小编Yan*_*ang的帖子

我试图在intellij中运行一个简单的火花示例,但我得到的错误是这样的:

Exception in thread "main" java.lang.ExceptionInInitializerError
at org.apache.spark.SparkContext.withScope(SparkContext.scala:701)
at org.apache.spark.SparkContext.textFile(SparkContext.scala:819)
at spark.test$.main(test.scala:19)
at spark.test.main(test.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)
Caused by: com.fasterxml.jackson.databind.JsonMappingException: Incompatible Jackson version: 2.7.6
at com.fasterxml.jackson.module.scala.JacksonModule$class.setupModule(JacksonModule.scala:64)
at com.fasterxml.jackson.module.scala.DefaultScalaModule.setupModule(DefaultScalaModule.scala:19)
at com.fasterxml.jackson.databind.ObjectMapper.registerModule(ObjectMapper.java:730)
at org.apache.spark.rdd.RDDOperationScope$.<init>(RDDOperationScope.scala:82)
at org.apache.spark.rdd.RDDOperationScope$.<clinit>(RDDOperationScope.scala)

我试图更新我的杰克逊依赖,但它似乎不起作用,我这样做:

libraryDependencies += "com.fasterxml.jackson.core" % "jackson-core" % "2.8.7"
libraryDependencies += "com.fasterxml.jackson.core" % "jackson-databind" % "2.8.7"

但它仍然出现相同的错误消息,有人可以帮我修复错误吗？

这是spark示例代码:

object test {
def main(args: Array[String]): Unit = {
    if (args.length < 1) {
        System.err.println("Usage: <file>")
        System.exit(1)
    }

    val conf = …

我在使用tensorflow恢复模型时使用批量规范有一点问题.

以下是我的批量规范,从这里:

def _batch_normalization(self, input_tensor, is_training, batch_norm_epsilon, decay=0.999):
    """batch normalization for dense nets.

    Args:
        input_tensor: `tensor`, the input tensor which needed normalized.
        is_training: `bool`, if true than update the mean/variance using moving average,
                             else using the store mean/variance.
        batch_norm_epsilon: `float`, param for batch normalization.
        decay: `float`, param for update move average, default is 0.999.

    Returns:
        normalized params.
    """
    # actually batch normalization is according to the channels dimension.
    input_shape_channels = int(input_tensor.get_shape()[-1])

    # scala and beta using in the …