görüntü_tanıma

[dyasam]

def my_product_two_dim_with_threshold(a,b):
return a[0]*b[0]+a[1]*b[1]+a[2]*b[2]

def get_my_data():
my_data_x=[]
my_data_x.append([1,0,0])
my_data_x.append([1,0,1])
my_data_x.append([1,1,0])
my_data_x.append([1,1,1])
my_data_x

my_data_y=[]
my_data_y.append(0)
my_data_y.append(0)
my_data_y.append(0)
my_data_y.append(1)
my_data_y

return my_data_x,my_data_y

x,y=get_my_data()

for a,b in zip(my_data_x,my_data_y):
print(a,b)

def get_parameters():
w=[]
w.append(300)
w.append(200) #random
w.append(100)
w
learning_rate=1
epoch=100 #random

return w, learning_rate, epoch

w,learning_rate,epoch=get_parameters()
samples,output=get_my_data()

output

for i in range (10): #epoch):
error="hata_yok"
s=-1
print("**************************************")
for each_sample,d in zip(samples,output):
print("ağırlık : ", w)
print("örnek ", each_sample)
print("gerçek output ",d)
#print(my_product_two_dim_with_threshold(w, each_sample))

    u=my_product_two_dim_with_threshold(each_sample,w)
    #print u
    if(u>0):
        y=1
    else:
        y=0
    print("tahmin çıktı : ", u)
    print("")
    
    if(y!=d):                  #error var
        for s in range(3):
            w[s]=w[s]-learning_rate*(y-d)*each_sample[s]
        error="hata_var"
if(error=="hata_yok"):
    print("hata yok", i)
    break                               #return 0