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  1. import numpy as np
  2. from itertools import product
  3.  
  4. # Parameters
  5. learning_rate = 0.1
  6. max_iterations = 1000
  7.  
  8. # Activation function: Simple thresholding
  9. def activation_function(weighted_sum, lower_threshold=0.8, upper_threshold=1.2):
  10.     return 1 if lower_threshold < weighted_sum < upper_threshold else 0
  11.  
  12. # Inputs for the two-variable logic tables
  13. inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
  14.  
  15. # Generate all 16 possible truth tables for 2 inputs
  16. all_truth_tables = list(product([0, 1], repeat=4))  # 16 combinations of binary outputs
  17.  
  18. # Train the network for each truth table
  19. for truth_table_idx, targets in enumerate(all_truth_tables):
  20.     print(f"\nTraining for Truth Table {truth_table_idx + 1}: {targets}")
  21.  
  22.     # Initialize weights for fully connected network
  23.     weights_N1_to_N3 = np.random.rand()  # Weight from N1 to N3
  24.     weights_N2_to_N3 = np.random.rand()  # Weight from N2 to N3
  25.     weights_N1_to_N4 = np.random.rand()  # Weight from N1 to N4
  26.     weights_N2_to_N4 = np.random.rand()  # Weight from N2 to N4
  27.     weights_N3_to_N4 = np.random.rand()  # Weight from N3 to N4
  28.  
  29.     epoch = 0
  30.     network_trained = False
  31.  
  32.     while epoch < max_iterations:
  33.         epoch += 1
  34.         all_correct = True  # Flag to track if all outputs are correct
  35.  
  36.         for input_vector, target in zip(inputs, targets):
  37.             N1, N2 = input_vector  # Input neurons
  38.  
  39.             # Forward pass
  40.             N3_input = N1 * weights_N1_to_N3 + N2 * weights_N2_to_N3
  41.             N3 = activation_function(N3_input)  # Output of N3
  42.  
  43.             N4_input = (N1 * weights_N1_to_N4 +
  44.                         N2 * weights_N2_to_N4 +
  45.                         N3 * weights_N3_to_N4)
  46.             N4 = activation_function(N4_input)  # Output of N4 (final output)
  47.  
  48.             # Error calculation
  49.             error = target - N4
  50.  
  51.             # Backpropagation and weight updates
  52.             if error != 0:
  53.                 all_correct = False
  54.  
  55.                 # Update weights connecting to N4
  56.                 weights_N1_to_N4 += learning_rate * error * N1
  57.                 weights_N2_to_N4 += learning_rate * error * N2
  58.                 weights_N3_to_N4 += learning_rate * error * N3
  59.  
  60.                 # Update weights connecting to N3
  61.                 weights_N1_to_N3 += learning_rate * error * N1
  62.                 weights_N2_to_N3 += learning_rate * error * N2
  63.  
  64.         if all_correct:
  65.             network_trained = True
  66.             break  # Stop training if all outputs are correct
  67.  
  68.     # Print results for the truth table
  69.     if network_trained:
  70.         print(f"The network learned the truth table correctly after {epoch} iterations.")
  71.     else:
  72.         print(f"The network failed to learn the truth table after {epoch} iterations.")
  73.  
  74.     # Test the trained network
  75.     print("\nTesting the trained network:")
  76.     for input_vector, target in zip(inputs, targets):
  77.         N1, N2 = input_vector
  78.  
  79.         # Forward pass
  80.         N3_input = N1 * weights_N1_to_N3 + N2 * weights_N2_to_N3
  81.         N3 = activation_function(N3_input)
  82.  
  83.         N4_input = (N1 * weights_N1_to_N4 +
  84.                     N2 * weights_N2_to_N4 +
  85.                     N3 * weights_N3_to_N4)
  86.         N4 = activation_function(N4_input)
  87.  
  88.         print(f"Input: {input_vector}, Target: {target}, Output: {N4}")
  89.     print("-----------------------------------------------------------")
  90.  
Success #stdin #stdout 1.51s 28872KB
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Standard input is empty
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Training for Truth Table 1: (0, 0, 0, 0)
The network learned the truth table correctly after 3 iterations.

Testing the trained network:
Input: [0 0], Target: 0, Output: 0
Input: [0 1], Target: 0, Output: 0
Input: [1 0], Target: 0, Output: 0
Input: [1 1], Target: 0, Output: 0
-----------------------------------------------------------

Training for Truth Table 2: (0, 0, 0, 1)
The network learned the truth table correctly after 2 iterations.

Testing the trained network:
Input: [0 0], Target: 0, Output: 0
Input: [0 1], Target: 0, Output: 0
Input: [1 0], Target: 0, Output: 0
Input: [1 1], Target: 1, Output: 1
-----------------------------------------------------------

Training for Truth Table 3: (0, 0, 1, 0)
The network learned the truth table correctly after 8 iterations.

Testing the trained network:
Input: [0 0], Target: 0, Output: 0
Input: [0 1], Target: 0, Output: 0
Input: [1 0], Target: 1, Output: 1
Input: [1 1], Target: 0, Output: 0
-----------------------------------------------------------

Training for Truth Table 4: (0, 0, 1, 1)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 0, Output: 0
Input: [0 1], Target: 0, Output: 1
Input: [1 0], Target: 1, Output: 0
Input: [1 1], Target: 1, Output: 0
-----------------------------------------------------------

Training for Truth Table 5: (0, 1, 0, 0)
The network learned the truth table correctly after 10 iterations.

Testing the trained network:
Input: [0 0], Target: 0, Output: 0
Input: [0 1], Target: 1, Output: 1
Input: [1 0], Target: 0, Output: 0
Input: [1 1], Target: 0, Output: 0
-----------------------------------------------------------

Training for Truth Table 6: (0, 1, 0, 1)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 0, Output: 0
Input: [0 1], Target: 1, Output: 0
Input: [1 0], Target: 0, Output: 1
Input: [1 1], Target: 1, Output: 0
-----------------------------------------------------------

Training for Truth Table 7: (0, 1, 1, 0)
The network learned the truth table correctly after 2 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: 1
Input: [1 1], Target: 0, Output: 0
-----------------------------------------------------------

Training for Truth Table 8: (0, 1, 1, 1)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 0, Output: 0
Input: [0 1], Target: 1, Output: 0
Input: [1 0], Target: 1, Output: 0
Input: [1 1], Target: 1, Output: 0
-----------------------------------------------------------

Training for Truth Table 9: (1, 0, 0, 0)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 1, Output: 0
Input: [0 1], Target: 0, Output: 0
Input: [1 0], Target: 0, Output: 0
Input: [1 1], Target: 0, Output: 0
-----------------------------------------------------------

Training for Truth Table 10: (1, 0, 0, 1)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 1, Output: 0
Input: [0 1], Target: 0, Output: 0
Input: [1 0], Target: 0, Output: 0
Input: [1 1], Target: 1, Output: 1
-----------------------------------------------------------

Training for Truth Table 11: (1, 0, 1, 0)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 1, Output: 0
Input: [0 1], Target: 0, Output: 0
Input: [1 0], Target: 1, Output: 1
Input: [1 1], Target: 0, Output: 0
-----------------------------------------------------------

Training for Truth Table 12: (1, 0, 1, 1)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 1, Output: 0
Input: [0 1], Target: 0, Output: 0
Input: [1 0], Target: 1, Output: 1
Input: [1 1], Target: 1, Output: 1
-----------------------------------------------------------

Training for Truth Table 13: (1, 1, 0, 0)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 1, Output: 0
Input: [0 1], Target: 1, Output: 1
Input: [1 0], Target: 0, Output: 0
Input: [1 1], Target: 0, Output: 0
-----------------------------------------------------------

Training for Truth Table 14: (1, 1, 0, 1)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 1, Output: 0
Input: [0 1], Target: 1, Output: 0
Input: [1 0], Target: 0, Output: 1
Input: [1 1], Target: 1, Output: 0
-----------------------------------------------------------

Training for Truth Table 15: (1, 1, 1, 0)
The network failed to learn the truth table after 1000 iterations.

Testing the trained network:
Input: [0 0], Target: 1, Output: 0
Input: [0 1], Target: 1, Output: 0
Input: [1 0], Target: 1, Output: 0
Input: [1 1], Target: 0, Output: 0
-----------------------------------------------------------

Training for Truth Table 16: (1, 1, 1, 1)
The network failed to learn the truth table after 1000 iterations.

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

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