1.编程环境
操作系统：Win10
python版本：3.6
tensorflow版本：1.6
集成开发环境：jupyter notebook
2.配置环境
使用卷积神经网络模型要求有较高的机器配置，如果使用CPU版tensorflow会花费大量时间。
3.下载并解压数据集
MNIST数据集下载链接: https://pan.baidu.com/s/1fPbgMqsEvk2WyM9hy5Em6w 密码: wa9p
下载压缩文件MNIST_data.rar完成后，选择解压到当前文件夹，不要选择解压到MNIST_data。
文件夹结构如下图所示：
image
4.完整代码
import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
batch_size = 100
X_holder = tf.placeholder(tf.float32)
y_holder = tf.placeholder(tf.float32)
X_images = tf.reshape(X_holder, [-1, 28, 28, 1])
#convolutional layer 1
conv1_Weights = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1))
conv1_biases = tf.Variable(tf.constant(0.1, shape=[32]))
conv1_conv2d = tf.nn.conv2d(X_images, conv1_Weights, strides=[1, 1, 1, 1], padding='SAME') + conv1_biases
conv1_activated = tf.nn.relu(conv1_conv2d)
conv1_pooled = tf.nn.max_pool(conv1_activated, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
#convolutional layer 2
conv2_Weights = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1))
conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))
conv2_conv2d = tf.nn.conv2d(conv1_pooled, conv2_Weights, strides=[1, 1, 1, 1], padding='SAME') + conv2_biases
conv2_activated = tf.nn.relu(conv2_conv2d)
conv2_pooled = tf.nn.max_pool(conv2_activated, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
#full connected layer 1
connect1_flat = tf.reshape(conv2_pooled, [-1, 7 * 7 * 64])
connect1_Weights = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1))
connect1_biases = tf.Variable(tf.constant(0.1, shape=[1024]))
connect1_Wx_plus_b = tf.add(tf.matmul(connect1_flat, connect1_Weights), connect1_biases)
connect1_activated = tf.nn.relu(connect1_Wx_plus_b)
#full connected layer 2
connect2_Weights = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1))
connect2_biases = tf.Variable(tf.constant(0.1, shape=[10]))
connect2_Wx_plus_b = tf.add(tf.matmul(connect1_activated, connect2_Weights), connect2_biases)
predict_y = tf.nn.softmax(connect2_Wx_plus_b)
#loss and train
loss = tf.reduce_mean(-tf.reduce_sum(y_holder * tf.log(predict_y), 1))
optimizer = tf.train.AdamOptimizer(0.0001)
train = optimizer.minimize(loss)
init = tf.global_variables_initializer()
session = tf.Session()
session.run(init)
for i in range(1001):
train_images, train_labels = mnist.train.next_batch(200)
session.run(train, feed_dict={X_holder:train_images, y_holder:train_labels})
if i % 100 == 0:
correct_prediction = tf.equal(tf.argmax(predict_y, 1), tf.argmax(y_holder, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
test_images, test_labels = mnist.test.next_batch(2000)
train_accuracy = session.run(accuracy, feed_dict={X_holder:train_images, y_holder:train_labels})
test_accuracy = session.run(accuracy, feed_dict={X_holder:test_images, y_holder:test_labels})
print('step:%d train accuracy:%.4f test accuracy:%.4f' %(i, train_accuracy, test_accuracy))
上面一段代码的运行结果如下图所示：
> Extracting MNIST_data\train-images-idx3-ubyte.gz
> Extracting MNIST_data\train-labels-idx1-ubyte.gz
> Extracting MNIST_data\t10k-images-idx3-ubyte.gz
> Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
> step:0 train accuracy:0.1750 test accuracy:0.1475
> step:100 train accuracy:0.8900 test accuracy:0.9080
> step:200 train accuracy:0.9150 test accuracy:0.9375
> step:300 train accuracy:0.9600 test accuracy:0.9525
> step:400 train accuracy:0.9600 test accuracy:0.9605
> step:500 train accuracy:0.9400 test accuracy:0.9670
> step:600 train accuracy:0.9700 test accuracy:0.9680
> step:700 train accuracy:0.9750 test accuracy:0.9630
> step:800 train accuracy:0.9850 test accuracy:0.9745
> step:900 train accuracy:1.0000 test accuracy:0.9760
> step:1000 train accuracy:0.9750 test accuracy:0.9795
5.数据准备
import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
batch_size = 100
X_holder = tf.placeholder(tf.float32)
y_holder = tf.placeholder(tf.float32)
第1行代码导入warnings库，第2行代码表示不打印警告信息；
第3行代码导入tensorflow库，取别名tf；
第4行代码人从tensorflow.examples.tutorials.mnist库中导入input_data文件；
本文作者使用anaconda集成开发环境，input_data文件所在路径：C:\ProgramData\Anaconda3\Lib\site-packages\tensorflow\examples\tutorials\mnist，如下图所示：
image
第6行代码调用input_data文件的read_data_sets方法，需要2个参数，第1个参数的数据类型是字符串，是读取数据的文件夹名，第2个关键字参数ont_hot数据类型为布尔bool，设置为True，表示预测目标值是否经过One-Hot编码；
第7行代码定义变量batch_size的值为100；
第8、9行代码中placeholder中文叫做占位符，将每次训练的特征矩阵X和预测目标值y赋值给变量X_holder和y_holder。
6.搭建神经网络
X_images = tf.reshape(X_holder, [-1, 28, 28, 1])
#convolutional layer 1
conv1_Weights = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1), name='conv1_Weights')
conv1_biases = tf.Variable(tf.constant(0.1, shape=[32]), name='conv1_biases')
conv1_conv2d = tf.nn.conv2d(X_images, conv1_Weights, strides=[1, 1, 1, 1], padding='SAME') + conv1_biases
conv1_activated = tf.nn.relu(conv1_conv2d)
conv1_pooled = tf.nn.max_pool(conv1_activated, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
#convolutional layer 2
conv2_Weights = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1), name='conv2_Weights')
conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]), name='conv2_biases')
conv2_conv2d = tf.nn.conv2d(conv1_pooled, conv2_Weights, strides=[1, 1, 1, 1], padding='SAME') + conv2_biases
conv2_activated = tf.nn.relu(conv2_conv2d)
conv2_pooled = tf.nn.max_pool(conv2_activated, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
#full connected layer 1
connect1_flat = tf.reshape(conv2_pooled, [-1, 7 * 7 * 64])
connect1_Weights = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1), name='connect1_Weights')
connect1_biases = tf.Variable(tf.constant(0.1, shape=[1024]), name='connect1_biases')
connect1_Wx_plus_b = tf.add(tf.matmul(connect1_flat, connect1_Weights), connect1_biases)
connect1_activated = tf.nn.relu(connect1_Wx_plus_b)
#full connected layer 2
connect2_Weights = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1), name='connect2_Weights')
connect2_biases = tf.Variable(tf.constant(0.1, shape=[10]), name='connect2_biases')
connect2_Wx_plus_b = tf.add(tf.matmul(connect1_activated, connect2_Weights), connect2_biases)
predict_y = tf.nn.softmax(connect2_Wx_plus_b)
#loss and train
loss = tf.reduce_mean(-tf.reduce_sum(y_holder * tf.log(predict_y), 1))
optimizer = tf.train.AdamOptimizer(0.0001)
train = optimizer.minimize(loss)
第1行代码表示将1张图片的784个特征变形为2828的矩阵；
第3-7这5行代码表示第1个卷积层；
第9-13这5行代码表示第2个卷积层；
卷积层的处理有3步：卷积——>激活——>池化；
第15-19这5行代码表示第1个全连接层；
第1个全连接层的处理有3步：展平——>矩阵计算——>激活
第21-24这4行代码表示第2个全连接层*；
第2个全连接层的处理有2步：矩阵计算——>激活
第26-28行代码定义损失函数loss、优化器optimizer、训练过程train。
7.变量初始化
init = tf.global_variables_initializer()
session = tf.Session()
session.run(init)
对于神经网络模型，重要是其中的W、b这两个参数。
开始神经网络模型训练之前，这两个变量需要初始化。
第1行代码调用tf.global_variables_initializer实例化tensorflow中的Operation对象。
image
第2行代码调用tf.Session方法实例化会话对象；
第3行代码调用tf.Session对象的run方法做变量初始化。
8.模型训练
for i in range(1001):
train_images, train_labels = mnist.train.next_batch(200)
session.run(train, feed_dict={X_holder:train_images, y_holder:train_labels})
if i % 100 == 0:
correct_prediction = tf.equal(tf.argmax(predict_y, 1), tf.argmax(y_holder, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
test_images, test_labels = mnist.test.next_batch(2000)
train_accuracy = session.run(accuracy, feed_dict={X_holder:train_images, y_holder:train_labels})
test_accuracy = session.run(accuracy, feed_dict={X_holder:test_images, y_holder:test_labels})
print('step:%d train accuracy:%.4f test accuracy:%.4f' %(i, train_accuracy, test_accuracy))
第1行代码表示模型迭代训练1001次；
第2行代码表示从训练集中随机选出过200个样本；
第3行代码表示模型训练，每运行1次此行代码则模型训练一次；
第4-10行代码表示每隔100次训练，打印模型的预测准确率；
第5-6行代码是计算准确率在tensorflow中的表达；
第7行代码表示从测试集中随机选出2000个样本；
第8行代码表示计算模型在训练集上的预测准确率，赋值给变量tran_accuracy；
第9行代码表示计算模型在测试集上的预测准确率，赋值给变量test_accuracy；
第10行代码打印步数、训练集预测准确率、测试集预测准确率。
为了节省读者的程序运行时间，只设置了1000次迭代。
本文作者迭代训练20000次后，模型准确率在99.2%左右。
上面一段代码的运行结果如下：
> step:0 train accuracy:0.0850 test accuracy:0.1200
> step:100 train accuracy:0.9200 test accuracy:0.8980
> step:200 train accuracy:0.9400 test accuracy:0.9445
> step:300 train accuracy:0.9400 test accuracy:0.9595
> step:400 train accuracy:0.9450 test accuracy:0.9595
> step:500 train accuracy:0.9750 test accuracy:0.9640
> step:600 train accuracy:0.9800 test accuracy:0.9675
> step:700 train accuracy:0.9800 test accuracy:0.9775
> step:800 train accuracy:0.9900 test accuracy:0.9700
> step:900 train accuracy:0.9850 test accuracy:0.9825
> step:1000 train accuracy:0.9750 test accuracy:0.9765
9.保存模型
saver = tf.train.Saver()
save_path = saver.save(session, 'save_model/mnist_cnn.ckpt')
print('Save to path:', save_path)
第1行代码实例化模型保存对象；
第2行代码调用模型保存对象的save方法，第1个参数是tensorflow的会话，第2个参数是表示路径的字符串；
第3行代码打印保存路径。
10.加载模型
模型下载链接: https://pan.baidu.com/s/11_CV9LG5vzLvA3X-3l83bQ 提取码: 8ayr
压缩文件下载后放到代码文件同级路径，选择解压到save_model，如下图所示：
image
save_model文件夹与代码文件在同级目录下，即可成功运行下面的代码。
请读者对照下图，确保自己的代码文件与数据、模型放置在正确的路径下。
image
import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.reset_default_graph()
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
batch_size = 100
X_holder = tf.placeholder(tf.float32)
y_holder = tf.placeholder(tf.float32)
X_images = tf.reshape(X_holder, [-1, 28, 28, 1])
#convolutional layer 1
conv1_Weights = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1), name='conv1_Weights')
conv1_biases = tf.Variable(tf.constant(0.1, shape=[32]), name='conv1_biases')
conv1_conv2d = tf.nn.conv2d(X_images, conv1_Weights, strides=[1, 1, 1, 1], padding='SAME') + conv1_biases
conv1_activated = tf.nn.relu(conv1_conv2d)
conv1_pooled = tf.nn.max_pool(conv1_activated, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
#convolutional layer 2
conv2_Weights = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1), name='conv2_Weights')
conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]), name='conv2_biases')
conv2_conv2d = tf.nn.conv2d(conv1_pooled, conv2_Weights, strides=[1, 1, 1, 1], padding='SAME') + conv2_biases
conv2_activated = tf.nn.relu(conv2_conv2d)
conv2_pooled = tf.nn.max_pool(conv2_activated, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
#full connected layer 1
connect1_flat = tf.reshape(conv2_pooled, [-1, 7 * 7 * 64])
connect1_Weights = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1), name='connect1_Weights')
connect1_biases = tf.Variable(tf.constant(0.1, shape=[1024]), name='connect1_biases')
connect1_Wx_plus_b = tf.add(tf.matmul(connect1_flat, connect1_Weights), connect1_biases)
connect1_activated = tf.nn.relu(connect1_Wx_plus_b)
#full connected layer 2
connect2_Weights = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1), name='connect2_Weights')
connect2_biases = tf.Variable(tf.constant(0.1, shape=[10]), name='connect2_biases')
connect2_Wx_plus_b = tf.add(tf.matmul(connect1_activated, connect2_Weights), connect2_biases)
predict_y = tf.nn.softmax(connect2_Wx_plus_b)
#loss and train
loss = tf.reduce_mean(-tf.reduce_sum(y_holder * tf.log(predict_y), 1))
optimizer = tf.train.AdamOptimizer(0.0001)
train = optimizer.minimize(loss)
session = tf.Session()
saver = tf.train.Saver()
saver.restore(session, 'save_model/mnist_cnn.ckpt')
correct_prediction = tf.equal(tf.argmax(predict_y, 1), tf.argmax(y_holder, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('load model successful')
train_images, train_labels = mnist.train.next_batch(5000)
test_images, test_labels = mnist.test.next_batch(5000)
train_accuracy = session.run(accuracy, feed_dict={X_holder:train_images, y_holder:train_labels})
test_accuracy = session.run(accuracy, feed_dict={X_holder:test_images, y_holder:test_labels})
print('train accuracy:%.4f test accuracy:%.4f' %(train_accuracy, test_accuracy))
上面一段代码的运行结果如下：
> Extracting MNIST_data\train-images-idx3-ubyte.gz
> Extracting MNIST_data\train-labels-idx1-ubyte.gz
> Extracting MNIST_data\t10k-images-idx3-ubyte.gz
> Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
> INFO:tensorflow:Restoring parameters from save_model/mnist_cnn.ckpt
> load model successful
> train accuracy:1.0000 test accuracy:0.9903
11.模型测试
本章代码在jupyter开发环境中才会有显示结果。
import math
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
def drawDigit2(position, image, title, isTrue):
plt.subplot(*position)
plt.imshow(image.reshape(-1, 28), cmap='gray_r')
plt.axis('off')
if not isTrue:
plt.title(title, color='red')
else:
plt.title(title)
def batchDraw2(batch_size):
images,labels = mnist.test.next_batch(batch_size)
predict_labels = session.run(predict_y, feed_dict={X_holder:images, y_holder:labels})
image_number = images.shape[0]
row_number = math.ceil(image_number ** 0.5)
column_number = row_number
plt.figure(figsize=(row_number+8, column_number+8))
for i in range(row_number):
for j in range(column_number):
index = i * column_number + j
if index < image_number:
position = (row_number, column_number, index+1)
image = images[index]
actual = np.argmax(labels[index])
predict = np.argmax(predict_labels[index])
isTrue = actual==predict
title = 'actual:%d\npredict:%d' %(actual,predict)
drawDigit2(position, image, title, isTrue)
batchDraw2(100)
plt.show()
上面一段代码的运行结果如下图所示：
image
从上面的运行结果可以看出，100个数字中只错了1个，符合前1章准确率为99%左右的计算结果。
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