# 简单的人工神经网络(ANN)设计
```python
import
numpy
as
np
import
matplotlib
.
pyplot
as
mp
class
ANNModel
(
)
:
def
__init__
(
self
)
:
# 随机初始化权重[-1 1)
self
.
w0
=
2
*
np
.
random
.
random
(
(
2
,
4
)
)
-
1
self
.
w1
=
2
*
np
.
random
.
random
(
(
4
,
1
)
)
-
1
# 学习率
self
.
lrate
=
0.1
# sigmiod 函数
def
active
(
self
,
x
)
:
return
1
/
(
1
+
np
.
exp
(
-
x
)
)
# sigmoid函数导函数
def
backward
(
self
,
x
)
:
return
x
*
(
1
-
x
)
# 单层网路前向传播
def
forward
(
self
,
x
,
w
)
:
return
np
.
dot
(
x
,
w
)
def
fit
(
self
,
x
)
:
for
j
in
range
(
10000
)
:
l0
=
x
l1
=
self
.
active
(
self
.
forward
(
l0
,
self
.
w0
)
)
l2
=
self
.
active
(
self
.
forward
(
l1
,
self
.
w1
)
)
# 损失
l2_error
=
y
-
l2
if
(
j
%
100
)
==
0
:
print
(
"Error:"
+
str
(
np
.
mean
(
np
.
abs
(
l2_error
)
)
)
)
l2_delta
=
l2_error
*
self
.
backward
(
l2
)
self
.
w1
+=
l1
.
T
.
dot
(
l2_delta
*
self
.
lrate
)
l1_error
=
l2_delta
.
dot
(
self
.
w1
.
T
)
l1_delta
=
l1_error
*
self
.
backward
(
l1
)
self
.
w0
+=
l0
.
T
.
dot
(
l1_delta
*
self
.
lrate
)
def
predict
(
self
,
x
)
:
l0
=
x
l1
=
self
.
active
(
self
.
forward
(
l0
,
self
.
w0
)
)
l2
=
self
.
active
(
self
.
forward
(
l1
,
self
.
w1
)
)
result
=
np
.
zeros_like
(
l2
)
result
[
l2
>
0.5
]
=
1
return
result
x
=
np
.
array
(
[
[
3
,
1
]
,
[
2
,
5
]
,
[
1
,
8
]
,
[
6
,
4
]
,
[
5
,
2
]
,
[
3
,
5
]
,
[
4
,
7
]
,
[
4
,
-
1
]
]
)
y
=
np
.
array
(
[
0
,
1
,
1
,
0
,
0
,
1
,
1
,
0
]
)
.
reshape
(
-
1
,
1
)
n
=
500
l
,
r
=
x
[
:
,
0
]
.
min
(
)
-
1
,
x
[
:
,
0
]
.
max
(
)
+
1
b
,
t
=
x
[
:
,
1
]
.
min
(
)
-
1
,
x
[
:
,
1
]
.
max
(
)
+
1
grid_x
=
np
.
meshgrid
(
np
.
linspace
(
l
,
r
,
n
)
,
np
.
linspace
(
b
,
t
,
n
)
)
flat_x
=
np
.
column_stack
(
(
grid_x
[
0
]
.
ravel
(
)
,
grid_x
[
1
]
.
ravel
(
)
)
)
model
=
ANNModel
(
)
model
.
fit
(
x
)
flat_y
=
model
.
predict
(
flat_x
)
grid_y
=
flat_y
.
reshape
(
grid_x
[
0
]
.
shape
)
mp
.
figure
(
'SVM Linear Classification'
,
facecolor
=
'lightgray'
)
mp
.
title
(
'SVM Linear Classification'
,
fontsize
=
20
)
mp
.
xlabel
(
'x'
,
fontsize
=
14
)
mp
.
ylabel
(
'y'
,
fontsize
=
14
)
mp
.
tick_params
(
labelsize
=
10
)
mp
.
pcolormesh
(
grid_x
[
0
]
,
grid_x
[
1
]
,
grid_y
,
cmap
=
'gray'
)
mp
.
scatter
(
x
[
:
,
0
]
,
x
[
:
,
1
]
,
c
=
y
.
ravel
(
)
,
cmap
=
'brg'
,
s
=
80
)
mp
.
show
(
)
