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perceptron.py
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perceptron.py
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import numpy as np
imput_layer = 3
output_layer = 1
# Input matrix, 4 entires each with 3 inputs
X = np.array([ [1,0,0],
[0,0,1],
[0,1,0],
[1,0,1] ])
# Output set, 1 output per input entry
y = np.array([ [0],
[1],
[0],
[1]])
# Sigmoid activation function
def sigmoid(x,deriv=False):
if(deriv==True):
return x*(1-x)
else:
return 1/(1+np.exp(-x))
np.random.seed(1)
# Initialize the array of weights randomly
W0 = np.random.random((imput_layer,output_layer))
# Define our forward propogation function
def forward_propagate(X):
L0 = X
L1 = sigmoid(np.dot(L0,W0))
return L1
def train():
global W0
for iter in range(10000):
# Forward propagation
L0 = X
L1 = sigmoid(np.dot(L0,W0))
# Calculate the difference between the predicted output (L1)
# and target output (y)
L1_error = y - L1
# Multiply how much we missed by the slope of the sigmoid
# at the values in L1
L1_delta = L1_error * sigmoid(L1,True)
# Update weights using the delta
W0 += np.dot(L0.T,L1_delta)
print("Output After Training:")
print(L1)
train()
result = np.round(forward_propagate(np.array([[1,0,1]])))
print(result)