-
Notifications
You must be signed in to change notification settings - Fork 0
/
mlp.py
executable file
·137 lines (105 loc) · 4.91 KB
/
mlp.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
# Code from Chapter 3 of Machine Learning: An Algorithmic Perspective
# by Stephen Marsland (http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html)
# You are free to use, change, or redistribute the code in any way you wish for
# non-commercial purposes, but please maintain the name of the original author.
# This code comes with no warranty of any kind.
# Stephen Marsland, 2008
from numpy import *
class mlp:
""" A Multi-Layer Perceptron"""
def __init__(self,inputs,targets,nhidden,beta=1,momentum=0.9,outtype='logistic'):
""" Constructor """
# Set up network size
self.nin = shape(inputs)[1]
self.nout = shape(targets)[1]
self.ndata = shape(inputs)[0]
self.nhidden = nhidden
self.beta = beta
self.momentum = momentum
self.outtype = outtype
# Initialise network
self.weights1 = (random.rand(self.nin+1,self.nhidden)-0.5)*2/sqrt(self.nin)
self.weights2 = (random.rand(self.nhidden+1,self.nout)-0.5)*2/sqrt(self.nhidden)
def earlystopping(self,inputs,targets,valid,validtargets,eta,niterations=100):
valid = concatenate((valid,-ones((shape(valid)[0],1))),axis=1)
old_val_error1 = 100002
old_val_error2 = 100001
new_val_error = 100000
count = 0
while (((old_val_error1 - new_val_error) > 0.001) or ((old_val_error2 - old_val_error1)>0.001)):
count+=1
print count
self.mlptrain(inputs,targets,eta,niterations)
old_val_error2 = old_val_error1
old_val_error1 = new_val_error
validout = self.mlpfwd(valid)
new_val_error = 0.5*sum((validtargets-validout)**2)
print "Stopped", new_val_error,old_val_error1, old_val_error2
return new_val_error
def mlptrain(self,inputs,targets,eta,niterations):
""" Train the thing """
# Add the inputs that match the bias node
inputs = concatenate((inputs,-ones((self.ndata,1))),axis=1)
change = range(self.ndata)
updatew1 = zeros((shape(self.weights1)))
updatew2 = zeros((shape(self.weights2)))
for n in range(niterations):
self.outputs = self.mlpfwd(inputs)
error = 0.5*sum((targets-self.outputs)**2)
if (mod(n,100)==0):
print "Iteration: ",n, " Error: ",error
# Different types of output neurons
if self.outtype == 'linear':
deltao = (targets-self.outputs)/self.ndata
elif self.outtype == 'logistic':
deltao = (targets-self.outputs)*self.outputs*(1.0-self.outputs)
elif self.outtype == 'softmax':
#deltao = (targets-self.outputs)*self.outputs/self.ndata
deltao = (targets-self.outputs)/self.ndata
else:
print "error"
deltah = self.hidden*(1.0-self.hidden)*(dot(deltao,transpose(self.weights2)))
updatew1 = eta*(dot(transpose(inputs),deltah[:,:-1])) + self.momentum*updatew1
updatew2 = eta*(dot(transpose(self.hidden),deltao)) + self.momentum*updatew2
self.weights1 += updatew1
self.weights2 += updatew2
# Randomise order of inputs
random.shuffle(change)
inputs = inputs[change,:]
targets = targets[change,:]
def mlpfwd(self,inputs):
""" Run the network forward """
self.hidden = dot(inputs,self.weights1);
self.hidden = 1.0/(1.0+exp(-self.beta*self.hidden))
self.hidden = concatenate((self.hidden,-ones((shape(inputs)[0],1))),axis=1)
outputs = dot(self.hidden,self.weights2);
# Different types of output neurons
if self.outtype == 'linear':
return outputs
elif self.outtype == 'logistic':
return 1.0/(1.0+exp(-self.beta*outputs))
elif self.outtype == 'softmax':
normalisers = sum(exp(outputs),axis=1)*ones((1,shape(outputs)[0]))
return transpose(transpose(exp(outputs))/normalisers)
else:
print "error"
def confmat(self,inputs,targets):
"""Confusion matrix"""
# Add the inputs that match the bias node
inputs = concatenate((inputs,-ones((shape(inputs)[0],1))),axis=1)
outputs = self.mlpfwd(inputs)
nclasses = shape(targets)[1]
if nclasses==1:
nclasses = 2
outputs = where(outputs>0.5,1,0)
else:
# 1-of-N encoding
outputs = argmax(outputs,1)
targets = argmax(targets,1)
cm = zeros((nclasses,nclasses))
for i in range(nclasses):
for j in range(nclasses):
cm[i,j] = sum(where(outputs==i,1,0)*where(targets==j,1,0))
print "Confusion matrix is:"
print cm
print "Percentage Correct: ",trace(cm)/sum(cm)*100