Basic example of a neural network
I am studying a neural network tutorial and made a simple perceptron code as shown below
purpose
- Dividing 20 points into two groups.
perceptron.py
import numpy as np
from pprint import pprint
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from tensorflow.contrib.learn.python.learn.tests.dataframe.mocks import Mock2x2Transform
plt.style.use('ggplot')
font = {'family' : 'meiryo'}
matplotlib.rc('font', **font)
rng = np.random.RandomState(123)
d = 2 #dimension
N = 10 # each group items
mean = 5
x1 = rng.randn(N,d) + np.array([0,0]) # group 0
x2 = rng.randn(N,d) + np.array([mean,mean]) $group 1
x = np.concatenate((x1,x2),axis = 0)
##### Plot points
allDf = pd.DataFrame(columns=['x','y'])
k = 0
for i in x:
print(i[0])
temp = pd.DataFrame({'x' : i[0],
'y' : i[1]},index=[k])
k = k + 1
allDf = pd.concat([allDf,temp])
pprint(allDf)
allDf.plot(kind='scatter',x = 'x',y='y')
#########
#initialize w b
w = np.zeros(d)
b = 0
def y(x):
return step(np.dot(w,x) + b)
def step(x):
return 1 * (x > 0)
def t(i):
if i < N:
return 0
else:
return 1
while True:
classified = True
for i in range(N * 2):
delta_w = (t(i) - y(x[i])) * x[i]
delta_b = (t(i) - y(x[i]))
w += delta_w
b += delta_b
classified *= all(delta_w == 0 ) * (delta_b == 0)
if classified:
print("Final answer")
pprint(w)
pprint(b) # I get the answer here but how can I plot this w and b
X = np.linspace(-2,6,100) # it wrong!!
Y = (w[0] * X + w[1] * X) - b # it wrong!!
plt.plot(X,Y)
plt.show()
break
This source code gives me a definitive answer like this
w = array([ 2.14037745, 1.2763927 ])
b = -9
But how to do that?
I want to draw a line between two groups.
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1 answer
You can plot using scatter
for data and contour
for edge solution:
xx = np.linspace(-2,10)
yy = np.linspace(-2,10)
[X1,X2] = np.meshgrid(xx,yy)
Y = [t(i) for i in range(len(x))]
Z = (w[0] * X1.ravel() + w[1] * X2.ravel()) + b
plt.scatter(x[:,0], x[:,1], s=20, c=Y, cmap=None, vmin=0, vmax=2)
plt.contour(X1,X2,Z.reshape(X1.shape), levels=[0], cmap='gray')
plt.show()
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