一：AlexNet网络结构在2012年ImageNet图像分类任务竞赛中AlexNet一鸣惊人，对128万张1000个分类的预测结果大大超过其他算法模型准确率，打败其它非DNN网络一鸣惊人。AlexNet包括5个卷积层与三个全连接层，与今天动则十几层、几十层甚至成百上千层相比，简直是太简单、太容易理解啦。AlexNet网络一共有八层。前面5层是卷积层，后面3层是全连接层，整个网络结构显示如下： 
各个层结构如下： 
输入图像大小为224x244x3 的彩色RGB图像 卷积层与池化层步长与填充方式：
采用ReLU激活函数，基于CIFAR-10数据集，训练收敛速度相比tanh激活函数提升6倍。图示如下： 
作者在2GPU上进行训练，所以paper中对上述完整的网络结构进行了差分，分别在2个GTX580 GPU上运行，基于ILSVRC-2000与ILSVRC-2012数据集进行了测试。很多文章中不加说明的将作者Paper中网络结构贴到文章中以后看文章了解AlexNet的读者都一头雾水，因为文章内容描述跟网络结构根本对不上，因此误导了不少人。 二：AlexNet网络实现基于tensorflow，很容易实现一个AlexNet网络，本人把它定义成一个单独的Python类，方便大家创建使用它，完整的AlexNet网络代码实现如下 import tensorflow as tf
class AlexNet_CNN:
def __init__(self, x, keep_prob, num_class, skip_layer):
self.X = x
self.KEEP_PROB = keep_prob
self.NUM_CLASS = num_class
self.SKIP_LAYER = skip_layer
print('AlexNet Network...')
def create(self):
with tf.name_scope('conv1') as scope:
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96], dtype=tf.float32, stddev=1e-1), name='weights1')
conv1 = tf.nn.conv2d(self.X, kernel, strides=[1, 4, 4, 1], padding='VALID')
lrn1 = tf.nn.lrn(conv1,depth_radius=2,bias=1.0,alpha=1e-05,beta=0.75)
pool1 = tf.nn.max_pool(lrn1, ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME', name='pool1')
print('pool1', pool1.shape)
with tf.name_scope('conv2') as scope:
kernel = tf.Variable(tf.truncated_normal([5, 5, 96, 256], dtype=tf.float32, stddev=1e-1), name='weights2')
conv2 = tf.nn.conv2d(pool1, kernel, strides=[1, 1, 1, 1], padding='SAME')
lrn2 = tf.nn.lrn(conv2, depth_radius=2, bias=1.0, alpha=1e-05, beta=0.75)
pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID', name='pool2')
print('pool2',pool2.shape)
with tf.name_scope('conv3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 384], dtype=tf.float32, stddev=1e-1), name='weights3')
conv3 = tf.nn.conv2d(pool2, kernel, strides=[1, 1, 1, 1], padding='SAME')
print('conv3',conv3.shape)
with tf.name_scope('conv4') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 384], dtype=tf.float32, stddev=1e-1), name='weights4')
conv4 = tf.nn.conv2d(conv3, kernel, strides=[1, 1, 1, 1], padding='SAME')
print('conv4',conv4.shape)
with tf.name_scope('conv5') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256], dtype=tf.float32, stddev=1e-1), name='weights5')
conv5 = tf.nn.conv2d(conv4, kernel, strides=[1, 1, 1, 1], padding='SAME')
pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID', name='pool5')
with tf.name_scope('fc6') as scope:
print('pool5', pool5.shape)
flattened = tf.reshape(pool5, [-1, 6 * 6 * 256])
weights = tf.Variable(tf.random_normal([6*6*256, 4096]))
biases = tf.Variable(tf.random_normal([4096]))
# Matrix multiply weights and inputs and add bias
act = tf.nn.xw_plus_b(flattened, weights, biases, name='fc6')
fc6 = tf.nn.relu(act)
dp6 = tf.nn.dropout(fc6,keep_prob=self.KEEP_PROB)
with tf.name_scope('fc7') as scope:
weights = tf.Variable(tf.random_normal([4096, 4096]))
biases = tf.Variable(tf.random_normal([4096]))
# Matrix multiply weights and inputs and add bias
act = tf.nn.xw_plus_b(dp6, weights, biases, name='fc7')
fc7 = tf.nn.relu(act)
dp7 = tf.nn.dropout(fc7, keep_prob=self.KEEP_PROB)
with tf.name_scope('fc8') as scope:
weights = tf.Variable(tf.random_normal([4096, self.NUM_CLASS]))
biases = tf.Variable(tf.random_normal([self.NUM_CLASS]))
# Matrix multiply weights and inputs and add bias
act = tf.nn.xw_plus_b(dp7, weights, biases, name='fc8')
return act
运行之后结构显示：
卷积层输出与论文上完全一致。
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