在自定义Keras回调中使用super

from keras.callbacks import Callback

class AccuracyCallback(Callback):
    def __init__(self):
        super(AccuracyCallback, self).__init__()

    def on_epoch_end(self, epoch, logs=None):
        super(AccuracyCallback, self).on_epoch_end(epoch, logs)
        train_acc = logs.get('accuracy')
        val_acc = logs.get('val_accuracy')
        print(f'Training accuracy: {train_acc}, Validation accuracy: {val_acc}')

from keras.models import Sequential
from keras.layers import Dense
from keras.datasets import mnist
from keras.utils import to_categorical

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 数据预处理
x_train = x_train.reshape(-1, 784) / 255.0
x_test = x_test.reshape(-1, 784) / 255.0
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)

# 构建模型
model = Sequential()
model.add(Dense(units=64, activation='relu', input_shape=(784,)))
model.add(Dense(units=10, activation='softmax'))

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 添加自定义回调
model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test), callbacks=[AccuracyCallback()])