可以给一段使用批归一化搭建的神经网络模型代码吗

当然可以，以下是一个简单的使用批归一化的神经网络模型代码示例：

import tensorflow as tf

def batch_norm_layer(input_tensor, is_training=True, epsilon=1e-5, decay=0.9):
    '''
    特征归一化层：也就是我们说的批量归一化层
    '''
    shape = input_tensor.get_shape().as_list()
    if len(shape) == 4:
        mean, var = tf.nn.moments(input_tensor, [0, 1, 2])
        beta = tf.Variable(tf.zeros([shape[-1]]))
        gamma = tf.Variable(tf.ones([shape[-1]]))
        moving_mean = tf.Variable(tf.zeros([shape[-1]]), trainable=False)
        moving_var = tf.Variable(tf.ones([shape[-1]]), trainable=False)
        if is_training:
            ema = tf.train.ExponentialMovingAverage(decay=decay)
            def mean_var_with_update():
                ema_apply_op = ema.apply([mean, var])
                with tf.control_dependencies([ema_apply_op]):
                    return tf.identity(mean), tf.identity(var)
            mean, var = mean_var_with_update()
            tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, ema_apply_op)
        else:
            mean, var = moving_mean, moving_var
        output_tensor = tf.nn.batch_normalization(input_tensor, mean, var, beta, gamma, epsilon)
    return output_tensor

def conv_bn_relu(name, input_tensor, kernel_shape, stride, padding='SAME', is_training=True):
    '''
    卷积层+特征归一化层+relu激活层：常用的卷积层组成，对于某些深度较大的网络，这种组合可以减少内部协方差偏移，加速训练速度。
    '''
    n_filters_in = input_tensor.get_shape().as_list()[-1]
    with tf.variable_scope(name):
        kernel = tf.get_variable('kernel', kernel_shape, initializer=tf.random_normal_initializer())
        conv_output = tf.nn.conv2d(input_tensor, kernel, strides=[1, stride, stride, 1], padding=padding)
        bn_output = batch_norm_layer(conv_output, is_training=is_training)
        relu_output = tf.nn.relu(bn_output)
    return relu_output

def dense_bn_relu(name, input_tensor, output_dim, is_training=True):
    '''
    全连接层+特征归一化层+relu激活层：常用的全连接层组成，同样可以减少内部协方差偏移，加速训练速度。
    '''
    n_inputs = input_tensor.get_shape().as_list()[-1]
    with tf.variable_scope(name):
        weights = tf.get_variable('weights', shape=[n_inputs, output_dim], initializer=tf.random_normal_initializer())
        biases = tf.Variable(tf.zeros([output_dim]))
        dense_output = tf.matmul(input_tensor, weights) + biases
        bn_output = batch_norm_layer(dense_output, is_training=is_training)
        relu_output = tf.nn.relu(bn_output)
    return relu_output

def create_model(input_shape, n_classes):
    '''
    创建神经网络模型，使用了4个卷积层和2个全连接层，其中卷积层和全连接层均使用了批归一化和relu激活函数，
    在最后输出层使用sigmoid函数进行二分类。
    '''
    input_tensor = tf.placeholder(tf.float32, shape=[None, *input_shape], name='input')
    y_true = tf.placeholder(tf.float32, shape=[None, n_classes], name='y_true')
    is_training = tf.placeholder(tf.bool, name='is_training')

    x = input_tensor

    x = conv_bn_relu('conv1', x, kernel_shape=[3, 3, input_shape[-1], 32], stride=1, is_training=is_training)
    x = conv_bn_relu('conv2', x, kernel_shape=[3, 3, 32, 64], stride=2, is_training=is_training)
    x = conv_bn_relu('conv3', x, kernel_shape=[3, 3, 64, 128], stride=2, is_training=is_training)
    x = conv_bn_relu('conv4', x, kernel_shape=[3, 3, 128, 256], stride=2, is_training=is_training)

    x = tf.reduce_mean(x, [1, 2])
    x = dense_bn_relu('dense1', x, output_dim=64, is_training=is_training)
    x = dense_bn_relu('dense2', x, output_dim=32, is_training=is_training)

    y_pred = tf.layers.dense(x, n_classes, activation=tf.nn.sigmoid, name='output')

    return input_tensor, y_true, y_pred, is_training

希望这个示例对你有所帮助。