提交 a6ed66b9 编写于 作者: N Nicky

Compare predict and actual data result

上级 34b152f7
......@@ -452,15 +452,18 @@ Use create_lod_tensor(data, lod, place) API to generate LoD Tensor, where `data`
For example, data = [[10, 2, 3], [2, 3]] means that it contains two sequences of indices, of length 3 and 2, respectively.
Correspondingly, lod = [[3, 2]] contains one level of detail info, indicating that `data` consists of two sequences of length 3 and 2.
In this infer example, we try to predict rating of movie 'Hunchback of Notre Dame' from user id 1 info.
```python
infer_movie_id = 783
infer_movie_name = paddle.dataset.movielens.movie_info()[infer_movie_id].title
user_id = fluid.create_lod_tensor([[1]], [[1]], place)
gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
age_id = fluid.create_lod_tensor([[0]], [[1]], place)
job_id = fluid.create_lod_tensor([[10]], [[1]], place)
movie_id = fluid.create_lod_tensor([[783]], [[1]], place)
category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place)
movie_id = fluid.create_lod_tensor([[783]], [[1]], place) # Hunchback of Notre Dame
category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place) # Animation, Children's, Musical
movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]], [[5]],
place)
place) # 'hunchback','of','notre','dame','the'
```
### Infer
......@@ -480,7 +483,9 @@ results = inferencer.infer(
},
return_numpy=False)
print("infer results: ", np.array(results[0]))
predict_rating = np.array(results[0])
print("Predict Rating of user id 1 on movie \"" + infer_movie_name + "\" is " + str(predict_rating[0][0]))
print("Actual Rating of user id 1 on movie \"" + infer_movie_name + "\" is 4.")
```
......
......@@ -494,15 +494,18 @@ Use create_lod_tensor(data, lod, place) API to generate LoD Tensor, where `data`
For example, data = [[10, 2, 3], [2, 3]] means that it contains two sequences of indices, of length 3 and 2, respectively.
Correspondingly, lod = [[3, 2]] contains one level of detail info, indicating that `data` consists of two sequences of length 3 and 2.
In this infer example, we try to predict rating of movie 'Hunchback of Notre Dame' from user id 1 info.
```python
infer_movie_id = 783
infer_movie_name = paddle.dataset.movielens.movie_info()[infer_movie_id].title
user_id = fluid.create_lod_tensor([[1]], [[1]], place)
gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
age_id = fluid.create_lod_tensor([[0]], [[1]], place)
job_id = fluid.create_lod_tensor([[10]], [[1]], place)
movie_id = fluid.create_lod_tensor([[783]], [[1]], place)
category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place)
movie_id = fluid.create_lod_tensor([[783]], [[1]], place) # Hunchback of Notre Dame
category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place) # Animation, Children's, Musical
movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]], [[5]],
place)
place) # 'hunchback','of','notre','dame','the'
```
### Infer
......@@ -522,7 +525,9 @@ results = inferencer.infer(
},
return_numpy=False)
print("infer results: ", np.array(results[0]))
predict_rating = np.array(results[0])
print("Predict Rating of user id 1 on movie \"" + infer_movie_name + "\" is " + str(predict_rating[0][0]))
print("Actual Rating of user id 1 on movie \"" + infer_movie_name + "\" is 4.")
```
......
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