均衡学习率在Keras中的实现

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import Callback

class BalancedLearningRate(Callback):
    def __init__(self, class_weight):
        super(BalancedLearningRate, self).__init__()
        self.class_weight = class_weight

    def on_epoch_begin(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, 'lr'):
            raise ValueError('Optimizer must have a "lr" attribute.')
        current_lr = float(K.get_value(self.model.optimizer.lr))
        for class_label, weight in self.class_weight.items():
            if class_label in logs['class_weight']:
                logs['class_weight'][class_label] = weight * current_lr

# 定义类别权重
class_weight = {0: 1.0, 1: 2.0, 2: 1.5}

# 创建模型
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=100))
model.add(Dense(3, activation='softmax'))

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

# 创建回调函数
balanced_lr = BalancedLearningRate(class_weight)

# 训练模型
model.fit(x_train, y_train, epochs=10, callbacks=[balanced_lr])