def train(self, training_set_inputs, training_set_outputs):

        for i in range(100):
            self.train_global_step(training_set_inputs, training_set_outputs)
            if (self.layer2_error) < 0.1:
                break


    def train_global_step(self, training_set_inputs, training_set_outputs):

            self.layer2_error = 0
            self.learning_rate = 0.1

            for iteration in range(len(training_set_inputs)):
                    # Pass the training set through our neural network
                output_from_layer_1, output_from_layer_2 = self.think(training_set_inputs)

                            # Calculate the error for layer 2 (The difference between the desired output
                            # and the predicted output).
                layer2_error = training_set_outputs - output_from_layer_2
                layer2_delta = layer2_error * self.__sigmoid_derivative(output_from_layer_2)

                            # Calculate the error for layer 1 (By looking at the weights in layer 1,
                            # we can determine by how much layer 1 contributed to the error in layer 2).
                layer1_error = layer2_delta.dot(self.layer2.synaptic_weights.T)
                layer1_delta = layer1_error * self.__sigmoid_derivative(output_from_layer_1)

                            # Calculate how much to adjust the weights by
                layer1_adjustment = training_set_inputs.T.dot(layer1_delta)
                layer2_adjustment = output_from_layer_1.T.dot(layer2_delta)

                            # Adjust the weights.

                self.layer1.synaptic_weights += self.learning_rate*layer1_adjustment
                self.layer2.synaptic_weights += self.learning_rate*layer2_adjustment


                break