结合神经网络去噪的ADMM代码python

我可以回答这个问题。以下是一个结合神经网络去噪的ADMM代码python的示例：

import numpy as np
import tensorflow as tf

# 定义神经网络模型
def denoising_autoencoder(input_shape):
    input_layer = tf.keras.layers.Input(shape=input_shape)
    encoded = tf.keras.layers.Dense(128, activation='relu')(input_layer)
    encoded = tf.keras.layers.Dense(64, activation='relu')(encoded)
    encoded = tf.keras.layers.Dense(32, activation='relu')(encoded)
    decoded = tf.keras.layers.Dense(64, activation='relu')(encoded)
    decoded = tf.keras.layers.Dense(128, activation='relu')(decoded)
    decoded = tf.keras.layers.Dense(input_shape, activation='sigmoid')(decoded)
    autoencoder = tf.keras.models.Model(inputs=input_layer, outputs=decoded)
    return autoencoder

# 定义ADMM算法
def admm_denoising_autoencoder(X, rho, alpha, max_iter):
    # 初始化变量
    m, n = X.shape
    z = np.zeros((m, n))
    u = np.zeros((m, n))

    # 定义神经网络模型
    autoencoder = denoising_autoencoder((n,))
    autoencoder.compile(optimizer='adam', loss='mse')

    # 迭代更新
    for i in range(max_iter):
        # 更新x
        x = np.linalg.inv(np.eye(n) + rho * np.eye(n)) @ (z - u)

        # 更新z
        z = autoencoder.predict(x)
        z = np.clip(z, 0, 1)

        # 更新u
        u = u + alpha * (z - x)

    return z

# 测试代码
X = np.random.rand(100, 784)
rho = 1
alpha = 1
max_iter = 10
Z = admm_denoising_autoencoder(X, rho, alpha, max_iter)