An array of structures, where each structure displays the results of one feature evaluation assignment to one user session. Type: Array of EvaluationResult objectsErrors For information about the errors that are common to all actions, see Common Errors.Access...
public void setRequests(Collection<EvaluationRequest> requests) An array of structures, where each structure assigns a feature variation to one user session. Parameters: requests- An array of structures, where each structure assigns a feature variation to one user session. ...
autoScaleEvaluationInterval string The time interval at which to automatically adjust the Pool size according to the autoscale formula. The default value is 15 minutes. The minimum and maximum value are 5 minutes and 168 hours respectively. If you specify a value less than 5 minutes or great...
evaluationInterval 如果省略,则默认值为 15 分钟(PT15M)。 字符串 公式 池中所需计算节点数的公式。 string (必需) AutoUserSpecification 展开表 名字描述价值 elevationLevel 默认值为 nonAdmin。 “Admin”“NonAdmin” 范围 默认值为 Pool。 如果池正在运行 Windows,则应指定任务值(如果需要在任务之间进行更严...
(xu). Next, the objective evaluation function is defined as_evaluate_F(), where the argumentxis a batch of design variables. As a simple example, we define the first objective as the first design variable, and the second objective as the second design variable subtracting the third design ...
autoScaleEvaluationInterval string 根据自动缩放公式自动调整池大小的时间间隔。 默认值为 15 分钟。 最小值和最大值分别为 5 分钟和 168 小时。 如果指定的值小于 5 分钟或大于 168 小时,Batch 服务将拒绝请求并显示无效属性值错误;如果直接调用 REST API,则 HTTP 状态代码为 400(请求错误)。 autoScaleForm...
Java 类名:com.alibaba.alink.operator.batch.evaluation.EvalMultiLabelBatchOp Python 类名:EvalMultiLabelBatchOp 功能介绍 多label分类评估是对多label分类算法的预测结果进行效果评估,支持下列评估指标。 f1 参数说明 代码示例 Python 代码 frompyalink.alinkimport*importpandas as pd ...
autoScaleEvaluationInterval string The time interval at which to automatically adjust the Pool size according to the autoscale formula. The default value is 15 minutes. The minimum and maximum value are 5 minutes and 168 hours respectively. If you specify a value less than 5 minutes or great...
个人认为,这种强大的效果其实来自于back-propagation时候,来自于均值和方差对输入样本的梯度( )。 这也是BN在训练模式与其在测试模式的重要区别,在测试模式(evaluation mode)下, 使用训练集上累积的均值和方差,在back-propagation的时候他们对输入样本没有梯度(gradient)。
An array of structures, where each structure displays the results of one feature evaluation assignment to one user session. Type: Array of EvaluationResult objectsErrors For information about the errors that are common to all actions, see Common Errors.Access...