keyboard_arrow_down NOR SIGMOID
import numpy as np
inputs = np.array([[0,0], [1,0], [0,1], [1,1]])
outputs = np.array([1, 0, 0, 0])
sum1 = np.array([0.0, 0.0])
weights = np.array([0.0, 0.0])
learning_rate = 0.1
def step_function(sum):
if (sum >= 1):
return 1
return 0
def calculate_output(instance):
s = sigmoid(np.dot(instance,weights))
return step_function(s)
def calculate_output1(instance):
s = sigmoid(np.dot(instance,weights))
step_function(s)
if step_function(s) == 1:
return 0
return 1
def sigmoid(sum):
return 1 / (1 + np.exp(-sum))
def train():
total_error = 1
while (total_error != 0):
total_error = 0
for i in range(len(outputs)):
prediction = calculate_output(inputs[i])
if (prediction == 0):
prediction = 1
else:
prediction = 0
error = abs(outputs[i] - prediction)
total_error += error
if error > 0:
for j in range(len(weights)):
sum1[j] = weights[j] * inputs[i][j]
c1 = sum1[0]
c2 = sum1[1]
sum2 = c1 + c2
sig = sigmoid(sum2)
print('Sigmoid: ' + str(sig))
print(inputs[i][j])
print(learning_rate)
print(error)
weights[j] = weights[j] + (learning_rate * inputs[i][j] * error)
print('Weight updated: ' + str(weights[j]))
print('Total error: ' + str(total_error))
print(prediction)
train()
account_circle
1
Weight updated: 36.60000000000025
Sigmoid: 0.9999999999999998
1
0.1
1
Weight updated: 36.60000000000025
Total error: 2
0
Sigmoid: 1.0
1
0.1
1
Weight updated: 36.70000000000025
Sigmoid: 0.9999999999999998
0
0.1
1
Weight updated: 36.60000000000025
Sigmoid: 0.5
0
0.1
1
Weight updated: 36.70000000000025
Sigmoid: 0.9999999999999998
1
0.1
1
Weight updated: 36.70000000000025
Total error: 2
0
Sigmoid: 1.0
1
0.1
1
Weight updated: 36.80000000000025
Sigmoid: 0.9999999999999998
0
0.1
1
Weight updated: 36.70000000000025
Sigmoid: 0.5
0
0.1
1
Weight updated: 36.80000000000025
Sigmoid: 0.9999999999999998
1
0.1
1
Weight updated: 36.80000000000025
Total error: 2
0
Total error: 0
0
weights
array([36.8, 36.8])
calculate_output1(np.array([0, 0]))
1
calculate_output1(np.array([0, 1]))
0
calculate_output1(np.array([1, 0]))
0
calculate_output1(np.array([1, 1]))
0