train-neural-net.py

# Train by iterating 10,000 times
for iteration in range(10000):
  
  # Define input layer
  input_layer = training_inputs
  
  # Normalize the product of the input layer with the synaptic 
  # weights by using the sigmoid function to get outputs
  outputs = sigmoid(np.dot(input_layer, synaptic_weights))
  
  # Calculate the error
  error = training_outputs - outputs
  
  # Multiply how much we missed by the slope of the sigmoid at the values in outputs
  adjustments = error * sigmoid_derivative(outputs)
  
  # Update the weights using our adjustments
  synaptic_weights += np.dot(input_layer.T, adjustments)