Add create LoDTensor from list option and simplify recommender book example #10946
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kexinzhao
changed the title
daming-lu
approved these changes
May 25, 2018
| @@ -126,6 +126,18 @@ def create_lod_tensor(data, lod, place): | |||
| """ | |||
| if isinstance(data, core.LoDTensor): | |||
| return create_lod_tensor(np.array(data), lod, place) | |||
| elif isinstance(data, list): | |||
| # When input data is a list, it only deal with the case where the base element | |||
| category_id = create_lod_tensor([[10], [8], [9]], [[0, 3]]) | ||
| movie_title = create_lod_tensor([[1069], [4140], [2923], [710], [988]], | ||
| [[0, 5]]) | ||
| # Use the first data from paddle.dataset.movielens.test() as input. |
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Since we are touching this inference, I raised an issue here, but it is not related to this PR 🤣
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Please check #10735 for why we are doing this.
To solve the issue above, we have merged a PR #10817 and now there is utility functions here to create LoDTensor.
In this PR, we further enhance
create_lod_tensorAPI so that user can create LoDTensor from a list of sequences of data as follows:Use create_lod_tensor(data, lod, place) API to generate LoD Tensor,
where
datais a list of sequences of index numbers,lodisthe level of detail (lod) info associated with
data.For example, data = [[10, 2, 3], [2, 3]] means that it contains
two sequences of indexes, of length 3 and 2, respectively.
Correspondingly, lod = [[3, 2]] contains one level of detail info,
indicating that
dataconsists of two sequences of length 3 and 2.Using this new option, we also simplify the recommender system book example.