forked from mindspore-Ecosystem/mindspore
add train lenet java demo
This commit is contained in:
xutianchun
parent
197db11fe4
commit
a967aab85e
50
build.sh
50
build.sh
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@ -671,6 +671,12 @@ build_lite_java_arm64() {
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if [[ "X$SUPPORT_TRAIN" = "Xon" ]]; then
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/lib/libmindspore-lite.so ${JAVA_PATH}/java/app/libs/arm64-v8a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/lib/libmindspore-lite.so ${JAVA_PATH}/native/libs/arm64-v8a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/lib/libminddata-lite.so ${JAVA_PATH}/java/app/libs/arm64-v8a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/lib/libminddata-lite.so ${JAVA_PATH}/native/libs/arm64-v8a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/third_party/libjpeg-turbo/lib/*.so ${JAVA_PATH}/java/app/libs/arm64-v8a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/third_party/libjpeg-turbo/lib/*.so ${JAVA_PATH}/native/libs/arm64-v8a/
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else
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/inference/lib/libmindspore-lite.so ${JAVA_PATH}/java/app/libs/arm64-v8a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/inference/lib/libmindspore-lite.so ${JAVA_PATH}/native/libs/arm64-v8a/
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@ -697,6 +703,12 @@ build_lite_java_arm32() {
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if [[ "X$SUPPORT_TRAIN" = "Xon" ]]; then
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/lib/libmindspore-lite.so ${JAVA_PATH}/java/app/libs/armeabi-v7a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/lib/libmindspore-lite.so ${JAVA_PATH}/native/libs/armeabi-v7a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/lib/libminddata-lite.so ${JAVA_PATH}/java/app/libs/armeabi-v7a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/lib/libminddata-lite.so ${JAVA_PATH}/native/libs/armeabi-v7a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/third_party/libjpeg-turbo/lib/*.so ${JAVA_PATH}/java/app/libs/armeabi-v7a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/third_party/libjpeg-turbo/lib/*.so ${JAVA_PATH}/native/libs/armeabi-v7a/
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else
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/inference/lib/libmindspore-lite.so ${JAVA_PATH}/java/app/libs/armeabi-v7a/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/inference/lib/libmindspore-lite.so ${JAVA_PATH}/native/libs/armeabi-v7a/
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@ -706,10 +718,15 @@ build_lite_java_arm32() {
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build_lite_java_x86() {
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# build mindspore-lite x86
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local inference_or_train=inference
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if [[ "X$SUPPORT_TRAIN" = "Xon" ]]; then
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inference_or_train=train
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fi
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if [[ "$X86_64_SIMD" == "sse" || "$X86_64_SIMD" == "avx" ]]; then
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local JTARBALL=mindspore-lite-${VERSION_STR}-inference-linux-x64-${X86_64_SIMD}
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local JTARBALL=mindspore-lite-${VERSION_STR}-${inference_or_train}-linux-x64-${X86_64_SIMD}
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else
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local JTARBALL=mindspore-lite-${VERSION_STR}-inference-linux-x64
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local JTARBALL=mindspore-lite-${VERSION_STR}-${inference_or_train}-linux-x64
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fi
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if [[ "X$INC_BUILD" == "Xoff" ]] || [[ ! -f "${BASEPATH}/mindspore/lite/build/java/${JTARBALL}.tar.gz" ]]; then
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build_lite "x86_64" "off" ""
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@ -721,8 +738,20 @@ build_lite_java_x86() {
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[ -n "${JAVA_PATH}" ] && rm -rf ${JAVA_PATH}/java/linux_x86/libs/
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mkdir -p ${JAVA_PATH}/java/linux_x86/libs/
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mkdir -p ${JAVA_PATH}/native/libs/linux_x86/
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if [[ "X$SUPPORT_TRAIN" = "Xon" ]]; then
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/lib/libmindspore-lite.so ${JAVA_PATH}/java/linux_x86/libs/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/lib/libmindspore-lite.so ${JAVA_PATH}/native/libs/linux_x86/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/lib/libminddata-lite.so ${JAVA_PATH}/java/linux_x86/libs/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/lib/libminddata-lite.so ${JAVA_PATH}/native/libs/linux_x86/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/third_party/libjpeg-turbo/lib/*.so* ${JAVA_PATH}/java/linux_x86/libs/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/train/minddata/third_party/libjpeg-turbo/lib/*.so* ${JAVA_PATH}/native/libs/linux_x86/
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else
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/inference/lib/libmindspore-lite.so ${JAVA_PATH}/java/linux_x86/libs/
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cp ${BASEPATH}/mindspore/lite/build/java/${JTARBALL}/inference/lib/libmindspore-lite.so ${JAVA_PATH}/native/libs/linux_x86/
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fi
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[ -n "${VERSION_STR}" ] && rm -rf ${JTARBALL}
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}
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build_jni_arm64() {
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@ -776,7 +805,7 @@ build_jni_x86_64() {
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mkdir -pv java/jni
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cd java/jni
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cmake -DMS_VERSION_MAJOR=${VERSION_MAJOR} -DMS_VERSION_MINOR=${VERSION_MINOR} -DMS_VERSION_REVISION=${VERSION_REVISION} \
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-DENABLE_VERBOSE=${ENABLE_VERBOSE} "${JAVA_PATH}/native/"
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-DENABLE_VERBOSE=${ENABLE_VERBOSE} -DSUPPORT_TRAIN=${SUPPORT_TRAIN} "${JAVA_PATH}/native/"
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make -j$THREAD_NUM
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if [[ $? -ne 0 ]]; then
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echo "---------------- mindspore lite: build jni x86_64 failed----------------"
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@ -825,11 +854,16 @@ build_java() {
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cd ${JAVA_PATH}/java/app/build
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zip -r mindspore-lite-maven-${VERSION_STR}.zip mindspore
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local inference_or_train=inference
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if [[ "X$SUPPORT_TRAIN" = "Xon" ]]; then
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inference_or_train=train
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fi
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# build linux x86 jar
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if [[ "$X86_64_SIMD" == "sse" || "$X86_64_SIMD" == "avx" ]]; then
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local LINUX_X86_PACKAGE_NAME=mindspore-lite-${VERSION_STR}-inference-linux-x64-${X86_64_SIMD}-jar
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local LINUX_X86_PACKAGE_NAME=mindspore-lite-${VERSION_STR}-${inference_or_train}-linux-x64-${X86_64_SIMD}-jar
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else
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local LINUX_X86_PACKAGE_NAME=mindspore-lite-${VERSION_STR}-inference-linux-x64-jar
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local LINUX_X86_PACKAGE_NAME=mindspore-lite-${VERSION_STR}-${inference_or_train}-linux-x64-jar
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fi
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check_java_home
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build_lite_java_x86
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@ -843,15 +877,17 @@ build_java() {
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gradle releaseJar
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# install and package
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mkdir -p ${JAVA_PATH}/java/linux_x86/build/lib
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cp ${JAVA_PATH}/java/linux_x86/libs/*.so ${JAVA_PATH}/java/linux_x86/build/lib/jar
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cp ${JAVA_PATH}/java/linux_x86/libs/*.so* ${JAVA_PATH}/java/linux_x86/build/lib/jar
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cd ${JAVA_PATH}/java/linux_x86/build/
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cp -r ${JAVA_PATH}/java/linux_x86/build/lib ${JAVA_PATH}/java/linux_x86/build/${LINUX_X86_PACKAGE_NAME}
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tar czvf ${LINUX_X86_PACKAGE_NAME}.tar.gz ${LINUX_X86_PACKAGE_NAME}
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# copy output
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cp ${JAVA_PATH}/java/app/build/mindspore-lite-maven-${VERSION_STR}.zip ${BASEPATH}/output
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cp ${LINUX_X86_PACKAGE_NAME}.tar.gz ${BASEPATH}/output
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cd ${BASEPATH}/output
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[ -n "${VERSION_STR}" ] && rm -rf ${BASEPATH}/mindspore/lite/build/java/mindspore-lite-${VERSION_STR}-inference-linux-x64
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[ -n "${VERSION_STR}" ] && rm -rf ${BASEPATH}/mindspore/lite/build/java/mindspore-lite-${VERSION_STR}-${inference_or_train}-linux-x64
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exit 0
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}
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@ -0,0 +1,55 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>com.mindspore.lite.demo</groupId>
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<artifactId>train_lenet_java</artifactId>
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<version>1.0</version>
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<properties>
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<maven.compiler.source>8</maven.compiler.source>
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<maven.compiler.target>8</maven.compiler.target>
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</properties>
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<dependencies>
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<dependency>
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<groupId>com.mindspore.lite</groupId>
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<artifactId>mindspore-lite-java</artifactId>
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<version>1.0</version>
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<scope>system</scope>
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<systemPath>${project.basedir}/lib/mindspore-lite-java.jar</systemPath>
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</dependency>
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</dependencies>
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<build>
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<finalName>${project.name}</finalName>
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<plugins>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-assembly-plugin</artifactId>
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<configuration>
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<archive>
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<manifest>
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<mainClass>com.mindspore.lite.train_lenet.Main</mainClass>
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</manifest>
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</archive>
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<descriptorRefs>
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<descriptorRef>jar-with-dependencies</descriptorRef>
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</descriptorRefs>
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</configuration>
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<executions>
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<execution>
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<id>make-assemble</id>
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<phase>package</phase>
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<goals>
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<goal>single</goal>
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</goals>
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</execution>
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</executions>
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</plugin>
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</plugins>
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</build>
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</project>
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Binary file not shown.
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@ -0,0 +1,131 @@
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package com.mindspore.lite.train_lenet;
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import java.io.BufferedInputStream;
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import java.io.FileInputStream;
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import java.io.IOException;
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import java.util.Vector;
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public class DataSet {
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private long numOfClasses = 0;
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private long expectedDataSize = 0;
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public class DataLabelTuple {
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public float[] data;
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public int label;
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}
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Vector<DataLabelTuple> trainData;
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Vector<DataLabelTuple> testData;
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public void initializeMNISTDatabase(String dpath) {
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numOfClasses = 10;
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trainData = new Vector<DataLabelTuple>();
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testData = new Vector<DataLabelTuple>();
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readMNISTFile(dpath + "/train/train-images-idx3-ubyte", dpath+"/train/train-labels-idx1-ubyte", trainData);
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readMNISTFile(dpath + "/test/t10k-images-idx3-ubyte", dpath+"/test/t10k-labels-idx1-ubyte", testData);
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System.out.println("train data cnt: " + trainData.size());
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System.out.println("test data cnt: " + testData.size());
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}
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private String bytesToHex(byte[] bytes) {
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StringBuffer sb = new StringBuffer();
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for (int i = 0; i < bytes.length; i++) {
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String hex = Integer.toHexString(bytes[i] & 0xFF);
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if (hex.length() < 2) {
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sb.append(0);
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}
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sb.append(hex);
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}
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return sb.toString();
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}
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private void readFile(BufferedInputStream inputStream, byte[] bytes, int len) throws IOException {
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int result = inputStream.read(bytes, 0, len);
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if (result != len) {
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System.err.println("expected read " + len + " bytes, but " + result + " read");
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System.exit(1);
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}
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}
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public void readMNISTFile(String inputFileName, String labelFileName, Vector<DataLabelTuple> dataset) {
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try {
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BufferedInputStream ibin = new BufferedInputStream(new FileInputStream(inputFileName));
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BufferedInputStream lbin = new BufferedInputStream(new FileInputStream(labelFileName));
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byte[] bytes = new byte[4];
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readFile(ibin, bytes, 4);
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if (!"00000803".equals(bytesToHex(bytes))) { // 2051
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System.err.println("The dataset is not valid: " + bytesToHex(bytes));
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return;
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}
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readFile(ibin, bytes, 4);
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int inumber = Integer.parseInt(bytesToHex(bytes), 16);
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readFile(lbin, bytes, 4);
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if (!"00000801".equals(bytesToHex(bytes))) { // 2049
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System.err.println("The dataset label is not valid: " + bytesToHex(bytes));
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return;
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}
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readFile(lbin, bytes, 4);
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int lnumber = Integer.parseInt(bytesToHex(bytes), 16);
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if (inumber != lnumber) {
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System.err.println("input data cnt: " + inumber + " not equal label cnt: " + lnumber);
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return;
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}
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// read all labels
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byte[] labels = new byte[lnumber];
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readFile(lbin, labels, lnumber);
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// row, column
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readFile(ibin, bytes, 4);
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int n_rows = Integer.parseInt(bytesToHex(bytes), 16);
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readFile(ibin, bytes, 4);
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int n_cols = Integer.parseInt(bytesToHex(bytes), 16);
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if (n_rows != 28 || n_cols != 28) {
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System.err.println("invalid n_rows: " + n_rows + " n_cols: " + n_cols);
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return;
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}
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// read images
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int image_size = n_rows * n_cols;
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byte[] image_data = new byte[image_size];
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for (int i = 0; i < lnumber; i++) {
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float [] hwc_bin_image = new float[32 * 32];
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readFile(ibin, image_data, image_size);
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for (int r = 0; r < 32; r++) {
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for (int c = 0; c < 32; c++) {
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int index = r * 32 + c;
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if (r < 2 || r > 29 || c < 2 || c > 29) {
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hwc_bin_image[index] = 0;
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} else {
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int data = image_data[(r-2)*28 + (c-2)] & 0xff;
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hwc_bin_image[index] = (float)data / 255.0f;
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}
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}
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}
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DataLabelTuple data_label_tupel = new DataLabelTuple();
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data_label_tupel.data = hwc_bin_image;
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data_label_tupel.label = labels[i] & 0xff;
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dataset.add(data_label_tupel);
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}
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} catch (IOException e) {
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System.err.println("Read Dateset exception");
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}
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}
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public void setExpectedDataSize(long data_size) {
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expectedDataSize = data_size;
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}
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public long getNumOfClasses() {
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return numOfClasses;
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}
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public Vector<DataLabelTuple> getTestData() {
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return testData;
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}
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public Vector<DataLabelTuple> getTrainData() {
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return trainData;
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}
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}
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@ -0,0 +1,20 @@
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package com.mindspore.lite.train_lenet;
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import com.mindspore.lite.Version;
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public class Main {
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public static void main(String[] args) {
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System.out.println(Version.version());
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if (args.length < 2) {
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System.err.println("model path and dataset path must be provided.");
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return;
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}
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String modelPath = args[0];
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String datasetPath = args[1];
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NetRunner net_runner = new NetRunner();
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net_runner.trainModel(modelPath, datasetPath);
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}
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}
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package com.mindspore.lite.train_lenet;
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import com.mindspore.lite.MSTensor;
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import com.mindspore.lite.TrainSession;
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import com.mindspore.lite.config.MSConfig;
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import java.nio.ByteBuffer;
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import java.nio.ByteOrder;
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import java.util.List;
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import java.util.Map;
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import java.util.Vector;
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public class NetRunner {
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private int dataIndex = 0;
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private int labelIndex = 1;
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private TrainSession session;
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private long batchSize;
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private long dataSize; // one input data size, in byte
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private DataSet ds = new DataSet();
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private long numOfClasses;
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private long cycles = 3500;
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private int idx = 1;
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private String trainedFilePath = "trained.ms";
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public void initAndFigureInputs(String modelPath) {
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MSConfig msConfig = new MSConfig();
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// arg 0: DeviceType:DT_CPU -> 0
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// arg 1: ThreadNum -> 2
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// arg 2: cpuBindMode:NO_BIND -> 0
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// arg 3: enable_fp16 -> false
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msConfig.init(0, 2, 0, false);
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session = new TrainSession();
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session.init(modelPath, msConfig);
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session.setLearningRate(0.01f);
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List<MSTensor> inputs = session.getInputs();
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if (inputs.size() <= 1) {
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System.err.println("model input size: " + inputs.size());
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return;
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}
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dataIndex = 0;
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labelIndex = 1;
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batchSize = inputs.get(dataIndex).getShape()[0];
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dataSize = inputs.get(dataIndex).size() / batchSize;
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System.out.println("batch_size: " + batchSize);
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int index = modelPath.lastIndexOf(".ms");
|
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if (index == -1) {
|
||||
System.out.println("The model " + modelPath + " should be named *.ms");
|
||||
return;
|
||||
}
|
||||
trainedFilePath = modelPath.substring(0, index) + "_trained.ms";
|
||||
}
|
||||
|
||||
public int initDB(String datasetPath) {
|
||||
if (dataSize != 0) {
|
||||
ds.setExpectedDataSize(dataSize);
|
||||
}
|
||||
ds.initializeMNISTDatabase(datasetPath);
|
||||
numOfClasses = ds.getNumOfClasses();
|
||||
if (numOfClasses != 10) {
|
||||
System.err.println("unexpected num_of_class: " + numOfClasses);
|
||||
System.exit(1);
|
||||
}
|
||||
|
||||
if (ds.testData.size() == 0) {
|
||||
System.err.println("test data size is 0");
|
||||
return -1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
public float getLoss() {
|
||||
MSTensor tensor = searchOutputsForSize(1);
|
||||
return tensor.getFloatData()[0];
|
||||
}
|
||||
|
||||
private MSTensor searchOutputsForSize(int size) {
|
||||
Map<String, MSTensor> outputs = session.getOutputMapByTensor();
|
||||
for (MSTensor tensor : outputs.values()) {
|
||||
if (tensor.elementsNum() == size) {
|
||||
return tensor;
|
||||
}
|
||||
}
|
||||
System.err.println("can not find output the tensor which element num is " + size);
|
||||
return null;
|
||||
}
|
||||
|
||||
public int trainLoop() {
|
||||
session.train();
|
||||
float min_loss = 1000;
|
||||
float max_acc = 0;
|
||||
for (int i = 0; i < cycles; i++) {
|
||||
fillInputData(ds.getTrainData(), false);
|
||||
session.runGraph();
|
||||
float loss = getLoss();
|
||||
if (min_loss > loss) {
|
||||
min_loss = loss;
|
||||
}
|
||||
if ((i + 1) % 500 == 0) {
|
||||
float acc = calculateAccuracy(10); // only test 10 batch size
|
||||
if (max_acc < acc) {
|
||||
max_acc = acc;
|
||||
}
|
||||
System.out.println("step_" + (i + 1) + ": \tLoss is " + loss + " [min=" + min_loss + "]" + " max_accc=" + max_acc);
|
||||
}
|
||||
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
public float calculateAccuracy(long maxTests) {
|
||||
float accuracy = 0;
|
||||
Vector<DataSet.DataLabelTuple> test_set = ds.getTestData();
|
||||
long tests = test_set.size() / batchSize;
|
||||
if (maxTests != -1 && tests < maxTests) {
|
||||
tests = maxTests;
|
||||
}
|
||||
session.eval();
|
||||
for (long i = 0; i < tests; i++) {
|
||||
Vector<Integer> labels = fillInputData(test_set, (maxTests == -1));
|
||||
if (labels.size() != batchSize) {
|
||||
System.err.println("unexpected labels size: " + labels.size() + " batch_size size: " + batchSize);
|
||||
System.exit(1);
|
||||
}
|
||||
session.runGraph();
|
||||
MSTensor outputsv = searchOutputsForSize((int) (batchSize * numOfClasses));
|
||||
if (outputsv == null) {
|
||||
System.err.println("can not find output tensor with size: " + batchSize * numOfClasses);
|
||||
System.exit(1);
|
||||
}
|
||||
float[] scores = outputsv.getFloatData();
|
||||
for (int b = 0; b < batchSize; b++) {
|
||||
int max_idx = 0;
|
||||
float max_score = scores[(int) (numOfClasses * b)];
|
||||
for (int c = 0; c < numOfClasses; c++) {
|
||||
if (scores[(int) (numOfClasses * b + c)] > max_score) {
|
||||
max_score = scores[(int) (numOfClasses * b + c)];
|
||||
max_idx = c;
|
||||
}
|
||||
|
||||
}
|
||||
if (labels.get(b) == max_idx) {
|
||||
accuracy += 1.0;
|
||||
}
|
||||
}
|
||||
}
|
||||
session.train();
|
||||
accuracy /= (batchSize * tests);
|
||||
return accuracy;
|
||||
}
|
||||
|
||||
// each time fill batch_size data
|
||||
Vector<Integer> fillInputData(Vector<DataSet.DataLabelTuple> dataset, boolean serially) {
|
||||
Vector<Integer> labelsVec = new Vector<Integer>();
|
||||
int totalSize = dataset.size();
|
||||
|
||||
List<MSTensor> inputs = session.getInputs();
|
||||
|
||||
int inputDataCnt = inputs.get(dataIndex).elementsNum();
|
||||
float[] inputBatchData = new float[inputDataCnt];
|
||||
|
||||
int labelDataCnt = inputs.get(labelIndex).elementsNum();
|
||||
int[] labelBatchData = new int[labelDataCnt];
|
||||
|
||||
for (int i = 0; i < batchSize; i++) {
|
||||
if (serially) {
|
||||
idx = (++idx) % totalSize;
|
||||
} else {
|
||||
idx = (int) (Math.random() * totalSize);
|
||||
}
|
||||
|
||||
int label = 0;
|
||||
DataSet.DataLabelTuple dataLabelTuple = dataset.get(idx);
|
||||
label = dataLabelTuple.label;
|
||||
System.arraycopy(dataLabelTuple.data, 0, inputBatchData, (int) (i * dataLabelTuple.data.length), dataLabelTuple.data.length);
|
||||
labelBatchData[i] = label;
|
||||
labelsVec.add(label);
|
||||
}
|
||||
|
||||
ByteBuffer byteBuf = ByteBuffer.allocateDirect(inputBatchData.length * Float.BYTES);
|
||||
byteBuf.order(ByteOrder.nativeOrder());
|
||||
for (int i = 0; i < inputBatchData.length; i++) {
|
||||
byteBuf.putFloat(inputBatchData[i]);
|
||||
}
|
||||
inputs.get(dataIndex).setData(byteBuf);
|
||||
|
||||
ByteBuffer labelByteBuf = ByteBuffer.allocateDirect(labelBatchData.length * 4);
|
||||
labelByteBuf.order(ByteOrder.nativeOrder());
|
||||
for (int i = 0; i < labelBatchData.length; i++) {
|
||||
labelByteBuf.putInt(labelBatchData[i]);
|
||||
}
|
||||
inputs.get(labelIndex).setData(labelByteBuf);
|
||||
|
||||
return labelsVec;
|
||||
}
|
||||
|
||||
public void trainModel(String modelPath, String datasetPath) {
|
||||
System.out.println("==========Loading Model, Create Train Session=============");
|
||||
initAndFigureInputs(modelPath);
|
||||
System.out.println("==========Initing DataSet================");
|
||||
initDB(datasetPath);
|
||||
System.out.println("==========Training Model===================");
|
||||
trainLoop();
|
||||
System.out.println("==========Evaluating The Trained Model============");
|
||||
float acc = calculateAccuracy(-1);
|
||||
System.out.println("accuracy = " + acc);
|
||||
|
||||
if (cycles > 0) {
|
||||
if (session.saveToFile(trainedFilePath)) {
|
||||
System.out.println("Trained model successfully saved: " + trainedFilePath);
|
||||
} else {
|
||||
System.err.println("Save model error.");
|
||||
}
|
||||
}
|
||||
session.free();
|
||||
}
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue