-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathSigmoidNeuron.py
168 lines (148 loc) · 5.8 KB
/
SigmoidNeuron.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
import numpy as np
class SN:
def __init__(self, w_init, b_init, algo):
self.w = w_init # Weight
self.b = b_init # bias
self.w_h = [] # History of weight
self.b_h = [] # History of bias
self.e_h = [] # Loss
self.algo = algo # Algorithm
def sigmoid(self, x, w=None, b=None):
if w is None:
w = self.w
if b is None:
b = self.b
return 1. / (1. + np.exp(-(w*x +b)))
def error(self, X, Y, w= None, b= None):
if w is None:
w = self.w
if b is None:
b = self.b
err = 0
for x,y in zip(X,Y):
err += 0.5 *(self.sigmoid(x,w,b) - y)**2 # MEAN SEQUARED ERROR
return err
def grad_w(self,x,y, w=None,b=None):
if w is None:
w = self.w
if b is None:
b = self.b
y_pred = self.sigmoid(x,w,b)
return (y_pred -y) * y_pred * (1 - y_pred) *x
def grad_b(self,x,y, w=None, b=None):
if w is None:
w = self.w
if b is None:
b = self.b
y_pred = self.sigmoid(x,w,b)
return (y_pred - y) * y_pred *(1 - y_pred)
def fit(self, X, Y,
epochs=100, eta=0.01, gamma =0.9,
mini_batch_size=100, eps=1e-8,
beta =0.9, beta1=0.9, beta2 =0.9):
self.w_h = []
self.b_h = []
self.e_h = []
self.X = X
self.Y = Y
if self.algo == 'GD':
for i in range(epochs):
dw, db =0, 0
for x,y in zip(X,Y):
dw += self.grad_w(x,y)
db += self.grad_b(x,y)
self.w -= eta * dw / X.shape[0]
self.b -= eta * db / X.shape[0]
self.append_log()
elif self.algo == 'Momentum':
v_w_prev, v_b_prev =0, 0
for i in range(epochs):
dw, db = 0, 0
for x,y in zip(X,Y):
dw += self.grad_w(x,y)
db += self.grad_b(x,y)
v_w = gamma * v_w_prev + eta * dw
v_b = gamma * v_b_prev + eta *db
self.w = self.w - v_w
self.b = self.b - v_b
v_w_prev = v_w
v_b_prev = v_b
self.append_log()
elif self.algo == 'NAG':
v_w_prev, v_b_prev = 0, 0
for i in range(epochs):
dw, db = 0,0
v_w = gamma * v_w_prev
v_b = gamma * v_b_prev
for x,y in zip(X,Y):
dw += self.grad_w(x, y, self.w - v_w, self.b - v_b)
db += self.grad_b(x, y, self.w - v_w, self.b - v_b)
v_w = gamma * v_w_prev + eta * dw
v_b = gamma * v_b_prev + eta * db
self.w = self.w - v_w
self.b = self.b - v_b
v_w_prev = v_w
v_b_prev = v_b
self.append_log()
elif self.algo == 'MiniBatch':
for i in range(epochs):
dw, db = 0, 0
points_seen = 0
for x,y in zip(X,Y):
dw += self.grad_w(x,y)
db += self.grad_b(x, y)
points_seen += 1
if points_seen % mini_batch_size == 0:
self.w -= eta * dw / mini_batch_size
self.b -= eta * db / mini_batch_size
self.append_log()
dw, db = 0,0
elif self.algo == 'AdaGrad':
v_w, v_b = 0, 0
for i in range(epochs):
dw, db = 0, 0
for x,y in zip(X,Y):
dw += self.grad_w(x, y)
db += self.grad_b(x, y)
v_w = dw**2
v_b = db**2
self.w -= (eta /np.sqrt(v_w)+eps) * dw
self.b -= (eta / np.sqrt(v_b) + eps) *db
self.append_log()
elif self.algo == 'RMSProp':
v_w, v_b = 0, 0
for i in range(epochs):
dw, db = 0, 0
for x, y in zip(X, Y):
dw += self.grad_w(x,y)
db += self.grad_b(x ,y)
v_w = beta * v_w + (1- beta)* dw**2
v_b = beta * v_b + (1 - beta) * db**2
self.w -= (eta / np.sqrt(v_w) + eps) *dw
self.b -= (eta /np.sqrt(v_b + eps)) *db
self.append_log()
elif self.algo == 'Adam':
v_w, v_b = 0, 0
m_w, m_b = 0, 0
num_updates = 0
for i in range(epochs):
dw, db = 0,0
for x,y in zip(X,Y):
dw = self.grad_w(x,y)
db = self.grad_b(x,y)
num_updates += 1
m_w = beta1 * m_w + (1 - beta1)*dw
m_b = beta1 * m_b + (1- beta1)*db
v_w = beta2 * v_w + (1 -beta2) * dw**2
v_b = beta2 * v_b + (1 - beta2) * db**2
m_w_c = m_w / (1 -np.power(beta1, num_updates))
m_b_c = m_b / (1- np.power(beta1, num_updates))
v_w_c = v_w / (1- np.power(beta2, num_updates))
v_b_c = v_b / (1- np.power(beta2, num_updates))
self.w -= (eta / np.sqrt(v_w_c)+ eps) * m_w_c
self.b -= (eta / np.sqrt(v_b_c) +eps) *m_b_c
self.append_log()
def append_log(self):
self.w_h.append(self.w)
self.b_h.append(self.b)
self.e_h.append(self.error(self.X, self.Y))