问题描述
我正在尝试为我的模型制作一个评估器。到现在为止,所有其他组件都很好,但是当我尝试此配置时:
eval_config = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(label_key='Category'),],metrics_specs=tfma.metrics.default_multi_class_classification_specs(),slicing_specs=[
tfma.SlicingSpec(),tfma.SlicingSpec(feature_keys=['Category'])
])
创建此评估器:
model_resolver = ResolverNode(
instance_name='latest_blessed_model_resolver',resolver_class=latest_blessed_model_resolver.LatestBlessedModelResolver,model=Channel(type=Model),model_blessing=Channel(type=ModelBlessing))
context.run(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs['examples'],model=trainer.outputs['model'],baseline_model=model_resolver.outputs['model'],eval_config=eval_config)
context.run(evaluator)
我明白了:
[...]
IndexError Traceback (most recent call last)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/apache_beam/runners/common.cpython-37m-darwin.so in apache_beam.runners.common.DoFnRunner.process()
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/apache_beam/runners/common.cpython-37m-darwin.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/apache_beam/runners/common.cpython-37m-darwin.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/apache_beam/runners/common.cpython-37m-darwin.so in apache_beam.runners.common._OutputProcessor.process_outputs()
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/apache_beam/runners/worker/operations.cpython-37m-darwin.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/apache_beam/runners/worker/operations.cpython-37m-darwin.so in apache_beam.runners.worker.operations.PGBKCVOperation.process()
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/apache_beam/runners/worker/operations.cpython-37m-darwin.so in apache_beam.runners.worker.operations.PGBKCVOperation.process()
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow_model_analysis/evaluators/metrics_and_plots_evaluator_v2.py in add_input(self,accumulator,element)
355 for i,(c,a) in enumerate(zip(self._combiners,accumulator)):
--> 356 result = c.add_input(a,get_combiner_input(elements[0],i))
357 for e in elements[1:]:
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow_model_analysis/metrics/calibration_histogram.py in add_input(self,element)
141 flatten=True,--> 142 class_weights=self._class_weights)):
143 example_weight = float(example_weight)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow_model_analysis/metrics/metric_util.py in to_label_prediction_example_weight(inputs,eval_config,model_name,output_name,sub_key,class_weights,flatten,squeeze,allow_none)
283 elif sub_key.top_k is not None:
--> 284 label,prediction = select_top_k(sub_key.top_k,label,prediction)
285
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow_model_analysis/metrics/metric_util.py in select_top_k(top_k,labels,predictions,scores)
621 if not labels.shape or labels.shape[-1] == 1:
--> 622 labels = one_hot(labels,predictions)
623
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow_model_analysis/metrics/metric_util.py in one_hot(tensor,target)
671 # indexing the -1 and then removing it after.
--> 672 tensor = np.delete(np.eye(target.shape[-1] + 1)[tensor],-1,axis=-1)
673 return tensor.reshape(target.shape)
IndexError: arrays used as indices must be of integer (or boolean) type
During handling of the above exception,another exception occurred:
[...]
IndexError: arrays used as indices must be of integer (or boolean) type [while running 'ExtractEvaluateAndWriteResults/ExtractAndEvaluate/EvaluateMetricsAndPlots/ComputeMetricsAndPlots()/ComputePerSlice/ComputeUnsampledMetrics/CombinePerSliceKey/WindowIntoDiscarding']
我以为这是我的配置,但是我不明白这是怎么回事。
我正在使用此数据集TF Github。 我已遵循以下笔记本:Kaggle - BBC News Classification以便通过Tensorflow Serving服务我的模型。
注意:我正在使用的模型如下:
def _build_keras_model(vectorize_layer: TextVectorization) -> tf.keras.Model:
input_layer = tf.keras.layers.Input(shape=(1,),dtype=tf.string)
deep = vectorize_layer(input_layer)
deep = layers.Embedding(_max_features + 1,_embedding_dim)(deep)
deep = layers.Dropout(0.5)(deep)
deep = layers.GlobalAveragePooling1D()(deep)
deep = layers.Dropout(0.5)(deep)
output = layers.Dense(5,activation=tf.nn.softmax)(deep)
model = tf.keras.Model(input_layer,output)
model.compile(
loss=losses.SparseCategoricalCrossentropy(from_logits=True),optimizer='adam',metrics=['accuracy'])
model.summary(print_fn=absl.logging.info)
return model
解决方法
我明白了。我的问题是在数据集中,标签(文档类别)的格式为字符串(例如:“体育”,“业务”等)。因此,为了将其编码为整数,我使用了Transform组件对其进行了预处理。
但是,在构建评估器组件时,我传递了ExampleGen组件,其中未对数据进行任何处理。因此,评估人员试图将ExampleGen中的字符串转换为与模型输出的整数相匹配。
因此,要解决此问题,我只是这样做了:
model_resolver = ResolverNode(
instance_name='latest_blessed_model_resolver',resolver_class=latest_blessed_model_resolver.LatestBlessedModelResolver,model=Channel(type=Model),model_blessing=Channel(type=ModelBlessing))
context.run(model_resolver)
evaluator = Evaluator(
examples=transform.outputs['transformed_examples'],model=trainer.outputs['model'],baseline_model=model_resolver.outputs['model'],eval_config=eval_config)
context.run(evaluator)
我使用了来自transform组件的示例。当然,我还更改了配置中的标签键以匹配转换组件的标签名称。
我不知道是否有一种“清洁”的方式来执行此操作(或者如果我做错了,请纠正我!)