keras利用callback获取的每个batch的acc数据精度不足

我想利用callback收集训练过程中每个batch的acc数据
但按batch收集的acc只有小数点后两位，按epoch收集的acc数据与就保留了小数点后很多位，按batch和epoch收集的loss数据都保留了小数点后很多位

代码如下

class LossHistory(callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = {'batch': [], 'epoch': []}
        self.accuracy = {'batch': [], 'epoch': []}
        self.val_loss = {'batch': [], 'epoch': []}
        self.val_acc = {'batch': [], 'epoch': []}

    def on_batch_end(self, batch, logs={}):
        self.losses['batch'].append(logs.get('loss'))
        self.accuracy['batch'].append(logs.get('acc'))
        self.val_loss['batch'].append(logs.get('val_loss'))
        self.val_acc['batch'].append(logs.get('val_acc'))

    def on_epoch_end(self, batch, logs={}):
        self.losses['epoch'].append(logs.get('loss'))
        self.accuracy['epoch'].append(logs.get('acc'))
        self.val_loss['epoch'].append(logs.get('val_loss'))
        self.val_acc['epoch'].append(logs.get('val_acc'))

    def loss_plot(self, loss_type):
        iters = range(len(self.losses[loss_type]))
        plt.figure()
        # acc
        plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')
        # loss
        plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
        if loss_type == 'epoch':
            # val_acc
            plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
            # val_loss
            plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
        plt.grid(True)
        plt.xlabel(loss_type)
        plt.ylabel('acc-loss')
        plt.legend(loc="upper right")
        plt.show()

class Csr:
    def __init__(self,voc):
        self.model = Sequential()
        #B*L
        self.model.add(Embedding(voc.num_words,
                            300,
                            mask_zero = True,
                            weights = [voc.index2emb],
                            trainable = False))
        #B*L*256
        self.model.add(GRU(256))
        #B*256
        self.model.add(Dropout(0.5))
        self.model.add(Dense(1, activation='sigmoid'))

        #B*1
        self.model.compile(loss='binary_crossentropy',
                      optimizer='rmsprop',
                      metrics=['accuracy'])
        print('compole complete')
    def train(self, x_train, y_train, b_s=50, epo=10):
        print('training.....')
        history = LossHistory()
        his = self.model.fit(x_train, 
                                                                            y_train,
                                                                            batch_size=b_s,
                                                                            epochs=epo,
                                                                            callbacks=[history])
        history.loss_plot('batch')
        print('training complete')
        return his, history

程序运行结果如下：

不知道你这个问题是否已经解决, 如果还没有解决的话:

• 关于该问题，我找了一篇非常好的博客，你可以看看是否有帮助，链接：Keras中如何利用回调函数Callback设置训练前先验证一遍验证集数据

如果你已经解决了该问题, 非常希望你能够分享一下解决方案, 以帮助更多的人 ^-^