I keep getting the same ValeError.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-70-9e8eaa918618> in <module>
9
10 # Run the component
---> 11 context.run(transform)
/opt/conda/lib/python3.8/site-packages/tfx/orchestration/experimental/interactive/interactive_context.py in run_if_ipython(*args, **kwargs)
65 # __IPYTHON__ variable is set by IPython, see
66 # https://ipython.org/ipython-doc/rel-0.10.2/html/interactive/reference.html#embedding-ipython.
---> 67 return fn(*args, **kwargs)
68 else:
69 absl.logging.warning(
/opt/conda/lib/python3.8/site-packages/tfx/orchestration/experimental/interactive/interactive_context.py in run(self, component, enable_cache, beam_pipeline_args)
180 telemetry_utils.LABEL_TFX_RUNNER: runner_label,
181 }):
--> 182 execution_id = launcher.launch().execution_id
183
184 return execution_result.ExecutionResult(
/opt/conda/lib/python3.8/site-packages/tfx/orchestration/launcher/base_component_launcher.py in launch(self)
200 absl.logging.info('Running executor for %s',
201 self._component_info.component_id)
--> 202 self._run_executor(execution_decision.execution_id,
203 execution_decision.input_dict,
204 execution_decision.output_dict,
/opt/conda/lib/python3.8/site-packages/tfx/orchestration/launcher/in_process_component_launcher.py in _run_executor(self, execution_id, input_dict, output_dict, exec_properties)
65 executor_context) # type: ignore
66
---> 67 executor.Do(input_dict, output_dict, exec_properties)
/opt/conda/lib/python3.8/site-packages/tfx/components/transform/executor.py in Do(self, input_dict, output_dict, exec_properties)
415 label_outputs[labels.CACHE_OUTPUT_PATH_LABEL] = cache_output
416 status_file = 'status_file' # Unused
--> 417 self.Transform(label_inputs, label_outputs, status_file)
418 absl.logging.debug('Cleaning up temp path %s on executor success',
419 temp_path)
/opt/conda/lib/python3.8/site-packages/tfx/components/transform/executor.py in Transform(***failed resolving arguments***)
933 materialization_format = (
934 transform_paths_file_formats[-1] if materialize_output_paths else None)
--> 935 self._RunBeamImpl(analyze_data_list, transform_data_list,
936 preprocessing_fn, input_dataset_metadata,
937 transform_output_path, raw_examples_data_format,
/opt/conda/lib/python3.8/site-packages/tfx/components/transform/executor.py in _RunBeamImpl(self, analyze_data_list, transform_data_list, preprocessing_fn, input_dataset_metadata, transform_output_path, raw_examples_data_format, temp_path, input_cache_dir, output_cache_dir, compute_statistics, per_set_stats_output_paths, materialization_format, analyze_paths_count)
1057
1058 transform_fn, cache_output = (
-> 1059 (input_analysis_data, input_cache,
1060 analyze_data_tensor_adapter_config)
1061 | 'Analyze' >> tft_beam.AnalyzeDatasetWithCache(
/opt/conda/lib/python3.8/site-packages/apache_beam/transforms/ptransform.py in __ror__(self, pvalueish, _unused)
1056
1057 def __ror__(self, pvalueish, _unused=None):
-> 1058 return self.transform.__ror__(pvalueish, self.label)
1059
1060 def expand(self, pvalue):
/opt/conda/lib/python3.8/site-packages/apache_beam/transforms/ptransform.py in __ror__(self, left, label)
571 pvalueish = _SetInputPValues().visit(pvalueish, replacements)
572 self.pipeline = p
--> 573 result = p.apply(self, pvalueish, label)
574 if deferred:
575 return result
/opt/conda/lib/python3.8/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
644 try:
645 old_label, transform.label = transform.label, label
--> 646 return self.apply(transform, pvalueish)
647 finally:
648 transform.label = old_label
/opt/conda/lib/python3.8/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
687 transform.type_check_inputs(pvalueish)
688
--> 689 pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
690
691 if type_options is not None and type_options.pipeline_type_check:
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
186 m = getattr(self, 'apply_%s' % cls.__name__, None)
187 if m:
--> 188 return m(transform, input, options)
189 raise NotImplementedError(
190 'Execution of [%s] not implemented in runner %s.' % (transform, self))
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
216 def apply_PTransform(self, transform, input, options):
217 # The base case of apply is to call the transform's expand.
--> 218 return transform.expand(input)
219
220 def run_transform(self,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/beam/impl.py in expand(self, dataset)
1168 input_values_pcoll_dict = dataset[1] or dict()
1169 analyzer_cache.validate_dataset_keys(input_values_pcoll_dict.keys())
-> 1170 return super(AnalyzeDatasetWithCache,
1171 self).expand(self._make_parent_dataset(dataset))
1172
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/beam/impl.py in expand(self, dataset)
1106 # deferred manner, once the analyzer outputs are known.
1107 if self._use_tf_compat_v1:
-> 1108 schema = schema_inference.infer_feature_schema(structured_outputs, graph)
1109 else:
1110 metadata_fn = schema_inference.get_traced_metadata_fn(
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in infer_feature_schema(features, graph, session)
148 feature_annotations[name] = tensor_annotations.get(hashable_values, [])
149
--> 150 return _infer_feature_schema_common(features, modified_tensor_ranges,
151 feature_annotations, global_annotations)
152
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in _infer_feature_schema_common(features, tensor_ranges, feature_annotations, global_annotations)
224 domains[name] = schema_pb2.IntDomain(
225 min=min_value, max=max_value, is_categorical=True)
--> 226 feature_spec = _feature_spec_from_batched_tensors(features)
227
228 schema_proto = schema_utils.schema_from_feature_spec(feature_spec, domains)
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in _feature_spec_from_batched_tensors(tensors)
71 shape = tensor.get_shape()
72 if shape.ndims in [None, 0]:
---> 73 raise ValueError(
74 'Feature {} ({}) had invalid shape {} for FixedLenFeature: must '
75 'have rank at least 1'.format(name, tensor, shape))
ValueError: Feature traffic_volume_xf (Tensor("Cast_2:0", shape=(), dtype=int64)) had invalid shape () for FixedLenFeature: must have rank at least 1
Here is the code for excercise 6:
And the excercise 7:
What could be the issue?
