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XOR - Backpropagation NN
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import numpy as np | |
#np.random.seed(0) | |
def sigmoid (x): | |
return 1/(1 + np.exp(-x)) | |
def sigmoid_derivative(x): | |
return x * (1 - x) | |
#Input datasets | |
inputs = np.array([[0,0],[0,1],[1,0],[1,1]]) | |
expected_output = np.array([[0],[1],[1],[0]]) | |
epochs = 10000 | |
lr = 0.1 | |
inputLayerNeurons, hiddenLayerNeurons, outputLayerNeurons = 2,2,1 | |
#Random weights and bias initialization | |
hidden_weights = np.random.uniform(size=(inputLayerNeurons,hiddenLayerNeurons)) | |
hidden_bias =np.random.uniform(size=(1,hiddenLayerNeurons)) | |
output_weights = np.random.uniform(size=(hiddenLayerNeurons,outputLayerNeurons)) | |
output_bias = np.random.uniform(size=(1,outputLayerNeurons)) | |
print("Initial hidden weights: ",end='') | |
print(*hidden_weights) | |
print("Initial hidden biases: ",end='') | |
print(*hidden_bias) | |
print("Initial output weights: ",end='') | |
print(*output_weights) | |
print("Initial output biases: ",end='') | |
print(*output_bias) | |
#Training algorithm | |
for _ in range(epochs): | |
#Forward Propagation | |
hidden_layer_activation = np.dot(inputs,hidden_weights) | |
hidden_layer_activation += hidden_bias | |
hidden_layer_output = sigmoid(hidden_layer_activation) | |
output_layer_activation = np.dot(hidden_layer_output,output_weights) | |
output_layer_activation += output_bias | |
predicted_output = sigmoid(output_layer_activation) | |
#Backpropagation | |
error = expected_output - predicted_output | |
d_predicted_output = error * sigmoid_derivative(predicted_output) | |
error_hidden_layer = d_predicted_output.dot(output_weights.T) | |
d_hidden_layer = error_hidden_layer * sigmoid_derivative(hidden_layer_output) | |
#Updating Weights and Biases | |
output_weights += hidden_layer_output.T.dot(d_predicted_output) * lr | |
output_bias += np.sum(d_predicted_output,axis=0,keepdims=True) * lr | |
hidden_weights += inputs.T.dot(d_hidden_layer) * lr | |
hidden_bias += np.sum(d_hidden_layer,axis=0,keepdims=True) * lr | |
print("Final hidden weights: ",end='') | |
print(*hidden_weights) | |
print("Final hidden bias: ",end='') | |
print(*hidden_bias) | |
print("Final output weights: ",end='') | |
print(*output_weights) | |
print("Final output bias: ",end='') | |
print(*output_bias) | |
print("\nOutput from neural network after 10,000 epochs: ",end='') | |
print(*predicted_output) |
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