保留分辨率的全卷积网络（Fully Convolutional Network, FCN）是一种用于图像语义分割的神经网络模型。它可以将输入图像的每个像素映射到对应的语义类别，同时保留输入图像的分辨率。

以下是一个使用TensorFlow实现的保留分辨率的全卷积网络的示例代码：

import tensorflow as tf

def fcn_model(input_shape, num_classes):
    # 定义输入占位符
    input_tensor = tf.placeholder(tf.float32, shape=input_shape, name='input')
    
    # 定义网络结构
    conv1 = tf.layers.conv2d(inputs=input_tensor, filters=64, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    conv2 = tf.layers.conv2d(inputs=conv1, filters=64, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool1 = tf.layers.max_pooling2d(inputs=conv2, pool_size=(2, 2), strides=(2, 2))
    
    conv3 = tf.layers.conv2d(inputs=pool1, filters=128, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    conv4 = tf.layers.conv2d(inputs=conv3, filters=128, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool2 = tf.layers.max_pooling2d(inputs=conv4, pool_size=(2, 2), strides=(2, 2))
    
    conv5 = tf.layers.conv2d(inputs=pool2, filters=256, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    conv6 = tf.layers.conv2d(inputs=conv5, filters=256, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    conv7 = tf.layers.conv2d(inputs=conv6, filters=256, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool3 = tf.layers.max_pooling2d(inputs=conv7, pool_size=(2, 2), strides=(2, 2))
    
    conv8 = tf.layers.conv2d(inputs=pool3, filters=512, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    conv9 = tf.layers.conv2d(inputs=conv8, filters=512, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    conv10 = tf.layers.conv2d(inputs=conv9, filters=512, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool4 = tf.layers.max_pooling2d(inputs=conv10, pool_size=(2, 2), strides=(2, 2))
    
    conv11 = tf.layers.conv2d(inputs=pool4, filters=512, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    conv12 = tf.layers.conv2d(inputs=conv11, filters=512, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    conv13 = tf.layers.conv2d(inputs=conv12, filters=512, kernel_size=(3, 3), activation=tf.nn.relu, padding='same')
    pool5 = tf.layers.max_pooling2d(inputs=conv13, pool_size=(2, 2), strides=(2, 2))
    
    # 上采样
    upconv1 = tf.layers.conv2d_transpose(inputs=pool5, filters=512, kernel_size=(3, 3), strides=(2, 2), padding='same')
    upconv2 = tf.layers.conv2d_transpose(inputs=upconv1, filters=256, kernel_size=(3, 3), strides=(2, 2), padding='same')
    upconv3 = tf.layers.conv2d_transpose(inputs=upconv2, filters=128, kernel_size=(3, 3), strides=(2, 2), padding='same')
    upconv4 = tf.layers.conv2d_transpose(inputs=upconv3, filters=64, kernel_size=(3, 3), strides=(2, 2), padding='same')
    
    # 最后一层卷积输出语义类别
    logits = tf.layers.conv2d(inputs=upconv4, filters=num_classes, kernel_size=(1, 1), padding='same')