Method train

neural_network/back_propagation_neural_network.py:126–158

(self, xdata, ydata, train_round, accuracy)

            print("bias.shape ", np.shape(layer.bias))

    def train(self, xdata, ydata, train_round, accuracy):
        self.train_round = train_round
        self.accuracy = accuracy

        self.ax_loss.hlines(self.accuracy, 0, self.train_round * 1.1)

        x_shape = np.shape(xdata)
        for _ in range(train_round):
            all_loss = 0
            for row in range(x_shape[0]):
                _xdata = np.asmatrix(xdata[row, :]).T
                _ydata = np.asmatrix(ydata[row, :]).T

                # forward propagation
                for layer in self.layers:
                    _xdata = layer.forward_propagation(_xdata)

                loss, gradient = self.cal_loss(_ydata, _xdata)
                all_loss = all_loss + loss

                # back propagation: the input_layer does not upgrade
                for layer in self.layers[:0:-1]:
                    gradient = layer.back_propagation(gradient)

            mse = all_loss / x_shape[0]
            self.train_mse.append(mse)

            self.plot_loss()

            if mse < self.accuracy:
                print("----达到精度----")
                return mse
        return None

    def cal_loss(self, ydata, ydata_):
        self.loss = np.sum(np.power((ydata - ydata_), 2))