# 簡單的人工神經網絡(ANN)設計
```python
import
numpy
as
np
import
matplotlib
.
pyplot
as
mp
class
ANNModel
(
)
:
def
__init__
(
self
)
:
# 隨機初始化權重[-1 1)
self
.
w0
=
2
*
np
.
random
.
random
(
(
2
,
4
)
)
-
1
self
.
w1
=
2
*
np
.
random
.
random
(
(
4
,
1
)
)
-
1
# 學習率
self
.
lrate
=
0.1
# sigmiod 函數
def
active
(
self
,
x
)
:
return
1
/
(
1
+
np
.
exp
(
-
x
)
)
# sigmoid函數導函數
def
backward
(
self
,
x
)
:
return
x
*
(
1
-
x
)
# 單層網路前向傳播
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
)
)
# 損失
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
(
)
更多文章、技術交流、商務合作、聯系博主
微信掃碼或搜索：z360901061
微信掃一掃加我為好友
QQ號聯系： 360901061
您的支持是博主寫作最大的動力，如果您喜歡我的文章，感覺我的文章對您有幫助，請用微信掃描下面二維碼支持博主2元、5元、10元、20元等您想捐的金額吧，狠狠點擊下面給點支持吧，站長非常感激您！手機微信長按不能支付解決辦法：請將微信支付二維碼保存到相冊，切換到微信，然后點擊微信右上角掃一掃功能，選擇支付二維碼完成支付。
【本文對您有幫助就好】元
