Python ValueError:未在未知的TensorShape上定义as_list（）

我在这个例子的基础上工作，这是我在这之后得到的

   jobs_train, jobs_test = jobs_df.randomSplit([0.6, 0.4])
>>> zuckerberg_train, zuckerberg_test = zuckerberg_df.randomSplit([0.6, 0.4])
>>> train_df = jobs_train.unionAll(zuckerberg_train)
>>> test_df = jobs_test.unionAll(zuckerberg_test)
>>> from pyspark.ml.classification import LogisticRegression
>>> from pyspark.ml import Pipeline
>>> from sparkdl import DeepImageFeaturizer
>>> featurizer = DeepImageFeaturizer(inputCol="image", outputCol="features", modelName="InceptionV3")
>>> lr = LogisticRegression(maxIter=20, regParam=0.05, elasticNetParam=0.3, labelCol="label")
>>> p = Pipeline(stages=[featurizer, lr])
>>> p_model = p.fit(train_df)

这就出现了

2018-06-08 20:57:18.985543: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
    INFO:tensorflow:Froze 376 variables.
    Converted 376 variables to const ops.
    Using TensorFlow backend.
    Using TensorFlow backend.
    INFO:tensorflow:Froze 0 variables.
    Converted 0 variables to const ops.
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "/opt/spark/python/pyspark/ml/base.py", line 64, in fit
        return self._fit(dataset)
      File "/opt/spark/python/pyspark/ml/pipeline.py", line 106, in _fit
        dataset = stage.transform(dataset)
      File "/opt/spark/python/pyspark/ml/base.py", line 105, in transform
        return self._transform(dataset)
      File "/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_spark-deep-learning-0.1.0-spark2.1-s_2.11.jar/sparkdl/transformers/named_image.py", line 159, in _transform
      File "/opt/spark/python/pyspark/ml/base.py", line 105, in transform
        return self._transform(dataset)
      File "/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_spark-deep-learning-0.1.0-spark2.1-s_2.11.jar/sparkdl/transformers/named_image.py", line 222, in _transform
      File "/opt/spark/python/pyspark/ml/base.py", line 105, in transform
        return self._transform(dataset)
      File "/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_spark-deep-learning-0.1.0-spark2.1-s_2.11.jar/sparkdl/transformers/tf_image.py", line 142, in _transform
      File "/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_tensorframes-0.2.8-s_2.11.jar/tensorframes/core.py", line 211, in map_rows
      File "/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_tensorframes-0.2.8-s_2.11.jar/tensorframes/core.py", line 132, in _map
      File "/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_tensorframes-0.2.8-s_2.11.jar/tensorframes/core.py", line 66, in _add_shapes
      File "/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_tensorframes-0.2.8-s_2.11.jar/tensorframes/core.py", line 35, in _get_shape
      File "/home/sulistyo/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py", line 900, in as_list
        raise ValueError("as_list() is not defined on an unknown TensorShape.")
    ValueError: as_list() is not defined on an unknown TensorShape.

2018-06-08 20:57:18.985543:I tensorflow/core/platform/cpu\u feature\u guard.cc:140]您的cpu支持未编译此tensorflow二进制文件以使用的指令：AVX2 FMA
信息：tensorflow：冻结376个变量。
将376个变量转换为常量。
使用TensorFlow后端。
使用TensorFlow后端。
信息：tensorflow:冻结0个变量。
已将0个变量转换为常量。
回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
文件“/opt/spark/python/pyspark/ml/base.py”，第64行，适合
返回自拟合（数据集）
文件“/opt/spark/python/pyspark/ml/pipeline.py”，第106行，格式为
数据集=stage.transform（数据集）
文件“/opt/spark/python/pyspark/ml/base.py”，第105行，在转换中
返回自转换（数据集）
文件“/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_spark-deep-learning-0.1.0-spark2.1-s_2.11.jar/sparkdl/transformers/named_image.py”，第159行，在_变换中
文件“/opt/spark/python/pyspark/ml/base.py”，第105行，在转换中
返回自转换（数据集）
文件“/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_spark-deep-learning-0.1.0-spark2.1-s_2.11.jar/sparkdl/transformers/named_image.py”，第222行，在_变换中
文件“/opt/spark/python/pyspark/ml/base.py”，第105行，在转换中
返回自转换（数据集）
文件“/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_spark-deep-learning-0.1.0-spark2.1-s_2.11.jar/sparkdl/transformers/tf_image.py”，第142行，in_transform
文件“/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_tensorframes-0.2.8-s_2.11.jar/tensorframes/core.py”，第211行，地图行
文件“/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_tensorframes-0.2.8-s_2.11.jar/tensorframes/core.py”，地图第132行
文件“/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/databricks_tensorframes-0.2.8-s_2.11.jar/tensorframes/core.py”，第66行，以“添加”的形式显示
文件“/tmp/spark-74707b69-e8c9-498b-b0f2-b38828e5ad21/userFiles-ca1eb7cf-9785-441d-a098-54b62380bcee/DataRicks_tensorframes-0.2.8-s_2.11.jar/tensorframes/core.py”，第35行，呈“get”字形
文件“/home/sulistyo/tensorflow/lib/python3.6/site packages/tensorflow/python/framework/tensor_shape.py”，as_列表第900行
raise VALUERROR（“as_list（）未在未知的张量形状上定义。”）
ValueError:未在未知的TensorShape上定义as_list（）。

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请提供帮助，谢谢

使用以下图片阅读并创建您的培训和测试集

from pyspark.sql.functions import lit
from sparkdl.image import imageIO

img_dir = "/PATH/TO/personalities/"

jobs_df = imageIO.readImagesWithCustomFn(img_dir + "/jobs",decode_f=imageIO.PIL_decode).withColumn("label", lit(1))
zuckerberg_df = imageIO.readImagesWithCustomFn(img_dir + "/zuckerberg", decode_f=imageIO.PIL_decode).withColumn("label", lit(0))

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非常感谢，在我的例子中，我忘记了使用Python2，因为源代码是针对Python2的。