# Network architecture: 2 -> 4 -> 1 (input -> hidden -> output)
input_size = 2      # Number of input features (x and y coordinates)
hidden_size = 4     # Number of neurons in hidden layer
output_size = 1     # Number of output values (0 or 1)

# Initialize weights and biases
np.random.seed(42)
W1 = np.random.randn(input_size, hidden_size) * 0.5  # Weights: connection strengths between layers
b1 = np.zeros((1, hidden_size))                      # Biases: adjustable offsets for each neuron
W2 = np.random.randn(hidden_size, output_size) * 0.5 # Weights for output layer
b2 = np.zeros((1, output_size))                      # Bias for output layer

print(f"W1 shape: {W1.shape}")  # (2, 4)
print(f"b1 shape: {b1.shape}")  # (1, 4)
print(f"W2 shape: {W2.shape}")  # (4, 1)
print(f"b2 shape: {b2.shape}")  # (1, 1)