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  1. import numpy as np
  2.  
  3. # Parameters
  4. learning_rate = 0.1
  5. max_iterations = 1000
  6.  
  7. # Threshold Activation Function
  8. def activation_function(weighted_sum, threshold=0.5):
  9.     return 1 if weighted_sum > threshold else 0
  10.  
  11. # Inputs for XOR truth table
  12. inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
  13. targets = [0, 1, 1, 0]  # XOR Truth Table
  14.  
  15. # Initialize weights for one hidden neuron and one output neuron
  16. weights_input_to_hidden = np.random.rand(3)  # 2 inputs, 1 bias → 1 hidden neuron (3 weights)
  17. weights_hidden_to_output = np.random.rand(2)  # 1 hidden neuron + 1 bias → 1 output neuron
  18.  
  19. # Training loop
  20. epoch = 0
  21. network_trained = False
  22.  
  23. while epoch < max_iterations:
  24.     epoch += 1
  25.     all_correct = True  # Flag to track if all outputs are correct
  26.  
  27.     for input_vector, target in zip(inputs, targets):
  28.         N1, N2 = input_vector
  29.         bias = 1  # Bias input
  30.  
  31.         # Forward pass (hidden layer)
  32.         N3_input = np.dot(np.append(input_vector, bias), weights_input_to_hidden)  # Hidden layer sum
  33.         N3 = activation_function(N3_input)  # Output of the hidden neuron
  34.  
  35.         # Output layer
  36.         N4_input = (N3 * weights_hidden_to_output[0] + bias * weights_hidden_to_output[1])  # Weighted sum to output
  37.         N4 = activation_function(N4_input)  # Output neuron decision
  38.  
  39.         # Error calculation
  40.         error = target - N4
  41.  
  42.         # Weight updates if error exists
  43.         if error != 0:
  44.             all_correct = False
  45.  
  46.             # Update weights for hidden to output neuron
  47.             weights_hidden_to_output[0] += learning_rate * error * N3
  48.             weights_hidden_to_output[1] += learning_rate * error * bias
  49.  
  50.             # Update weights for input to hidden neuron
  51.             weights_input_to_hidden += learning_rate * error * N3 * np.append(input_vector, bias)
  52.  
  53.     if all_correct:
  54.         network_trained = True
  55.         break
  56.  
  57. # Results
  58. if network_trained:
  59.     print(f"The network learned the XOR truth table correctly after {epoch} iterations.")
  60. else:
  61.     print(f"The network failed to learn the XOR truth table after {epoch} iterations.")
  62.  
  63. # Testing the trained network
  64. print("\nTesting the trained network:")
  65. for input_vector, target in zip(inputs, targets):
  66.     N1, N2 = input_vector
  67.     bias = 1
  68.  
  69.     # Forward pass (hidden layer)
  70.     N3_input = np.dot(np.append(input_vector, bias), weights_input_to_hidden)
  71.     N3 = activation_function(N3_input)
  72.  
  73.     # Output layer
  74.     N4_input = (N3 * weights_hidden_to_output[0] + bias * weights_hidden_to_output[1])
  75.     N4 = activation_function(N4_input)
  76.  
  77.     print(f"Input: {input_vector}, Target: {target}, Output: {N4}")
  78.  
Success #stdin #stdout 0.26s 28708KB
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Standard input is empty
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The network failed to learn the XOR truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 0, Output: 0
Input: [0 1], Target: 1, Output: 1
Input: [1 0], Target: 1, Output: 0
Input: [1 1], Target: 0, Output: 0
https://ideone.com/lbeUoU
language:
Python 3 (python  3.9.5)
created:
7 hours ago
visibility:
public

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