五层深度卷积自编码器代码

五层深度卷积自编码器是一种用于无监督学习的神经网络模型，其主要目的是将输入数据压缩成编码，然后再通过解码器将编码还原成原始数据。下面是一个五层深度卷积自编码器的代码示例：

import tensorflow as tf

# 定义输入和输出的大小
input_size = 784
output_size = 784

# 定义神经网络的结构
def encoder(x):
    conv1 = tf.layers.conv2d(x, filters=32, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool1 = tf.layers.max_pooling2d(conv1, pool_size=(2, 2), strides=(2, 2), padding='same')
    conv2 = tf.layers.conv2d(pool1, filters=64, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool2 = tf.layers.max_pooling2d(conv2, pool_size=(2, 2), strides=(2, 2), padding='same')
    conv3 = tf.layers.conv2d(pool2, filters=128, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool3 = tf.layers.max_pooling2d(conv3, pool_size=(2, 2), strides=(2, 2), padding='same')
    conv4 = tf.layers.conv2d(pool3, filters=256, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool4 = tf.layers.max_pooling2d(conv4, pool_size=(2, 2), strides=(2, 2), padding='same')
    conv5 = tf.layers.conv2d(pool4, filters=512, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    encoded = tf.layers.max_pooling2d(conv5, pool_size=(2, 2), strides=(2, 2), padding='same')
    return encoded

def decoder(x):
    upsample1 = tf.image.resize_nearest_neighbor(x, size=(7, 7))
    conv6 = tf.layers.conv2d(upsample1, filters=256, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    upsample2 = tf.image.resize_nearest_neighbor(conv6, size=(14, 14))
    conv7 = tf.layers.conv2d(upsample2, filters=128, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    upsample3 = tf.image.resize_nearest_neighbor(conv7, size=(28, 28))
    conv8 = tf.layers.conv2d(upsample3, filters=64, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    decoded = tf.layers.conv2d(conv8, filters=output_size, kernel_size=(3, 3), activation=None, padding='same')
    return decoded

# 定义输入和输出的占位符
inputs = tf.placeholder(tf.float32, shape=[None, input_size])
targets = tf.placeholder(tf.float32, shape=[None, output_size])

# 构建模型
encoded = encoder(tf.reshape(inputs, [-1, 28, 28, 1]))
decoded = decoder(encoded)

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.square(targets - decoded))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

# 训练模型
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(1000):
    batch_inputs = # 获取一个batch的输入数据
    batch_targets = # 获取一个batch的输出数据
    _, batch_loss = sess.run([optimizer, loss], feed_dict={inputs: batch_inputs, targets: batch_targets})
    if i % 100 == 0:
        print("Step:", i+1,"Loss:", batch_loss)

# 使用模型进行预测
test_inputs = # 获取测试数据
test_targets = # 获取测试数据的真实标签
test_predictions = sess.run(decoded, feed_dict={inputs: test_inputs})