RTS
tensorflow
data_error
ai_generated
partial
无效参数错误：行长度必须为非负。得到值：[-1, 3, 2]
InvalidArgumentError: Row lengths must be non-negative. Got values: [-1, 3, 2]
ID: tensorflow/ragged-tensor-to-sparse-invalid-splits
75%修复率
84%置信度
1证据数
2023-09-30首次发现
版本兼容性
| 版本 | 状态 | 引入 | 弃用 | 备注 |
|---|---|---|---|---|
| tensorflow 2.10 | active | — | — | — |
| tensorflow 2.11 | active | — | — | — |
| tensorflow 2.12 | active | — | — | — |
| tensorflow 2.13 | active | — | — | — |
根因分析
不规则张量构造或转换为稀疏张量时遇到负行长度，通常是由于数据损坏或嵌套 tf.RaggedTensor.from_row_splits 中的索引错误。
English
Ragged tensor construction or conversion to sparse tensor encountered negative row lengths, typically due to corrupted data or incorrect indexing in nested tf.RaggedTensor.from_row_splits.
官方文档
https://www.tensorflow.org/api_docs/python/tf/RaggedTensor解决方案
-
Validate and sanitize row splits before constructing RaggedTensor: row_splits = [0, 2, 5, 8]; row_splits = tf.maximum(row_splits, 0); row_splits = tf.cast(row_splits, tf.int64); rt = tf.RaggedTensor.from_row_splits(values, row_splits)
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Use tf.RaggedTensor.from_value_rowids instead of from_row_splits if row lengths are derived from data: value_rowids = [0, 0, 1, 1, 1, 2, 2, 2]; rt = tf.RaggedTensor.from_value_rowids(values, value_rowids)
无效尝试
常见但无效的做法:
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Using tf.debugging.assert_all_values_non_negative on the entire tensor
85% 失败
The error is in the splits structure, not the values; the assert would not catch the issue in row lengths.
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Setting allow_negative=True on RaggedTensor constructor (non-existent flag)
99% 失败
There is no such flag; negative row lengths are always invalid.