类型错误：__init__() 得到了一个意外的关键字参数 'ragged'

Question

TypeError: init () 得到一个意外的关键字参数 'ragged'

from collections import Container

fs = !ls /tmp/flower_photos/sample/*.jpg
uri_df = spark.createDataFrame(fs, StringType()).toDF("uri")
uri_df.show()
keras_pred_df = transformer.transform(uri_df)

所以问题是这个错误 TypeError: init () got an unexpected keyword argument 'ragged'

在这一行：

keras_pred_df = transformer.transform(uri_df)

完整的笔记本在此链接中： https : //drive.google.com/file/d/15B_RoLy2hMTYN0momooxUJmhW8qOZ0ng/view?usp=sharing

TypeError                                 Traceback (most recent call last)
<command-283125553983182> in <module>
      4 uri_df = spark.createDataFrame(fs, StringType()).toDF("uri")
      5 uri_df.show()
----> 6 keras_pred_df = transformer.transform(uri_df)

/databricks/spark/python/pyspark/ml/base.py in transform(self, dataset, params)
    171                 return self.copy(params)._transform(dataset)
    172             else:
--> 173                 return self._transform(dataset)
    174         else:
    175             raise ValueError("Params must be a param map but got %s." % type(params))

/local_disk0/spark-e5f610f1-7237-4c13-b797-13678ab4f87a/userFiles-4ecd8f57-be4d-421f-8819-be7bd43892bb/addedFile6225198141175559077spark_deep_learning_1_4_0_spark2_4_s_2_11-4ec64.jar/sparkdl/transformers/keras_tensor.py in _transform(self, dataset)
     55         with KSessionWrap() as (sess, keras_graph):
     56             tfGraph, inputTensorName, outputTensorName = self._loadTFGraph(sess=sess,
---> 57                                                                            graph=keras_graph)
     58             inputGraph = TFInputGraph.fromGraph(graph=tfGraph, sess=sess,
     59                                                 feed_names=[inputTensorName],

/local_disk0/spark-e5f610f1-7237-4c13-b797-13678ab4f87a/userFiles-4ecd8f57-be4d-421f-8819-be7bd43892bb/addedFile6225198141175559077spark_deep_learning_1_4_0_spark2_4_s_2_11-4ec64.jar/sparkdl/param/shared_params.py in _loadTFGraph(self, sess, graph)
    165         with graph.as_default():
    166             K.set_learning_phase(0)  # Inference phase
--> 167             model = load_model(self.getModelFile())
    168             out_op_name = tfx.op_name(model.output, graph)
    169             stripped_graph = tfx.strip_and_freeze_until([out_op_name], graph, sess,

/databricks/python/lib/python3.7/site-packages/keras/engine/saving.py in load_wrapper(*args, **kwargs)
    456                 os.remove(tmp_filepath)
    457             return res
--> 458         return load_function(*args, **kwargs)
    459 
    460     return load_wrapper

/databricks/python/lib/python3.7/site-packages/keras/engine/saving.py in load_model(filepath, custom_objects, compile)
    548     if H5Dict.is_supported_type(filepath):
    549         with H5Dict(filepath, mode='r') as h5dict:
--> 550             model = _deserialize_model(h5dict, custom_objects, compile)
    551     elif hasattr(filepath, 'write') and callable(filepath.write):
    552         def load_function(h5file):

/databricks/python/lib/python3.7/site-packages/keras/engine/saving.py in _deserialize_model(h5dict, custom_objects, compile)
    241         raise ValueError('No model found in config.')
    242     model_config = json.loads(model_config.decode('utf-8'))
--> 243     model = model_from_config(model_config, custom_objects=custom_objects)
    244     model_weights_group = h5dict['model_weights']
    245