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我有一个多类 NLP 分类问题，我需要针对大约 1000 个类别标签训练大约 100 万个文本样本。随着未来的数据集被输入管道，这个唯一类别标签的数量将略有变化。

为此，我需要将 tf.one_hot 中的深度参数设置为该训练实例的动态确定的唯一标签的数量。

要知道唯一标签的全部数量，我知道我需要完整地遍历数据。所以，我被困的地方是如何计算这个数字。

我认为 tft.size 适合获得这个完整的通行证，但它似乎不起作用。当我硬编码 1000 时，您可以在下面看到它工作正常：

    labels = inputs[LABEL_KEY]

    sparse_labels_tokens = tft.compute_and_apply_vocabulary(labels, vocab_filename=LABEL_VOCAB_FILE_NAME)
    
    dense_labels_tokens = tf.sparse.to_dense(sparse_labels_tokens)
    #labels_count = tf.cast( tft.size(dense_labels_tokens), tf.int32 ) #FIXME
    labels_count = 1000
    
    labels_one_hot = tf.one_hot(dense_labels_tokens, depth=labels_count)
    labels_indicators = tf.reduce_max(labels_one_hot, axis=1)
    outputs[transformed_name(LABEL_KEY)] = labels_indicators
    outputs[LABEL_KEY] = _fill_in_missing(inputs[LABEL_KEY])

给予：

# Iterate over the first few tfrecords and decode them.
for tfrecord in dataset.take(5):
    serialized_example = tfrecord.numpy()
    example = tf.train.Example()
    example.ParseFromString(serialized_example)
    pprint.pprint(example)```

  feature {
    key: "label_xf"
    value {
      float_list {
        value: 0.0
        value: 0.0
        value: 1.0
        value: 0.0

但是，如果我改为使用 tft.size 我会收到以下错误：

...~/.local/lib/python3.6/site-packages/tensorflow_transform/schema_inference.py in _infer_feature_schema_common(features, tensor_ranges, feature_annotations, global_annotations)
    241       domains[name] = schema_pb2.IntDomain(
    242           min=min_value, max=max_value, is_categorical=True)
--> 243   feature_spec = _feature_spec_from_batched_tensors(features)
    244 
    245   schema_proto = schema_utils.schema_from_feature_spec(feature_spec, domains)

~/.local/lib/python3.6/site-packages/tensorflow_transform/schema_inference.py in _feature_spec_from_batched_tensors(tensors)
     86             'Feature {} ({}) had invalid shape {} for FixedLenFeature: apart '
     87             'from the batch dimension, all dimensions must have known size'
---> 88             .format(name, tensor, shape))
     89       feature_spec[name] = tf.io.FixedLenFeature(shape.as_list()[1:],
     90                                                  tensor.dtype)

ValueError: Feature label_xf (Tensor("Max:0", shape=(None, None), dtype=float32)) had invalid shape (None, None) for FixedLenFeature: apart from the batch dimension, all dimensions must have known size

This cell will be skipped during export to pipeline.

我可以将深度硬编码到 1500 并交叉手指说标签数量永远不会超过这个值，但我不确定如果我这样做的话我是否能够和自己一起生活:(

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