堆叠稀疏自编码器SSAE的python代码编写，数据集使用minist手写体识别

以下是堆叠稀疏自编码器(SSAE)的Python代码，使用MNIST手写数字数据集：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

# 加载MNIST数据集
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# 定义超参数
learning_rate = 0.01
training_epochs = 20
batch_size = 256
display_step = 1

# 定义输入大小
n_input = 784

# 定义隐藏层大小
n_hidden_1 = 256
n_hidden_2 = 128
n_hidden_3 = 64

# 定义占位符
X = tf.placeholder("float", [None, n_input])

# 定义权重和偏置项
weights = {
    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'encoder_h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),
    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_2])),
    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
    'decoder_h3': tf.Variable(tf.random_normal([n_hidden_1, n_input])),
}
biases = {
    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),
    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_2])),
    'decoder_b2': tf.Variable(tf.random_normal([n_hidden_1])),
    'decoder_b3': tf.Variable(tf.random_normal([n_input])),
}

# 定义编码器
def encoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2']))
    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']), biases['encoder_b3']))
    return layer_3

# 定义解码器
def decoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2']))
    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']), biases['decoder_b3']))
    return layer_3

# 构建模型
encoder_op = encoder(X)
decoder_op = decoder(encoder_op)

# 定义损失函数和优化器
cost = tf.reduce_mean(tf.pow(X - decoder_op, 2))
optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)

# 初始化变量
init = tf.global_variables_initializer()

# 运行图
with tf.Session() as sess:
    sess.run(init)
    total_batch = int(mnist.train.num_examples/batch_size)
    # 开始训练
    for epoch in range(training_epochs):
        # 遍历所有批次
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # 运行优化器
            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})
        # 显示训练日志
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c))
    print("Optimization Finished!")
    
    # 测试模型
    encode_decode = sess.run(
        decoder_op, feed_dict={X: mnist.test.images[:10]})
    # 显示重构结果
    f, a = plt.subplots(2, 10, figsize=(10, 2))
    for i in range(10):
        a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))
        a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))
    plt.show()

这段代码实现了一个三层的堆叠稀疏自编码器(SSAE)，使用MNIST手写数字数据集进行训练和测试。在训练过程中，使用RMSProp优化器和均方误差损失函数来训练模型。最后，测试模型，并展示重构结果。