ann/ann-var.py at master · abhirathb/ann · 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
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
import numpy as np
import sys
#basic constants for run
#this is assuming one hidden layer i.e. a sigmoid layer and a softmax-output layer
num_hidden = 5  #number of units in hidden layer
num_inputs = int(sys.argv[1]) #number of inputs
N = int(sys.argv[2])
eps = float(sys.argv[3])
var = float(sys.argv[4])
#.....

#layer variables:
hidden_weights = np.zeros((num_inputs,num_hidden),np.float128)
hidden_biases = np.zeros((num_hidden),np.float128)
output_weights = np.zeros((num_hidden,2),np.float128)
output_biases = np.zeros((2),np.float128)

hidden_weights_grad = np.zeros((num_inputs,num_hidden),np.float128)
hidden_biases_grad = np.zeros((num_hidden),np.float128)
output_weights_grad = np.zeros((num_hidden,2),np.float128)
output_biases_grad = np.zeros((2),np.float128)

hidden_outputs = np.zeros((num_hidden),np.float128)
output_outputs = np.zeros((2),np.float128)
#....

#Sampling Variables:
init_sd_output = 1.0
init_sd_hidden = 1.0
pW = np.random.normal(0,init_sd_output,output_weights.shape)
pB = np.random.normal(0,init_sd_output,output_biases.shape)
pw = np.random.normal(0,init_sd_hidden,hidden_weights.shape)
pb = np.random.normal(0,init_sd_hidden,hidden_biases.shape)
#....


def compute_outputs(hidden_weights, hidden_biases, output_weights, output_biases,inputs):
    h_z = np.dot(inputs,hidden_weights)
    for i in range(len(h_z)):
        h_z[i]+=hidden_biases
        for j in range(len(h_z[i])):
            h_z[i][j] = 1/(1+np.exp(-h_z[i][j]))


    output_outputs = np.dot(h_z,output_weights)


    for i in range(len(output_outputs)):
        output_outputs[i]+=output_biases
        o1 = np.exp(output_outputs[i][0])
        o2 = np.exp(output_outputs[i][1])
        output_outputs[i][0] = o1/(o1+o2)
        output_outputs[i][1] = o2/(o1+o2)


    return h_z,output_outputs

def compute_grads(hidden_weights, hidden_outputs, output_weights,output_outputs,inputs,outputs):
    diff = outputs - output_outputs    #the main difference term

    dB = np.dot(np.ones((1,np.shape(diff)[0])),diff).reshape((output_weights.shape[1],))
    dW = np.dot(hidden_outputs.T,diff)
    bp = np.dot(diff,output_weights.T)
    prod = hidden_outputs*(1-hidden_outputs)
    back = bp*prod
    db = np.dot(np.ones((1,np.shape(back)[0])),back).reshape((hidden_weights.shape[1],))
    dw = np.dot(inputs.T,back)
    return dB,dW,db,dw

def Hamiltonian(outputs, output_outputs,pw,pb,pB,pW):
    log = outputs*np.log(output_outputs)
    log = log.sum()
    k = (pw**2).sum() + (pb**2).sum() + (pW**2).sum() + (pB**2).sum()
    return log,k,log-k

def leap_frog(hw, hb, pw,pb, dw,db, ow,ob,pW,pB,dW,dB,eps,inputs,outputs):
    pw += (eps/2.0)*dw
    pb += (eps/2.0)*db
    pW += (eps/2.0)*dW
    pB += (eps/2.0)*dB

    hw += (eps)*pw
    hb += (eps)*pb
    ow += (eps)*pW
    ob += (eps)*pB

    hz,oo = compute_outputs(hw,hb,ow,ob,inputs)
    dB,dW,db,dw = compute_grads(hw,hz,ow,oo,inputs,outputs)

    pw += (eps/2.0)*dw
    pb += (eps/2.0)*db
    pW += (eps/2.0)*dW
    pB += (eps/2.0)*dB

    return hw,hb,pw,pb,ow,ob,pW,pB


if __name__ == "__main__":
    #:    global hidden_weights, hidden_biases, output_weights, output_biases
    inputs = np.loadtxt('input_files/maf_%d_%d'%(num_inputs,N),dtype=np.float128)
    outputs = np.loadtxt('input_files/hc_%d'%(N),dtype=np.float128)
    hidden_weights = np.random.normal(0,var,(num_inputs,num_hidden)).astype(np.float128)
    #hidden_weights = np.loadtxt('input_files/init_hw')
    hidden_biases +=  np.random.normal(0,var,(num_hidden)).astype(np.float128)
    output_weights += np.random.normal(0,var,(num_hidden,2)).astype(np.float128)
    #output_weights += np.loadtxt('input_files/init_ow')
    output_biases += np.random.normal(0,var,(2)).astype(np.float128)

#    eps = 0.00001
    hidden_outputs,output_outputs = compute_outputs(hidden_weights,hidden_biases, output_weights, output_biases, inputs)
    dB,dW,db,dW = compute_grads(hidden_weights,hidden_outputs, output_weights, output_outputs, inputs,outputs)
    steps = 2

    for i in range(2):
        print "Step:",(i+1)

        hidden_outputs,output_outputs = compute_outputs(hidden_weights,hidden_biases, output_weights, output_biases, inputs)
        output_biases_grad,output_weights_grad,hidden_biases_grad,hidden_weights_grad = compute_grads(hidden_weights,hidden_outputs, output_weights, output_outputs, inputs,outputs)
        l,k,H = Hamiltonian(outputs,output_outputs,pw,pb,pW,pB)
        hidden_weights,hidden_biases,pw,pb,output_weights,output_biases,pW,pB = leap_frog(hidden_weights, hidden_biases,pw,pb,hidden_weights_grad,hidden_biases_grad,output_weights,output_biases,pW,pB,output_weights_grad,output_biases_grad,eps,inputs,outputs)

        hidden_outputs,output_outputs = compute_outputs(hidden_weights,hidden_biases, output_weights, output_biases, inputs)
        l_new,k_new,H_new = Hamiltonian(outputs,output_outputs,pw,pb,pW,pB)

        print 'current U:',l
        print 'current K:',k
        print 'current H:',H
        print 'proposed U:',l_new
        print 'proposed K:',k_new
        print 'proposed H:',H_new
        print 'diff-h:',H_new-H
        print 'diff-k:',k_new-k
        print 'diff-u:',l_new-l
        print 'ratio-u:',(l_new-l)/l
        print 'ratio-h:',(H_new-H)/H
        print 'ratio-k:',(k_new-k)/k