我们能用Tensorflow严格重现Alexnet网络架构吗？

import tensorflow as tf

# 定义AlexNet网络结构
def alexnet(inputs):
    # 卷积层1
    conv1 = tf.layers.conv2d(inputs, filters=96, kernel_size=11, strides=4, padding='valid', activation=tf.nn.relu)
    pool1 = tf.layers.max_pooling2d(conv1, pool_size=3, strides=2)
    
    # 卷积层2
    conv2 = tf.layers.conv2d(pool1, filters=256, kernel_size=5, strides=1, padding='same', activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling2d(conv2, pool_size=3, strides=2)
    
    # 卷积层3
    conv3 = tf.layers.conv2d(pool2, filters=384, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
    
    # 卷积层4
    conv4 = tf.layers.conv2d(conv3, filters=384, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
    
    # 卷积层5
    conv5 = tf.layers.conv2d(conv4, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
    pool5 = tf.layers.max_pooling2d(conv5, pool_size=3, strides=2)
    
    # 全连接层1
    flatten = tf.layers.flatten(pool5)
    fc1 = tf.layers.dense(flatten, units=4096, activation=tf.nn.relu)
    
    # 全连接层2
    fc2 = tf.layers.dense(fc1, units=4096, activation=tf.nn.relu)
    
    # 输出层
    logits = tf.layers.dense(fc2, units=1000)
    
    return logits

# 输入图像的占位符
inputs = tf.placeholder(tf.float32, [None, 227, 227, 3])

# 构建AlexNet网络
logits = alexnet(inputs)

# 打印网络结构
print(logits)

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