incorrect)/len(f))