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from unittest import result
import numpy as np
from classificationtest import sigmoid
def __init__(self,gate_name,xdata,tdata):
self.name=gate_name
self.__xdata=xdata.reshape(4,2)
self.__tdata=tdata.reshape(4,1)
self.___W=np.random.rand(2,1)
self.___b=np.random.rand(1)
self.__learning_rate=1e-2
def __loss_func(self):
delta=1e-7
z=np.dot(self.__xdata,self.__W)+self.__b
y=sigmoid(z)
return -np.sum((self.__tdata*np.log(y+delta)+(1-self.__tdata)*np.log(1-y+delta)))
def __error_val(self):
delta=1e-7
z=np.dot(self.__xdata.self.__W)+self.__b
y=sigmoid(z)
return -np.sum((self.__tdata*np.log(y+delta)+(1-self.__tdata)*np.log(1-y+delta)))
def train(self):
f=lambda x:self.__loss_func()
for step in range(8001):
self.__W=self.__learning_rate*numerical_derivative(f,self.__W)
self.__b=self.__learning_rate*numerical_derivative(f,self.__W)
if(step%400==0):
print(",step=,"step,"error value=",self.error_val())
def predict(self,input_data):
z=np.dot(input_data,self.__W)+self.__b
y=sigmoid(z)
if y>0.5:
result=1
else:
result=0
return y,result
xdata=np.array([[0,0],[0,1],[1,0],[1,1]])
tdata=np.array([0,0,0,1])
AND_obj=LoginGate("AND_GATE",xdata,tdata)
AND_obj.train()
print(AND_obj.name,"n")
test_data=np.array([[0,0],[0,1],[1,0],[1,1]])
for input_data in test_data:
(sigmoid.val,logical_val)=AND_obj.predict(input_data)
print(input_data,"=",logical_val,"n")
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