小编prb*_*_cm的帖子

我想开发 Stash 插件但无法开始。我们的 pom.xml 中有错误。

Project build error: Unknown packaging: atlassian-plugin
Project build error: Unresolveable build extension:
Plugin com.atlassian.maven.plugins:maven-stash-plugin:6.0.0
    or one of its dependencies could not be resolved:
Could not find artifact com.sun:tools:jar:1.7.0
    at specified path C:\Program Files (x86)\Java\jre1.7.0_75/../lib/tools.jar

我开始知道 maven 无法将 atlasian-plugin 识别为包类型。有没有办法创建我们自己的包装类型？解决此问题的最佳方法是什么？

pom.xml 文件：

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.xxxxx</groupId>
    <artifactId>fkjghff</artifactId>
    <version>856</version>

    <organization>
        <name>CATE Developer Experience</name>
        <url>www.xxxxxx</url>
    </organization>

    <name>name of the project</name>
    <description>description of the project</description>
    <packaging>atlassian-plugin</packaging>

    <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>com.atlassian.stash</groupId>
                <artifactId>stash-parent</artifactId>
                <version>${stash.version}</version>
                <type>pom</type> …

我想使用更少的训练数据样本来训练深度神经网络，以减少测试代码的时间。我想知道如何使用 Keras TensorFlow 对 Cifar-10 数据集进行子集化。我有以下代码，用于训练 Cifar-10 完整数据集。

#load and prepare data
if WhichDataSet == 'CIFAR10':
    (x_train, y_train), (x_test, y_test) = tensorflow.keras.datasets.cifar10.load_data()
else:
    (x_train, y_train), (x_test, y_test) = tensorflow.keras.datasets.cifar100.load_data()
num_classes = np.unique(y_train).shape[0]
K_train = x_train.shape[0]
input_shape = x_train.shape[1:]
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
y_train = tensorflow.keras.utils.to_categorical(y_train, num_classes)
y_test = tensorflow.keras.utils.to_categorical(y_test, num_classes)