请用python写一段用于稀疏自编码器的损失函数代码

当然，我可以为你提供这段代码，如下：

import tensorflow as tf

def sparse_autoencoder_loss(inputs, outputs, sparsity_target, sparsity_weight, reconstruction_weight):
    # calculate squared error loss
    reconstruction_loss = tf.reduce_mean(tf.square(outputs - inputs))

    # calculate sparsity loss
    sparsity_prob = tf.reduce_mean(outputs, axis=0)
    kl_divergence = sparsity_target * tf.math.log(sparsity_target / sparsity_prob) \
                    + (1 - sparsity_target) * tf.math.log((1 - sparsity_target) / (1 - sparsity_prob))
    sparsity_loss = sparsity_weight * tf.reduce_sum(kl_divergence)

    # calculate total loss
    loss = reconstruction_weight * reconstruction_loss + sparsity_loss

    return loss

这段代码实现了一个带有稀疏化约束的自编码器的损失函数。其中，`inputs`为输入数据，`outputs`为重构后的数据，`sparsity_target`为稀疏化约束的目标稀疏度，`sparsity_weight`和`reconstruction_weight`分别为稀疏化约束和重构误差的权重。