Tensorflow深度学习之二十二：AlexNet的实现（CIFAR-10数据集）

一、模型



模型向前向后传播时间的计算请参考：Tensorflow深度学习之十：Tensorflow实现经典卷积神经网络AlexNet

二、工程结构

由于我自己训练的机器内存显存不足，不能一次性读取10000张图片，因此，在这之前我按照图片的类别，将每一张图片都提取了出来，保存成了jpg格式。与此同时，在保存图片的过程中，存储了一个python的dict结构，键为每一张图片的相对地址，值为每一张图片对应的类别，将这个dict结构保存成npy文件。每一张jpg图片的大小为32*32，而AlexNet需要的输入为224*224，所以在读取图片的时候需要使用cv2.resize进行图片分辨率的调整。

分别对训练集和测试集做以上操作。得到的工程目录如下所示：



每个文件和文件夹的作用显示如下：

文件	作用
AlexNet文件夹	保存相关日志的文件夹
cifar-10-python文件夹	保存CIFAR-10数据集的源文件
data\test	测试集数据
data\train	训练集数据，按照标签分成十类，分别存储在0~9的文件夹内，test文件夹也是一样
model文件夹	保存模型的目录
AlexNet.py	建立AlexNet网络结构和训练
AlexNetPrediction.py	使用训练好的模型进行预测
label.npy	保存训练集的文件名与标签的文件，是一个dict
test-label.npy	保存测试集的文件名与标签的文件，是一个dict

三，训练代码

import tensorflow as tf
import numpy as np
import random
import cv2

# 将传入的label转换成one hot的形式。
def getOneHotLabel(label, depth):
m = np.zeros([len(label), depth])
for i in range(len(label)):
m[i][label[i]] = 1
return m

# 建立神经网络。
def alexnet(image, keepprob=0.5):

# 定义卷积层1，卷积核大小，偏置量等各项参数参考下面的程序代码，下同。
with tf.name_scope("conv1") as scope:
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32, stddev=1e-1, name="weights"))
conv = tf.nn.conv2d(image, kernel, [1, 4, 4, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[64]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope)

pass

# LRN层
lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001/9, beta=0.75, name="lrn1")

# 最大池化层
pool1 = tf.nn.max_pool(lrn1, ksize=[1,3,3,1], strides=[1,2,2,1],padding="VALID", name="pool1")

# 定义卷积层2
with tf.name_scope("conv2") as scope:
kernel = tf.Variable(tf.truncated_normal([5,5,64,192], dtype=tf.float32, stddev=1e-1, name="weights"))
conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[192]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias, name=scope)
pass

# LRN层
lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9, beta=0.75, name="lrn2")

# 最大池化层
pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID", name="pool2")

# 定义卷积层3
with tf.name_scope("conv3") as scope:
kernel = tf.Variable(tf.truncated_normal([3,3,192,384], dtype=tf.float32, stddev=1e-1<
4000
/span>, name="weights"))
conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[384]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv3 = tf.nn.relu(bias, name=scope)
pass

# 定义卷积层4
with tf.name_scope("conv4") as scope:
kernel = tf.Variable(tf.truncated_normal([3,3,384,256], dtype=tf.float32, stddev=1e-1, name="weights"))
conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv4 = tf.nn.relu(bias, name=scope)
pass

# 定义卷积层5
with tf.name_scope("conv5") as scope:
kernel = tf.Variable(tf.truncated_normal([3,3,256,256], dtype=tf.float32, stddev=1e-1, name="weights"))
conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv5 = tf.nn.relu(bias, name=scope)
pass

# 最大池化层
pool5 = tf.nn.max_pool(conv5, ksize=[1,3,3,1], strides=[1,2,2,1], padding="VALID", name="pool5")

# 全连接层
flatten = tf.reshape(pool5, [-1, 6*6*256])

weight1 = tf.Variable(tf.truncated_normal([6*6*256, 4096], mean=0, stddev=0.01))

fc1 = tf.nn.sigmoid(tf.matmul(flatten, weight1))

dropout1 = tf.nn.dropout(fc1, keepprob)

weight2 = tf.Variable(tf.truncated_normal([4096, 4096], mean=0, stddev=0.01))

fc2 = tf.nn.sigmoid(tf.matmul(dropout1, weight2))

dropout2 = tf.nn.dropout(fc2, keepprob)

weight3 = tf.Variable(tf.truncated_normal([4096, 10], mean=0, stddev=0.01))

fc3 = tf.nn.sigmoid(tf.matmul(dropout2, weight3))

return fc3

def alexnet_main():
# 加载使用的训练集文件名和标签。
files = np.load("label.npy", encoding='bytes')[()]

# 提取文件名。
keys = [i for i in files]

print(len(keys))

myinput = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3], name='input')
mylabel = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='label')

# 建立网络，keepprob为0.6。
myoutput = alexnet(myinput, 0.6)

# 定义训练的loss函数。
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=myoutput, labels=mylabel))

# 定义优化器，学习率设置为0.09，学习率可以设置为其他的数值。
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.09).minimize(loss)

# 定义准确率
valaccuracy = tf.reduce_mean(
tf.cast(
tf.equal(
tf.argmax(myoutput, 1),
tf.argmax(mylabel, 1)),
tf.float32))

# tensorflow的saver，可以用于保存模型。
saver = tf.train.Saver()
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
# 40个epoch
for loop in range(40):

# 生成并打乱训练集的顺序。
indices = np.arange(50000)
random.shuffle(indices)

# batch size此处定义为200。
# 训练集一共50000张图片，前40000张用于训练，后10000张用于验证集。
for i in range(0, 0+40000, 200):
photo = []
label = []
for j in range(0, 200):
# print(keys[indices[i + j]])
photo.append(cv2.resize(cv2.imread(keys[indices[i + j]]), (224, 224))/225)
label.append(files[keys[indices[i + j]]])
m = getOneHotLabel(label, depth=10)
a, b = sess.run([optimizer, loss], feed_dict={myinput: photo, mylabel: m})
print("\r%lf"%b, end='')

acc = 0
# 每次取验证集的200张图片进行验证，返回这200张图片的正确率。
for i in range(40000, 40000+10000, 200):
photo = []
label = []
for j in range(i, i + 200):
photo.append(cv2.resize(cv2.imread(keys[indices[j]]), (224, 224))/225)
label.append(files[keys[indices[j]]])
m = getOneHotLabel(label, depth=10)
acc += sess.run(valaccuracy, feed_dict={myinput: photo, mylabel: m})
# 输出，一共有50次验证集数据相加，所以需要除以50。
print("Epoch ", loop, ': validation rate: ', acc/50)
# 保存模型。
saver.save(sess, "model/model.ckpt")

if __name__ == '__main__':
alexnet_main()

以下为结果的部分输出：

50000
1.781297Epoch  0 : validation rate:  0.562699974775
1.775934Epoch  1 : validation rate:  0.547099971175
1.768913Epoch  2 : validation rate:  0.52679997623
1.719084Epoch  3 : validation rate:  0.548099977374
1.721695Epoch  4 : validation rate:  0.562299972177
1.745009Epoch  5 : validation rate:  0.56409997642
1.746290Epoch  6 : validation rate:  0.612299977541
1.726248Epoch  7 : validation rate:  0.574799978137
1.735083Epoch  8 : validation rate:  0.617399973869
1.722523Epoch  9 : validation rate:  0.61839998126
1.712282Epoch  10 : validation rate:  0.643999977112
1.697912Epoch  11 : validation rate:  0.63789998889
1.708088Epoch  12 : validation rate:  0.641699975729
1.716783Epoch  13 : validation rate:  0.64499997735
1.718689Epoch  14 : validation rate:  0.664099971056
1.712452Epoch  15 : validation rate:  0.659299976826
1.699410Epoch  16 : validation rate:  0.666799970865
1.682442Epoch  17 : validation rate:  0.660699977875
1.650028Epoch  18 : validation rate:  0.673199976683
1.662869Epoch  19 : validation rate:  0.692699990273
1.652857Epoch  20 : validation rate:  0.687699975967
1.672175Epoch  21 : validation rate:  0.710799975395
1.662848Epoch  22 : validation rate:  0.707699980736
1.653844Epoch  23 : validation rate:  0.708999979496
1.636483Epoch  24 : validation rate:  0.736199990511
1.658812Epoch  25 : validation rate:  0.688499983549
1.658808Epoch  26 : validation rate:  0.748899987936
1.642705Epoch  27 : validation rate:  0.751199992895
1.609915Epoch  28 : validation rate:  0.742099983692
1.610037Epoch  29 : validation rate:  0.757699984312
1.647516Epoch  30 : validation rate:  0.771899987459
1.615854Epoch  31 : validation rate:  0.762699997425
1.598617Epoch  32 : validation rate:  0.785299996138
1.579349Epoch  33 : validation rate:  0.791699982882
1.615915Epoch  34 : validation rate:  0.780799984932
1.586894Epoch  35 : validation rate:  0.790699990988
1.573043Epoch  36 : validation rate:  0.799299983978
1.580690Epoch  37 : validation rate:  0.812399986982
1.598764Epoch  38 : validation rate:  0.824699985981
1.566866Epoch  39 : validation rate:  0.821999987364

在实际的训练过程中，我进行了多次训练，每次在前一模型的基础上调整学习率继续进行训练。最后的loss值可以下降到1.3~1.4，验证集的正确率可以到0.96~0.97。

四、预测代码

预测代码：

import tensorflow as tf
import numpy as np
import random
import cv2

def getOneHotLabel(label, depth):
m = np.zeros([len(label), depth])
for i in range(len(label)):
m[i][label[i]] = 1
return m

# 建立神经网络
def alexnet(image, keepprob=0.5):

# 定义卷积层1，卷积核大小，偏置量等各项参数参考下面的程序代码，下同
with tf.name_scope("conv1") as scope:
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32, stddev=1e-1, name="weights"))
conv = tf.nn.conv2d(image, kernel, [1, 4, 4, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[64]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope)

pass

# LRN层
lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001/9, beta=0.75, name="lrn1")

# 最大池化层
pool1 = tf.nn.max_pool(lrn1, ksize=[1,3,3,1], strides=[1,2,2,1],padding="VALID", name="pool1")

# 定义卷积层2
with tf.name_scope("conv2") as scope:
kernel = tf.Variable(tf.truncated_normal([5,5,64,192], dtype=tf.float32, stddev=1e-1, name="weights"))
conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[192]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias, name=scope)
pass

# LRN层
lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9, beta=0.75, name="lrn2")

# 最大池化层
pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID", name="pool2")

# 定义卷积层3
with tf.name_scope("conv3") as scope:
kernel = tf.Variable(tf.truncated_normal([3,3,192,384], 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, dtype=tf.float32, shape=[384]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv3 = tf.nn.relu(bias, name=scope)
pass

# 定义卷积层4
with tf.name_scope("conv4") as scope:
kernel = tf.Variable(tf.truncated_normal([3,3,384,256], dtype=tf.float32, stddev=1e-1, name="weights"))
conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv4 = tf.nn.relu(bias, name=scope)
pass

# 定义卷积层5
with tf.name_scope("conv5") as scope:
kernel = tf.Variable(tf.truncated_normal([3,3,256,256], dtype=tf.float32, stddev=1e-1, name="weights"))
conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]), trainable=True, name="biases")
bias = tf.nn.bias_add(conv, biases)
conv5 = tf.nn.relu(bias, name=scope)
pass

# 最大池化层
pool5 = tf.nn.max_pool(conv5, ksize=[1,3,3,1], strides=[1,2,2,1], padding="VALID", name="pool5")

# 全连接层
flatten = tf.reshape(pool5, [-1, 6*6*256])

weight1 = tf.Variable(tf.truncated_normal([6*6*256, 4096], mean=0, stddev=0.01))

fc1 = tf.nn.sigmoid(tf.matmul(flatten, weight1))

dropout1 = tf.nn.dropout(fc1, keepprob)

weight2 = tf.Variable(tf.truncated_normal([4096, 4096], mean=0, stddev=0.01))

fc2 = tf.nn.sigmoid(tf.matmul(dropout1, weight2))

dropout2 = tf.nn.dropout(fc2, keepprob)

weight3 = tf.Variable(tf.truncated_normal([4096, 10], mean=0, stddev=0.01))

fc3 = tf.nn.sigmoid(tf.matmul(dropout2, weight3))

return fc3

def alexnet_main():
# 加载测试集的文件名和标签。
files = np.load("test-label.npy", encoding='bytes')[()]
keys = [i for i in files]
print(len(keys))

myinput = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3], name='input')
mylabel = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='label')
myoutput = alexnet(myinput, 0.6)

prediction = tf.argmax(myoutput, 1)
truth = tf.argmax(mylabel, 1)
valaccuracy = tf.reduce_mean(
tf.cast(
tf.equal(
prediction,
truth),
tf.float32))

saver = tf.train.Saver()

with tf.Session() as sess:
# 加载训练好的模型，路径根据自己的实际情况调整
saver.restore(sess, r"model/model.ckpt")

cnt = 0
for i in range(10000):
photo = []
label = []

photo.append(cv2.resize(cv2.imread(keys[i]), (224, 224))/225)
label.append(files[keys[i]])
m = getOneHotLabel(label, depth=10)
a, b= sess.run([prediction, truth], feed_dict={myinput: photo, mylabel: m})
print(a, ' ', b)
if a[0] == b[0]:
cnt += 1

print("Epoch ", 1, ': prediction rate: ', cnt / 10000)

if __name__ == '__main__':
alexnet_main()

预测结果：（这里只显示部分输出结果）

10000
[3]   [3]
[8]   [8]
[6]   [6]
[4]   [4]
[5]   [9]
[2]   [3]
[9]   [9]
[5]   [5]
[1]   [7]
[3]   [4]
[4]   [4]
[4]   [3]
[9]   [9]
[5]   [5]
[8]   [8]
[3]   [8]
[0]   [0]
[8]   [8]
[7]   [7]
[7]   [4]
[7]   [7]
[5]   [5]
[6]   [5]
...
[7]   [7]
[3]   [3]
[0]   [0]
[7]   [4]
[6]   [2]
[0]   [0]
[7]   [7]
[2]   [5]
[8]   [8]
[5]   [3]
[5]   [5]
[1]   [1]
[7]   [7]
Epoch  1 : prediction rate:  0.7685

五、结果分析

在测试集的表现上，自己训练的AlexNet网络的预测结果达到了0.7685，即76.85%的正确率。相比较LeNet，这个结果好很多，这是因为在网络结构中，使用了更多的卷积操作，可以提取更多的潜在特征。足以证明AlexNet在CIFAR-10数据集上表现比LeNet好。

但是0.7685的正确率还是不是很让人满意，所以后面可以选择继续调整网络的参数，调整网络的结构等手段继续进行网络的训练，或者可以选择使用预训练好的模型进行自己网络的训练，或者可以尝试使用其他更加优秀的网络结构。

接下来的任务是尝试使用GoogleNet模型进行CIFAR-10数据集的求解。