def train()

in #U57fa#U7840#U6559#U7a0b/A2-#U795e#U7ecf#U7f51#U7edc#U57fa#U672c#U539f#U7406/#U7b2c9#U6b65 - #U5faa#U73af#U795e#U7ecf#U7f51#U7edc/src/ch19-RNNBasic/MiniFramework/NeuralNet_4_2.py [0:0]

• 35 lines of code
• 20 McCabe index (conditional complexity)

    def train(self, dataReader, checkpoint=0.1, need_test=True):
        t0 = time.time()
        self.lossFunc = LossFunction_1_1(self.hp.net_type)
        if self.hp.regular_name == RegularMethod.EarlyStop:
            self.loss_trace = TrainingHistory_3_0(True, self.hp.regular_value)
        else:
           self.loss_trace = TrainingHistory_3_0()

        if self.hp.batch_size == -1 or self.hp.batch_size > dataReader.num_train:
            self.hp.batch_size = dataReader.num_train
        # end if
        max_iteration = math.ceil(dataReader.num_train / self.hp.batch_size)
        checkpoint_iteration = (int)(math.ceil(max_iteration * checkpoint))
        need_stop = False
        for epoch in range(self.hp.max_epoch):
            dataReader.Shuffle()
            for iteration in range(max_iteration):
                # get x and y value for one sample
                batch_x, batch_y = dataReader.GetBatchTrainSamples(self.hp.batch_size, iteration)
                # for optimizers which need pre-update weights
                if self.hp.optimizer_name == OptimizerName.Nag:
                    self.__pre_update()
                # get z from x,y
                self.__forward(batch_x, train=True)
                # calculate gradient of w and b
                self.__backward(batch_x, batch_y)
                # final update w,b
                self.__update()
                
                total_iteration = epoch * max_iteration + iteration               
                if (total_iteration+1) % checkpoint_iteration == 0:
                    need_stop = self.CheckErrorAndLoss(dataReader, batch_x, batch_y, epoch, total_iteration)
                    if need_stop:
                        break                
                #end if
            # end for
            self.save_parameters()
            if need_stop:
                break
            # end if
        # end for

        t1 = time.time()
        print("time used:", t1 - t0)

        if need_test:
            print("testing...")
            self.accuracy = self.Test(dataReader)
            print(self.accuracy)