Fit_generator 在纪元结束时产生两个详细信息

如何解决Fit_generator 在纪元结束时产生两个详细信息

我正在使用类似的混合生成器

import numpy as np
from tensorflow.keras.utils import Sequence

class MixupGenerator(Sequence):
    def __init__(self,x_train,y_train,batch_size=32,alpha=0.2,shuffle=True):
        self.X_train = x_train
        self.y_train = y_train
        self.batch_size = batch_size
        self.alpha = alpha
        self.shuffle = shuffle
        self.sample_num = len(x_train)
        self.lock = threading.Lock()

    def __iter__(self):
        return self

    #@threadsafe_generator
    def __call__(self):
        with self.lock:
            while True:
                indexes = self.__get_exploration_order()
                itr_num = int(len(indexes) // (self.batch_size * 2))

                for i in range(itr_num):
                    batch_ids = indexes[i * self.batch_size * 2:(i + 1) * self.batch_size * 2]
                    X,y = self.__data_generation(batch_ids)

                    yield X,y

    def __get_exploration_order(self):
        indexes = np.arange(self.sample_num)

        if self.shuffle:
            np.random.shuffle(indexes)

        return indexes

    def __data_generation(self,batch_ids):
        _,h,w,c = self.X_train.shape
        l = np.random.beta(self.alpha,self.alpha,self.batch_size)
        X_l = l.reshape(self.batch_size,1,1)
        y_l = l.reshape(self.batch_size,1)

        X1 = self.X_train[batch_ids[:self.batch_size]]
        X2 = self.X_train[batch_ids[self.batch_size:]]

        X = X1 * X_l + X2 * (1.0 - X_l)

        if isinstance(self.y_train,list):
            y = []

            for y_train_ in self.y_train:
                y1 = y_train_[batch_ids[:self.batch_size]]
                y2 = y_train_[batch_ids[self.batch_size:]]
                y.append(y1 * y_l + y2 * (1.0 - y_l))
        else:
            y1 = self.y_train[batch_ids[:self.batch_size]]
            y2 = self.y_train[batch_ids[self.batch_size:]]
            y = y1 * y_l + y2 * (1.0 - y_l)

        return X,y

我在训练期间有 13965 个样本，在测试期间有 2970 个样本。我称之为适合：

history = model.fit_generator(train_datagen,validation_data=(val_x,val_y),epochs=epochs,steps_per_epoch=np.ceil((x.shape[0] - 1) / config.batch_size),callbacks=callbacks,verbose=tr_verbose)

batch_size = 32

verbose 比较少，是不是因为 epochs 和 batch size 是十进制的？

时代 49/500 436/437 [============================>.] - ETA：0s - 损失：0.1408 - categorical_accuracy：0.8295Epoch 1/ 500 2968/437 [============================================== ================================================== ================================================== ================================================== ========] - 6s 2ms/sample - 损失：0.2304 - categorical_accuracy：0.5162 437/437 [==============================] - 131 秒 299 毫秒/步 - 损失：0.1409 - categorical_accuracy：0.8294 - val_loss ：0.2510 - val_categorical_accuracy：0.5162