【转载】Java中连结MySQL启用预编译的先决条件是useServerPstmts=true.

本文详细探讨了在Java编程中如何利用PreparedStatement实现MySQL预编译功能。从直接使用JDBC命令到通过设置JDBC连接参数的方式,一步步解析开启MySQL预编译的方法,并强调了即使未启用预编译,使用PreparedStatement对于防止SQL注入的重要性。

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在Java编程中,应用代码绝大多数使用了PreparedStatement,无论你是直接使用JDBC还是使用框架。
在Java编程中,绝大多数使用了使用了PreparedStatement连接MySQL的应用代码没有启用预编译,无论你是直接使用JDBC还是使用框架。

在我所能见到的项目中,几乎没有见过启用MySQL预编译功能的。网上更有文章说MySQL不支持预编译,实在是害人不浅。
要想知道你的应用是否真正的使用了预编译,请执行:show global status like '%prepare%';看看曾经编译过几条,当前Prepared_stmt_count 是多少。大多数是0吧?
这篇文章分以下几个方面:

一.MySQL是支持预编译的

打开MySQL日志功能,启动MySQL,然后 tail -f mysql.log.path(默认:/var/log/mysql/mysql.log).

create table axman_test (ID int(4) auto_increment primary key, name varchar(20),age int(4));
insert into axman_test (name,age) values ('axman',1000);

prepare myPreparedStmt from 'select * from axman_test where name = ?';
set @name='axman';
execute myPreparedStmt using @name;

控制台可以正确地输出:

mysql> execute myPreparedStmt using @name;
+----+-------+------+
| ID | name | age |
+----+-------+------+
| 1 | axman | 1000 |
+----+-------+------+
1 row in set (0.00 sec)

而log文件中也忠实地记录如下:

111028 9:25:06 51 Query prepare myPreparedStmt from 'select * from axman_test where name = ?'
51 Prepare select * from axman_test where name = ?
51 Query set @name='axman'
111028 9:25:08 51 Query execute myPreparedStmt using @name
51 Execute select * from axman_test where name = 'axman'


二.通过JDBC本身是可以预编译的,这个不用多说。相当于我们把控制台输入的命令直接通过JDBC语句来执行:

Class.forName("org.gjt.mm.mysql.Driver");
String url = "jdbc:mysql://localhost:3306/mysql";
Connection conn = null;
try {
conn = DriverManager.getConnection(url, "root", "12345678");
Statement stmt = conn.createStatement();
/*以下忽略返回值处理*/
stmt.executeUpdate("prepare mystmt from 'select * from axman_test where name = ?'");
stmt.execute("set @name='axman'");
stmt.executeQuery("execute mystmt using @name");
stmt.close();
} finally {
if (conn != null) {
conn.close();
}
}

看日志输出:

111028 9:30:19 52 Connect root@localhost on mysql
52 Query /* @MYSQL_CJ_FULL_PROD_NAME@ ( Revision: @MYSQL_CJ_REVISION@ ) */SHOW VARIABLES WHERE Variable_name ='language' OR Variable_name = 'net_write_timeout' OR Variable_name = 'interactive_timeout' OR Variable_name = 'wait_timeout' OR Variable_name = 'character_set_client' OR Variable_name = 'character_set_connection' OR Variable_name = 'character_set' OR Variable_name = 'character_set_server' OR Variable_name = 'tx_isolation' OR Variable_name = 'transaction_isolation' OR Variable_name = 'character_set_results' OR Variable_name = 'timezone' OR Variable_name = 'time_zone' OR Variable_name = 'system_time_zone' OR Variable_name = 'lower_case_table_names' OR Variable_name = 'max_allowed_packet' OR Variable_name = 'net_buffer_length' OR Variable_name = 'sql_mode' OR Variable_name = 'query_cache_type' OR Variable_name = 'query_cache_size' OR Variable_name = 'init_connect'
52 Query /* @MYSQL_CJ_FULL_PROD_NAME@ ( Revision: @MYSQL_CJ_REVISION@ ) */SELECT @@session.auto_increment_increment
52 Query SHOW COLLATION
52 Query SET NAMES latin1
52 Query SET character_set_results = NULL
52 Query SET autocommit=1
52 Query SET sql_mode='STRICT_TRANS_TABLES'
52 Query prepare mystmt from 'select * from axman_test where name = ?'
52 Prepare select * from axman_test where name = ?
52 Query set @name='axman'
52 Query execute mystmt using @name
52 Execute select * from axman_test where name = 'axman'
52 Quit


三.默认的PrearedStatement不能开启MySQL预编译功能:

虽然第二节中我们通过JDBC手工指定MySQL进行预编译,但是PrearedStatement却并不自动帮我们做这件事。
Class.forName("org.gjt.mm.mysql.Driver");
String url = "jdbc:mysql://localhost:3306/mysql";
Connection conn = null;
try {
conn = DriverManager.getConnection(url, "root", "12345678");
PreparedStatement ps = conn.prepareStatement("select * from axman_test where name = ?");
ps.setString(1, "axman' or 1==1");
ResultSet rs = ps.executeQuery();
if (rs.next()) {
System.out.println(rs.getString(1));
}
Thread.sleep(1000);
rs.close();
ps.clearParameters();
ps.setString(1, "axman");
rs = ps.executeQuery();
if (rs.next()) {
System.out.println(rs.getString(1));
}
rs.close();
ps.close();
} finally {
if (conn != null) {
conn.close();
}
}

废话少说,直接看日志:
111028 9:54:03 53 Connect root@localhost on mysql
53 Query /* @MYSQL_CJ_FULL_PROD_NAME@ ( Revision: @MYSQL_CJ_REVISION@ ) */SHOW VARIABLES WHERE Variable_name ='language' OR Variable_name = 'net_write_timeout' OR Variable_name = 'interactive_timeout' OR Variable_name = 'wait_timeout' OR Variable_name = 'character_set_client' OR Variable_name = 'character_set_connection' OR Variable_name = 'character_set' OR Variable_name = 'character_set_server' OR Variable_name = 'tx_isolation' OR Variable_name = 'transaction_isolation' OR Variable_name = 'character_set_results' OR Variable_name = 'timezone' OR Variable_name = 'time_zone' OR Variable_name = 'system_time_zone' OR Variable_name = 'lower_case_table_names' OR Variable_name = 'max_allowed_packet' OR Variable_name = 'net_buffer_length' OR Variable_name = 'sql_mode' OR Variable_name = 'query_cache_type' OR Variable_name = 'query_cache_size' OR Variable_name = 'init_connect'
53 Query /* @MYSQL_CJ_FULL_PROD_NAME@ ( Revision: @MYSQL_CJ_REVISION@ ) */SELECT @@session.auto_increment_increment
53 Query SHOW COLLATION
53 Query SET NAMES latin1
53 Query SET character_set_results = NULL
53 Query SET autocommit=1
53 Query SET sql_mode='STRICT_TRANS_TABLES'
53 Query select * from axman_test where name = 'axman\' or 1==1'
111028 9:54:04 53 Query select * from axman_test where name = 'axman'
53 Quit

两条语句都是直接执行,而没有预编译。注意我的第一条语句select * from axman_test where name = 'axman\' or 1==1',下面还会说到它。
接着我们改变一下jdbc.url的选项:
String url = "jdbc:mysql://localhost:3306/mysql?cachePrepStmts=true&prepStmtCacheSize=25&prepStmtCacheSqlLimit=256";
执行上面的代码还是没有开启Mysql的预编译。


四.只有使用了useServerPrepStmts=true才能开启Mysql的预编译。

上面的代码其它不变,只修改String url = "jdbc:mysql://localhost:3306/mysql?useServerPrepStmts=true";
查看日志:

111028 10:04:52 54 Connect root@localhost on mysql
54 Query /* @MYSQL_CJ_FULL_PROD_NAME@ ( Revision: @MYSQL_CJ_REVISION@ ) */SHOW VARIABLES WHERE Variable_name ='language' OR Variable_name = 'net_write_timeout' OR Variable_name = 'interactive_timeout' OR Variable_name = 'wait_timeout' OR Variable_name = 'character_set_client' OR Variable_name = 'character_set_connection' OR Variable_name = 'character_set' OR Variable_name = 'character_set_server' OR Variable_name = 'tx_isolation' OR Variable_name = 'transaction_isolation' OR Variable_name = 'character_set_results' OR Variable_name = 'timezone' OR Variable_name = 'time_zone' OR Variable_name = 'system_time_zone' OR Variable_name = 'lower_case_table_names' OR Variable_name = 'max_allowed_packet' OR Variable_name = 'net_buffer_length' OR Variable_name = 'sql_mode' OR Variable_name = 'query_cache_type' OR Variable_name = 'query_cache_size' OR Variable_name = 'init_connect'
54 Query /* @MYSQL_CJ_FULL_PROD_NAME@ ( Revision: @MYSQL_CJ_REVISION@ ) */SELECT @@session.auto_increment_increment
54 Query SHOW COLLATION
54 Query SET NAMES latin1
54 Query SET character_set_results = NULL
54 Query SET autocommit=1
54 Query SET sql_mode='STRICT_TRANS_TABLES'
54 Prepare select * from axman_test where name = ?
54 Execute select * from axman_test where name = 'axman\' or 1==1'
111028 10:04:53 54 Execute select * from axman_test where name = 'axman'
54 Close stmt
54 Quit

如果useServerPrepStmts=true,ConneciontImpl在prepareStatement时会产生一个ServerPreparedStatement.在这个ServerPreparedStatement对象构造时首先会把当前SQL语句发送给MySQL进行预编译,然后将返回的结果缓存起来,其中包含预编译的名称(我们可以看成是当前SQL语句编译后的函数名),签名(参数列表),然后执行的时候就会直接把参数传给这个函数请求MySQL执行这个函数。否则返回的是客户端预编译语句,它仅做参数化工作,见第五节。
ServerPreparedStatement在请求预编译和执行预编译后的SQL 函数时,虽然和我们上面手工预编译工作相同,但它与MySQL交互使用的是压缩格式,如prepare指令码是22,这样可以减少交互时传输的数据量。

注意上面的代码中,两次执行使用的是同一个PreparedStatement句柄.如果使用个不同的PreparedStatement句柄,把代码改成:
Class.forName("org.gjt.mm.mysql.Driver");
String url = "jdbc:mysql://localhost:3306/mysql?useServerPrepStmts=true";
Connection conn = null;
try {
conn = DriverManager.getConnection(url, "root", "12345678");
PreparedStatement ps = conn.prepareStatement("select * from axman_test where name = ?");
ps.setString(1, "axman' or 1==1");
ResultSet rs = ps.executeQuery();
if (rs.next()) {
System.out.println(rs.getString(1));
}
Thread.sleep(1000);
rs.close();
ps.close();
ps = conn.prepareStatement("select * from axman_test where name = ?");
ps.setString(1, "axman");
rs = ps.executeQuery();
if (rs.next()) {
System.out.println(rs.getString(1));
}
rs.close();
ps.close();
} finally {
if (conn != null) {
conn.close();
}
}

再看日志输出:
Connect root@localhost on mysql
55 Query /* @MYSQL_CJ_FULL_PROD_NAME@ ( Revision: @MYSQL_CJ_REVISION@ ) */SHOW VARIABLES WHERE Variable_name ='language' OR Variable_name = 'net_write_timeout' OR Variable_name = 'interactive_timeout' OR Variable_name = 'wait_timeout' OR Variable_name = 'character_set_client' OR Variable_name = 'character_set_connection' OR Variable_name = 'character_set' OR Variable_name = 'character_set_server' OR Variable_name = 'tx_isolation' OR Variable_name = 'transaction_isolation' OR Variable_name = 'character_set_results' OR Variable_name = 'timezone' OR Variable_name = 'time_zone' OR Variable_name = 'system_time_zone' OR Variable_name = 'lower_case_table_names' OR Variable_name = 'max_allowed_packet' OR Variable_name = 'net_buffer_length' OR Variable_name = 'sql_mode' OR Variable_name = 'query_cache_type' OR Variable_name = 'query_cache_size' OR Variable_name = 'init_connect'
55 Query /* @MYSQL_CJ_FULL_PROD_NAME@ ( Revision: @MYSQL_CJ_REVISION@ ) */SELECT @@session.auto_increment_increment
55 Query SHOW COLLATION
55 Query SET NAMES latin1
55 Query SET character_set_results = NULL
55 Query SET autocommit=1
55 Query SET sql_mode='STRICT_TRANS_TABLES'
55 Prepare select * from axman_test where name = ?
55 Execute select * from axman_test where name = 'axman\' or 1==1'
111028 10:10:24 55 Close stmt
55 Prepare select * from axman_test where name = ?
55 Execute select * from axman_test where name = 'axman'
55 Close stmt
55 Quit
55 Quit
同一个SQL语句发生了两次预编译。这不是我们想要的效果,要想对同一SQL语句多次执行不是每次都预编译,就要使用cachePrepStmts=true,这个选项可以让JVM端缓存每个SQL语句的预编译结果,说白了就是以SQL语句为key, 将预编译结果缓存起来,下次遇到相同的SQL语句时作为key去get一下看看有没有这个SQL语句的预编译结果,有就直接合出来用。我们还是以事实来说明:
上面的代码只修改String url = "jdbc:mysql://localhost:3306/mysql?useServerPrepStmts=true&cachePrepStmts=true&prepStmtCacheSize=25&prepStmtCacheSqlLimit=256";
这行代码中有其它参数自己去读文档,我不多啰嗦,执行的结果:
111028 10:27:23 58 Connect root@localhost on mysql
58 Query /* mysql-connector-java-5.1.18 ( Revision: tonci.grgin@oracle.com-20110930151701-jfj14ddfq48ifkfq ) */SHOW VARIABLES WHERE Variable_name ='language' OR Variable_name = 'net_write_timeout' OR Variable_name = 'interactive_timeout' OR Variable_name = 'wait_timeout' OR Variable_name = 'character_set_client' OR Variable_name = 'character_set_connection' OR Variable_name = 'character_set' OR Variable_name = 'character_set_server' OR Variable_name = 'tx_isolation' OR Variable_name = 'transaction_isolation' OR Variable_name = 'character_set_results' OR Variable_name = 'timezone' OR Variable_name = 'time_zone' OR Variable_name = 'system_time_zone' OR Variable_name = 'lower_case_table_names' OR Variable_name = 'max_allowed_packet' OR Variable_name = 'net_buffer_length' OR Variable_name = 'sql_mode' OR Variable_name = 'query_cache_type' OR Variable_name = 'query_cache_size' OR Variable_name = 'init_connect'
58 Query /* mysql-connector-java-5.1.18 ( Revision: tonci.grgin@oracle.com-20110930151701-jfj14ddfq48ifkfq ) */SELECT @@session.auto_increment_increment
58 Query SHOW COLLATION
58 Query SET NAMES latin1
58 Query SET character_set_results = NULL
58 Query SET autocommit=1
58 Query SET sql_mode='STRICT_TRANS_TABLES'
58 Prepare select * from axman_test where name = ?
58 Execute select * from axman_test where name = 'axman\' or 1==1'
111028 10:27:24 58 Execute select * from axman_test where name = 'axman'
58 Quit

注意仅发生一次预编译,尽管代码本身在第一次执行后关闭了ps.close();但因为使用了cachePrepStmts=true,底层并没有真实关闭。
千万注意,同一条SQL语句尽量在一个全局的地方定义,然后在不同地方引用,这样做一是为了DBA方便地对SQL做统一检查和优化,就象IBatis把SQL语句定义在XML文件中一样。二是同一语句不同写法,即使空格不同,大小写不同也会重新预编译,因为JVM端缓存是直接以SQL本身为key而不会对SQL格式化以后再做为key。
我们来看下面的输出:
35 Prepare select * from axman_test where name = ?
35 Execute select * from axman_test where name = 'axman\' or 1==1'
111029 9:54:31 35 Prepare select * FROM axman_test where name = ?
35 Execute select * FROM axman_test where name = 'axman'
第一条语句和第二条语句的差别是FROM在第二条语句中被大写了,这样还是发生了两次预编译。
37 Prepare select * from axman_test where name = ?
37 Execute select * from axman_test where name = 'axman\' or 1==1'
111029 9:59:00 37 Prepare select * from axman_test where name = ?
37 Execute select * from axman_test where name = 'axman'
这里两条语句只是第二条的from后面多了个空格,因为你现在看到是HTML格式,如果不加转义符,两个空格也显示一个空格,所以你能可看不到区别,但你可以在自己的机器上试一下。

五.即使没有开启MySQL的预编译,坚持使用PreparedStatement仍然非常必要。
在第三节的最后我说到"注意我的第一条语句select * from axman_test where name = 'axman\' or 1==1',下面还会说到它。",现在我们回过头来看,即使没有开启MySQL端的预编译,我们仍然要坚持使用PreparedStatement,因为JVM端对PreparedStatement的SQL语句进行了参数化,即用占位符替换参数,以后任何内容输入都是字符串或其它类型的值,而不会和原始的SQL语句拚接产生SQL注入,对字符串中的任何字符都会做检查,如果可能是SQL语句使用的标识符,会进行转义。然后发送一个合法的安全的SQL语句给数据库执行。
# -*- coding: utf-8 -*- """ Created on Thu Apr 25 16:05:29 2024 @author: lich5 """ import numpy as np # linear algebra import tensorflow as tf # from tensorflow import keras import matplotlib.pyplot as plt from tensorflow.keras import layers, models, Model, Sequential, datasets from tensorflow.keras.layers import MaxPool2D # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory # import os # for dirname, _, filenames in os.walk('/kaggle/input'): # for filename in filenames: # print(os.path.join(dirname, filename)) class Inception(tf.keras.Model): # c1--c4是每条路径的输出通道数 def __init__(self, ch1x1, ch3x3, ch5x5, pool_proj): super().__init__() # 线路1,单1x1卷积层 self.p1_1 = layers.Conv2D(ch1x1, 1, activation='relu') # 线路2,1x1卷积层后接3x3卷积层 self.p2_1 = layers.Conv2D(ch3x3[0], 1, activation='relu') self.p2_2 = layers.Conv2D(ch3x3[1], 3, padding='same', activation='relu') # 线路3,1x1卷积层后接5x5卷积层 self.p3_1 = layers.Conv2D(ch5x5[0], 1, activation='relu') self.p3_2 = layers.Conv2D(ch5x5[1], 5, padding='same', activation='relu') # 线路4,3x3最大汇聚层后接1x1卷积层 self.p4_1 = layers.MaxPool2D(3, 1, padding='same') self.p4_2 = layers.Conv2D(pool_proj, 1, activation='relu') def call(self, x): p1 = self.p1_1(x) p2 = self.p2_2(self.p2_1(x)) p3 = self.p3_2(self.p3_1(x)) p4 = self.p4_2(self.p4_1(x)) # 在通道维度上连结输出 return layers.Concatenate()([p1, p2, p3, p4]) class InceptionAux(tf.keras.Model): def __init__(self, num_classes): super().__init__() self.averagePool = layers.AvgPool2D(pool_size=5, strides=3) self.conv = layers.Conv2D(128, kernel_size=1, activation="relu") self.fc1 = layers.Dense(1024, activation="relu") self.fc2 = layers.Dense(num_classes) self.softmax = layers.Softmax() def call(self, x): # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14 x = self.averagePool(x) # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4 x = self.conv(x) # N x 128 x 4 x 4 x = layers.Flatten()(x) x = layers.Dropout(rate=0.5)(x) # N x 2048 x = self.fc1(x) x = layers.Dropout(rate=0.5)(x) # N x 1024 x = self.fc2(x) # N x num_classes x = self.softmax(x) return x # class GoogLeNet(im_height=224, im_width=224, class_num=1000, aux_logits=False): # # tensorflow中的tensor通道排序是NHWC # input_image = layers.Input(shape=(im_height, im_width, 3), dtype="float32") # # def b1: # # (None, 224, 224, 3) # x = layers.Conv2D(64, kernel_size=7, strides=2, padding="SAME", activation="relu")(input_image) # # (None, 112, 112, 64) # x = layers.MaxPool2D(pool_size=3, strides=2, padding="SAME")(x) # # def b2: # # (None, 56, 56, 64) # x = layers.Conv2D(64, kernel_size=1, activation="relu")(x) # # (None, 56, 56, 64) # x = layers.Conv2D(192, kernel_size=3, padding="SAME", activation="relu")(x) # # (None, 56, 56, 192) # x = layers.MaxPool2D(pool_size=3, strides=2, padding="SAME")(x) # # def b3: # # (None, 28, 28, 192) # x = Inception(64, (96, 128), (16, 32), 32)(x) # # (None, 28, 28, 256) # x = Inception(128, (128, 192), (32, 96), 64)(x) # # (None, 28, 28, 480) # x = layers.MaxPool2D(pool_size=3, strides=2, padding="SAME")(x) # # (None, 14, 14, 480) # # def b4: # x = Inception(192, (96, 208), (16, 48), 64)(x) # if aux_logits: # aux1 = InceptionAux(class_num)(x) # # (None, 14, 14, 512) # x = Inception(160, (112, 224), (24, 64), 64)(x) # # (None, 14, 14, 512) # x = Inception(128, (128, 256), (24, 64), 64)(x) # # (None, 14, 14, 512) # x = Inception(112, (144, 288), (32, 64), 64)(x) # if aux_logits: # aux2 = InceptionAux(class_num)(x) # # # def b5: # # (None, 14, 14, 528) # x = Inception(256, (160, 320), (32, 128), 128)(x) # # (None, 14, 14, 532) # x = Inception(384, (192, 384), (48, 128), 128)(x) # # (None, 7, 7, 1024) # x = layers.GlobalAvgPool2D()(x) # # (None, 1, 1, 1024) # x = layers.Flatten()(x) # x = layers.Dense(class_num)(x) # # (None, class_num) # aux3 = layers.Softmax(x) # if aux_logits: # model = models.Model(inputs=input_image, outputs=[aux1, aux2, aux3]) # else: # model = models.Model(inputs=input_image, outputs=aux3) # return model if __name__ == '__main__': #%% load and preprocess data (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() train_ds=tf.data.Dataset.from_tensor_slices((train_images,train_labels)) test_ds=tf.data.Dataset.from_tensor_slices((test_images,test_labels)) CLASS_NAMES= ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] # plt.figure(figsize=(30,30)) # for i,(image,label) in enumerate(train_ds.shuffle(100000).take(20)): # #print(label) # ax=plt.subplot(5,5,i+1) # plt.imshow(image) # plt.title(CLASS_NAMES[label.numpy()[0]]) # plt.axis('off') def process_image(image,label): if len(image.shape) == 2: # 检查是否为二维图像 image = tf.expand_dims(image, axis=-1) # 添加通道维度 image=tf.image.per_image_standardization(image) image=tf.image.resize(image, (32,32), method=tf.image.ResizeMethod.BILINEAR) return image,label train_ds_size=tf.data.experimental.cardinality(train_ds).numpy() test_ds_size=tf.data.experimental.cardinality(test_ds).numpy() train_ds=(train_ds .map(process_image) .shuffle(buffer_size=train_ds_size) .batch(batch_size=128,drop_remainder=True) ) test_ds=(test_ds .map(process_image) .shuffle(buffer_size=test_ds_size) .batch(batch_size=128,drop_remainder=True) ) #%% define the model im_height = 96 im_width = 96 batch_size = 128 epochs = 15 # model = GoogLeNet(im_height=im_height, im_width=im_width, class_num=10, aux_logits=True) model = tf.keras.Sequential() # def b1: model.add(layers.Conv2D(64, 7, strides=2, padding='same', activation='relu')) model.add(layers.MaxPool2D(pool_size=3, strides=2, padding='same')) # def b2: model.add(layers.Conv2D(64, 1, activation='relu')) model.add(layers.Conv2D(192, 3, padding='same', activation='relu')) model.add(layers.MaxPool2D(pool_size=3, strides=2, padding='same')) # def b3: model.add(Inception(64, (96, 128), (16, 32), 32)) model.add(Inception(128, (128, 192), (32, 96), 64)) model.add(layers.MaxPool2D(pool_size=3, strides=2, padding='same')) # def b4: model.add(Inception(192, (96, 208), (16, 48), 64)) model.add(Inception(160, (112, 224), (24, 64), 64)) model.add(Inception(128, (128, 256), (24, 64), 64)) model.add(Inception(112, (144, 288), (32, 64), 64)) model.add(Inception(256, (160, 320), (32, 128), 128)) model.add(layers.MaxPool2D(pool_size=3, strides=2, padding='same')) # def b5: model.add(Inception(256, (160, 320), (32, 128), 128)) model.add(Inception(384, (192, 384), (48, 128), 128)) model.add(layers.GlobalAvgPool2D()) model.add(layers.Flatten()) # def FC model.add(layers.Dense(10)) model.compile( loss='sparse_categorical_crossentropy', optimizer=tf.optimizers.Adam(learning_rate=0.0005), metrics=['accuracy'] ) # model.build((batch_size, 224, 224, 3)) # when using subclass model # model.summary() history=model.fit( train_ds, epochs=epochs, #50 validation_data=test_ds ) # # 保存模型 # model.save('cnn_model.h5') # # 加载模型 # model = tf.keras.models.load_model('cnn_model.h5') model.evaluate(test_ds, verbose=2) idx = np.random.randint(1e4,size=9) images = test_images[idx,:] y_ = test_labels[idx] # 测试模型 def plot_cifar10_3_3(images, y_, y=None): assert images.shape[0] == len(y_) fig, axes = plt.subplots(3, 3) for i, ax in enumerate(axes.flat): ax.imshow(images[i], cmap='binary') if y is None: xlabel = 'True: {}'.format(CLASS_NAMES[y_[i][0]]) else: xlabel = 'True: {0}, Pred: {1}'.format(CLASS_NAMES[y_[i][0]], CLASS_NAMES[y[i]]) ax.set_xlabel(xlabel) ax.set_xticks([]) ax.set_yticks([]) plt.show() '''利用predict命令,输入x_test生成测试样本的测试值''' predictions = model.predict(images) y_pred = np.argmax(predictions, axis = 1) plot_cifar10_3_3(images, y_, y_pred) f,ax=plt.subplots(2,1,figsize=(10,10)) #Assigning the first subplot to graph training loss and validation loss ax[0].plot(history.history['loss'],color='b',label='Training Loss') ax[0].plot(history.history['val_loss'],color='r',label='Validation Loss') #Plotting the training accuracy and validation accuracy ax[1].plot(history.history['accuracy'],color='b',label='Training Accuracy') ax[1].plot(history.history['val_accuracy'],color='r',label='Validation Accuracy') plt.legend() # [EOF]修改一下
最新发布
06-17
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