def train()

in #U57fa#U7840#U6559#U7a0b/A2-#U795e#U7ecf#U7f51#U7edc#U57fa#U672c#U539f#U7406/#U7b2c5#U6b65 - #U975e#U7ebf#U6027#U5206#U7c7b/src/ch10-NonLinearBinaryClassification/HelperClass2/NeuralNet_2_1.py [0:0]

• 27 lines of code
• 15 McCabe index (conditional complexity)

    def train(self, dataReader, checkpoint, need_test):
        # calculate loss to decide the stop condition
        self.loss_trace = TrainingHistory_2_1()
        self.loss_func = LossFunction_1_1(self.hp.net_type)
        if self.hp.batch_size == -1:
            self.hp.batch_size = dataReader.num_train
        max_iteration = math.ceil(dataReader.num_train / self.hp.batch_size)
        checkpoint_iteration = (int)(max_iteration * checkpoint)
        need_stop = False
        for epoch in range(self.hp.max_epoch):
            #print("epoch=%d" %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)
                # get z from x,y
                batch_a = self.forward(batch_x)
                # calculate gradient of w and b
                self.backward(batch_x, batch_y, batch_a)
                # 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 if
            # end for
            if need_stop:
                break
        # end for
        self.SaveResult()
        #self.CheckErrorAndLoss(dataReader, batch_x, batch_y, epoch, total_iteration)
        if need_test:
            print("testing...")
            accuracy = self.Test(dataReader)
            print(accuracy)