PythonNN_demo003

PythonNN_demo003.py

def train(self, input_list, target_list):
    inputs = np.array(input_list, ndmin=2).T
    targets = np.array(target_list, ndmin=2).T

    # signals into hidden layer
    hidden_inputs = np.dot(self.wih, inputs)
    # signals from hidden layer
    hidden_outputs = self.activation_func(hidden_inputs)

    # signals into output
    final_inputs = np.dot(self.who, hidden_outputs)
    # signals from output
    outputs = self.activation_func(final_inputs)

    # error between hidden and output
    output_errors = targets - outputs
    # error between input and hiddent
    hidden_errors = np.dot(self.who.T, output_errors)

    # updating weight between hidden and output layers
    self.who += self.alpha * np.dot((output_errors * outputs * (1.0 - outputs)),
                                    np.transpose(hidden_outputs))

    # updating weight between input and hidden
    self.wih += self.alpha * np.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),
                                    np.transpose(inputs))