AlexNet_加载ImageNet上的预训练模型_tensorflow版本

文章目录

* 1. 下载 Alexnet的预训练模型参数
<https://blog.csdn.net/Herrerra/article/details/83189153#1__Alexnet_12>
* 2. 定义Alexnet网络模型
<https://blog.csdn.net/Herrerra/article/details/83189153#2_Alexnet_16>
* 3. 加载模型参数 <https://blog.csdn.net/Herrerra/article/details/83189153#3__162>
在tensorflow的GitHub仓库
<https://github.com/tensorflow/models/tree/master/research/slim>
中没有直接给出Alexnet在ImageNet上的预训练模型供tensorflow调用。本文主要通过参考以下资料，实现了使用tensorflow加载在ImageNet上预训练好的其它格式保存的Alexnet模型：


* https://github.com/stephen-v/tensorflow_alexnet_classify
<https://github.com/stephen-v/tensorflow_alexnet_classify>
* http://www.cnblogs.com/vipyoumay/p/7686230.html
<http://www.cnblogs.com/vipyoumay/p/7686230.html>
关键步骤分为以下几步：

* 下载 Alexnet的预训练模型参数
* 定义Alexnet网络模型
* 加载模型参数
<>1. 下载 Alexnet的预训练模型参数

下载地址为：http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/
<http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/>
，所有的参数数据存放在bvlc_alexnet.npy文件中。

<>2. 定义Alexnet网络模型


对网络模型定义的时候，关键在于变量的name、shape、dtype等属性的设置，要与bvlc_alexnet.npy文件中保存的一致。个人猜测是因为在加载恢复Alexnet网络模型的时候，代码中定义的变量要与文件中保存的变量一一对应。定义Alexnet代码如下
(可根据需要灵活修改，只要不改变所定义变量的name、shape、dtype等属性)：
def alexnet(x, keep_prob, num_classes=1): # conv1 with tf.variable_scope(
'conv1') as scope: kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96],
dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(x, kernel, [
1, 4, 4, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[96],
dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv,
biases) conv1 = tf.nn.relu(bias, name='conv1') # lrn1 with tf.variable_scope(
'lrn1') as scope: lrn1 = tf.nn.local_response_normalization(conv1, alpha=1e-4,
beta=0.75, depth_radius=2, bias=2.0) # pool1 with tf.variable_scope('pool1') as
scope: pool1 = tf.nn.max_pool(lrn1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
padding='VALID') # conv2 with tf.variable_scope('conv2') as scope: pool1_groups
= tf.split(axis=3, value=pool1, num_or_size_splits=2) kernel = tf.Variable(tf.
truncated_normal([5, 5, 48, 256], dtype=tf.float32, stddev=1e-1), name='weights'
) kernel_groups = tf.split(axis=3, value=kernel, num_or_size_splits=2) conv_up =
tf.nn.conv2d(pool1_groups[0], kernel_groups[0], [1, 1, 1, 1], padding='SAME')
conv_down= tf.nn.conv2d(pool1_groups[1], kernel_groups[1], [1, 1, 1, 1], padding
='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases') biases_groups = tf.split(axis=0, value=biases,
num_or_size_splits=2) bias_up = tf.nn.bias_add(conv_up, biases_groups[0])
bias_down= tf.nn.bias_add(conv_down, biases_groups[1]) bias = tf.concat(axis=3,
values=[bias_up, bias_down]) conv2 = tf.nn.relu(bias, name='conv2') # lrn2 with
tf.variable_scope('lrn2') as scope: lrn2 = tf.nn.local_response_normalization(
conv2, alpha=1e-4, beta=0.75, depth_radius=2, bias=2.0) # pool2 with tf.
variable_scope('pool2') as scope: pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1
], strides=[1, 2, 2, 1], padding='VALID') # conv3 with tf.variable_scope('conv3'
) as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 384], dtype=tf.
float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(pool2, kernel, [1, 1,
1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=
tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases)
conv3= tf.nn.relu(bias, name='conv3') # conv4 with tf.variable_scope('conv4') as
scope: conv3_groups = tf.split(axis=3, value=conv3, num_or_size_splits=2)
kernel= tf.Variable(tf.truncated_normal([3, 3, 192, 384], dtype=tf.float32,
stddev=1e-1), name='weights') kernel_groups = tf.split(axis=3, value=kernel,
num_or_size_splits=2) conv_up = tf.nn.conv2d(conv3_groups[0], kernel_groups[0],
[1, 1, 1, 1], padding='SAME') conv_down = tf.nn.conv2d(conv3_groups[1],
kernel_groups[1], [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant
(0.0, shape=[384], dtype=tf.float32), trainable=True, name='biases')
biases_groups= tf.split(axis=0, value=biases, num_or_size_splits=2) bias_up = tf
.nn.bias_add(conv_up, biases_groups[0]) bias_down = tf.nn.bias_add(conv_down,
biases_groups[1]) bias = tf.concat(axis=3, values=[bias_up, bias_down]) conv4 =
tf.nn.relu(bias, name='conv4') # conv5 with tf.variable_scope('conv5') as scope:
conv4_groups= tf.split(axis=3, value=conv4, num_or_size_splits=2) kernel = tf.
Variable(tf.truncated_normal([3, 3, 192, 256], dtype=tf.float32, stddev=1e-1),
name='weights') kernel_groups = tf.split(axis=3, value=kernel,
num_or_size_splits=2) conv_up = tf.nn.conv2d(conv4_groups[0], kernel_groups[0],
[1, 1, 1, 1], padding='SAME') conv_down = tf.nn.conv2d(conv4_groups[1],
kernel_groups[1], [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant
(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases')
biases_groups= tf.split(axis=0, value=biases, num_or_size_splits=2) bias_up = tf
.nn.bias_add(conv_up, biases_groups[0]) bias_down = tf.nn.bias_add(conv_down,
biases_groups[1]) bias = tf.concat(axis=3, values=[bias_up, bias_down]) conv5 =
tf.nn.relu(bias, name='conv5') # pool5 with tf.variable_scope('pool5') as scope:
pool5= tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding=
'VALID', ) # flattened6 with tf.variable_scope('flattened6') as scope: flattened
= tf.reshape(pool5, shape=[-1, 6 * 6 * 256]) # fc6 with tf.variable_scope('fc6')
as scope: weights = tf.Variable(tf.truncated_normal([6 * 6 * 256, 100], dtype=tf
.float32, stddev=1e-1), name='weights') biases = tf.Variable(tf.constant(0.0,
shape=[100], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.
xw_plus_b(flattened, weights, biases) fc6 = tf.nn.relu(bias) # dropout6 with tf.
variable_scope('dropout6') as scope: dropout6 = tf.nn.dropout(fc6, keep_prob) #
fc7 with tf.variable_scope('fc7') as scope: weights = tf.Variable(tf.
truncated_normal([100, 1], dtype=tf.float32, stddev=1e-1), name='weights')
biases= tf.Variable(tf.constant(0.0, shape=[1], dtype=tf.float32), trainable=
True, name='biases') bias = tf.nn.xw_plus_b(dropout6, weights, biases) fc7 = tf.
nn.relu(bias) return fc7
<>3. 加载模型参数

加载模型参数的时候，可以只加载卷积层的参数，也可以加载全部的参数，根据需要自行决定。首先定义加载参数的函数：
def load_initial_weights(session): """ :param session: It's need to define
tf.Session() first to run the graph of tensorflow. :return: """ # Load the
weights into memory. # WEIGHTS_PATH: 存放 bvlc_alexnet.npy 文件的路径 weights_dict = np
.load(os.path.join(WEIGHTS_PATH, 'bvlc_alexnet.npy'), encoding='bytes').item()
# Loop over all layer names stored in the weights dict for op_name in
weights_dict: # Check if layer should be trained from scratch # SKIP_LAYER：
指定对某些层的参数不进行恢复，比如只恢复卷积层的参数，就设为： SKIP_LAYER = ['fc6', 'fc7', 'fc8'] # 一定要注意
variable_scope 的相对范围。 if op_name not in SKIP_LAYER: with tf.variable_scope(
op_name, reuse=True): # Assign weights/biases to their corresponding tf variable
for data in weights_dict[op_name]: # Biases if len(data.shape) == 1: # var =
tf.get_variable('biases') 会报错 var = slim.get_unique_variable(op_name + '/biases'
) session.run(var.assign(data)) # Weights else: # var =
tf.get_variable('weights') var = slim.get_unique_variable(op_name + '/weights')
session.run(var.assign(data))
加载模型的示例代码如下：
sess.run(init) # print(sess.run(slim.get_unique_variable('conv1/biases')))
load_initial_weights(sess) #
print(sess.run(slim.get_unique_variable('conv1/biases'))) check_init = tf.
report_uninitialized_variables() assert sess.run(check_init).size == 0

本文主要介绍了一种在tensorflow上加载其它格式（非ckpt格式）的Alexnet预训练模型的方法。在预训练模型的基础上，可以根据自己的任务需要修改loss函数、全连接层设置等进行fine
tune。

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