Python中的 AdamWeightDecayOptimizer

在TensorFlow中，AdamWeightDecayOptimizer是一种特殊类型的优化器，它继承自Optimizer类。AdamWeightDecayOptimizer是Adam优化器的扩展版本，它引入了权重衰减的概念，有助于减少模型过拟合的可能性。

然而，当你尝试使用AdamWeightDecayOptimizer时，你可能会遇到一个名为“'module' object has no attribute 'Optimizer'”的AttributeError异常。这通常会出现在没有正确导入所需模块时。以下是可能导致出现此异常的情况：

• 未正确导入TensorFlow库
• 没有正确导入tf.train.Optimizer模块

为了解决这个问题，确保你已正确导入TensorFlow并且已经导入tf.train.Optimizer模块和AdamWeightDecayOptimizer类。你可以使用以下代码来确保导入正确：

import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.optimizers.schedules import PolynomialDecay
from tensorflow.python.ops import control_flow_ops

class AdamWeightDecayOptimizer(tf.keras.optimizers.Adam):
    def __init__(self, weight_decay_rate, *args, **kwargs):
        super(AdamWeightDecayOptimizer, self).__init__(*args, **kwargs)
        self.weight_decay_rate = weight_decay_rate

    @tf.function
    def apply_gradients(self, grads_and_vars, name=None):
        grads, tvars = list(zip(*grads_and_vars))
        (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)

        for grad, var in zip(grads, tvars):
            if grad is None:
                continue

            new_grad = grad
            if len(var.shape) > 1:
                new_grad = grad + self.weight_decay_rate * var

            self._create_slots(var)
            slot = self.get_slot(var, 'm')
            beta1_t = self._get_hyper('beta_1')
            beta2_t = self._get_hyper('beta_2')
            epsilon_t = self._get_hyper('epsilon')
            lr_t = self._decayed_lr(var.dtype)

            # m_t = beta1 * m + (1 - beta1) * g_t
            m = slot.assign(beta1_t * slot + (1.0 - beta1_t) * new_grad)

            # v_t = beta2 * v + (1 - beta2) * (g_t - m_t) ** 2
            v = self.get_slot(var, 'v').assign(
                beta2_t * self.get_slot(var, 'v') +
                (1.0 - beta2_t) * tf.square(new_grad - m))

            # variable -= learning_rate * m_t / (sqrt(v_t) + epsilon)
            var_update = m / (tf.sqrt(v) + epsilon_t)
            if self._momentum:
                v_name = self.get_slot(var, 'v').name
                var_update = control_flow_ops.group(*[
                    var_update, self.get_slot(var, 'v').assign(
                        v / (tf.sqrt(v) + epsilon_t))])
            var_update = var_update * lr_t

            if self._clip_norm > 0:
                clip_norm = self._clip_norm
                var_update = tf.clip_by_norm(var_update, clip_norm)
            if self._clip_value > 0:
                clip_value = self._clip_value
                var_update = tf.clip_by_value(var_update, -clip_value,
                                               clip_value)
            var.assign_sub(var_update)

        return tf.group(*[])

上面的代码是AdamWeightDecayOptimizer的实现。如果你需要使用它，请确保导入该类所需的模块，并正确实现它。