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back propagation neural networks python example

_loganthx answered on May 19, 2023 Popularity 7/10 Helpfulness 2/10

answer back propagation neural networks python example

back propagation neural networks python example

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import numpy as np
input = np.array([1]) # size(1,1)
desired_output = np.array(np.pi/5) # pi/5 = 0.628..
# Architecture: 
# inp: (1,1) x w1: (1,2) + b1: (1,2)
# inp: (1,2) x w2: (2,1) + b2: (1,1) 
W1, b1 = np.random.randn(1,2), np.zeros((1,2))
W2, b2 = np.random.randn(2,1), np.zeros((1,1))

def ReLU(inp, deriv=False):
	inp = np.clip(inp, 1e-2, 1e2)
	if deriv==True: return np.where(inp>0,1,0)
	else: return np.maximum(0, inp)

def Sigmoid(inp, deriv=False):
	inp = np.clip(inp, 1e-2, 1e2); fx = 1/(1+np.exp(-inp))
	if deriv==True: return fx*(1-fx)
	else: return fx

def Loss(output, label):
	return (output-label)**2

loss, epoch = [], 10
for ep in range(epoch):
	label = desired_output
	Z1 = np.dot(input, W1) + b1; A1 = ReLU(Z1)
	Z2 = np.dot(A1, W2) + b2; A2 = Sigmoid(Z2)
	L = Loss(A2, desired_output); loss.append(L)
	print(f'output: {A2}, Loss: {np.mean(L)}')

	# Back Propagate: Loss with respect to Weight 2
	dLossdW2 = 2*(A2 - label)*np.dot(Sigmoid(Z2, deriv=True), A1)  # (1,1)x(1,1)x(1,2) = (1,2) = W2.T 
	dLossdW2 = dLossdW2.T
	# Back Propagate: Loss with respect to Bias 2
	dLossdb2 = 2*(A2 - label)*Sigmoid(Z2, deriv=True)
	# Back Propagate: Loss with respect to Weight 1
	dLossdW1 = 2*(A2 - label)*np.dot(Sigmoid(Z2, deriv=True), W2.T)*np.dot(ReLU(Z1, deriv=True).T, input)
	# Back Propagate: Loss with respect to Bias 1
	dLossdb1 = 2*(A2 - label)*np.dot(Sigmoid(Z2, deriv=True), ReLU(Z1, deriv=True))
    # Update Weights and Biases
	W2 -= dLossdW2
	b2 -= dLossdb2
	W1 -= dLossdW1
	b1 -= dLossdb1

Popularity 7/10 Helpfulness 2/10 Language python

Source: Grepper

Tags: back propagation python

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Contributed on May 19 2023

_loganthx

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back propagation neural networks python example

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