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未验证 提交 21bd7a30 编写于 作者: H hutuxian 提交者： GitHub
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Revert upgrade 1.6 API (#825) 

* Revert "change -1 to None in data's shape (#819)"

This reverts commit 77fe59e5.

* Revert "Upgrade API using paddle 1.6 (#813)"

This reverts commit 900ba733.
上级 00c8ae7f
隐藏空白更改
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Showing with 139 addition and 137 deletion
05.recommender_system/README.cn.md
浏览文件 @ 21bd7a30
......@@ -254,9 +254,9 @@ def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = fluid.data(name='user_id', shape=[None], dtype='int64')
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = fluid.embedding(
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
......@@ -267,9 +267,9 @@ def get_usr_combined_features():
USR_GENDER_DICT_SIZE = 2
usr_gender_id = fluid.data(name='gender_id', shape=[None], dtype='int64')
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = fluid.embedding(
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
......@@ -278,9 +278,9 @@ def get_usr_combined_features():
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = fluid.data(name='age_id', shape=[None], dtype="int64")
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = fluid.embedding(
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
......@@ -289,9 +289,9 @@ def get_usr_combined_features():
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = fluid.data(name='job_id', shape=[None], dtype="int64")
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = fluid.embedding(
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
......@@ -320,9 +320,9 @@ def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = fluid.data(name='movie_id', shape=[None], dtype='int64')
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = fluid.embedding(
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
......@@ -333,10 +333,10 @@ def get_mov_combined_features():
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = fluid.data(
name='category_id', shape=[None], dtype='int64', lod_level=1)
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = fluid.embedding(
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
......@@ -344,10 +344,10 @@ def get_mov_combined_features():
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = fluid.data(
name='movie_title', shape=[None], dtype='int64', lod_level=1)
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = fluid.embedding(
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
......@@ -390,7 +390,7 @@ def train_program():
scale_infer = inference_program()
label = fluid.data(name='score', shape=[None, 1], dtype='float32')
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(square_cost)
......@@ -416,12 +416,12 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
下一步是为训练和测试定义数据提供器。提供器读入一个大小为 `BATCH_SIZE`的数据。`paddle.dataset.movielens.train` 每次会在乱序化后提供一个大小为`BATCH_SIZE`的数据，乱序化的大小为缓存大小`buf_size`。
```python
train_reader = fluid.io.batch(
fluid.io.shuffle(
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
test_reader = fluid.io.batch(
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
```
......@@ -533,13 +533,13 @@ train_loop()
```python
infer_movie_id = 783
infer_movie_name = paddle.dataset.movielens.movie_info()[infer_movie_id].title
user_id = np.array([1]).astype("int64").reshape(-1)
gender_id = np.array([1]).astype("int64").reshape(-1)
age_id = np.array([0]).astype("int64").reshape(-1)
job_id = np.array([10]).astype("int64").reshape(-1)
movie_id = np.array([783]).astype("int64").reshape(-1) # Hunchback of Notre Dame
category_id = fluid.create_lod_tensor(np.array([10, 8, 9], dtype='int64'), [[3]], place) # Animation, Children's, Musical
movie_title = fluid.create_lod_tensor(np.array([1069, 4140, 2923, 710, 988], dtype='int64'), [[5]],
user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place) # Hunchback of Notre Dame
category_id = fluid.create_lod_tensor([np.array([10, 8, 9], dtype='int64')], [[3]], place) # Animation, Children's, Musical
movie_title = fluid.create_lod_tensor([np.array([1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
place) # 'hunchback','of','notre','dame','the'
```
......
05.recommender_system/README.md
浏览文件 @ 21bd7a30
......@@ -241,9 +241,9 @@ def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = fluid.data(name='user_id', shape=[None], dtype='int64')
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = fluid.embedding(
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
......@@ -254,9 +254,9 @@ def get_usr_combined_features():
USR_GENDER_DICT_SIZE = 2
usr_gender_id = fluid.data(name='gender_id', shape=[None], dtype='int64')
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = fluid.embedding(
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
......@@ -265,9 +265,9 @@ def get_usr_combined_features():
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = fluid.data(name='age_id', shape=[None], dtype="int64")
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = fluid.embedding(
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
......@@ -276,9 +276,9 @@ def get_usr_combined_features():
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = fluid.data(name='job_id', shape=[None], dtype="int64")
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = fluid.embedding(
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
......@@ -307,9 +307,9 @@ def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = fluid.data(name='movie_id', shape=[None], dtype='int64')
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = fluid.embedding(
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
......@@ -320,10 +320,10 @@ def get_mov_combined_features():
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = fluid.data(
name='category_id', shape=[None], dtype='int64', lod_level=1)
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = fluid.embedding(
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
......@@ -331,10 +331,10 @@ def get_mov_combined_features():
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = fluid.data(
name='movie_title', shape=[None], dtype='int64', lod_level=1)
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = fluid.embedding(
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
......@@ -378,7 +378,7 @@ def train_program():
scale_infer = inference_program()
label = fluid.data(name='score', shape=[None, 1], dtype='float32')
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(square_cost)
......@@ -404,12 +404,12 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
The next step is to define a data provider for training and testing. The provider reads in a data of size `BATCH_SIZE`. `paddle.dataset.movielens.train` will provide a data of size `BATCH_SIZE` after each scribbling, and the size of the out-of-order is the cache size `buf_size`.
```python
train_reader = fluid.io.batch(
fluid.io.shuffle(
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
test_reader = fluid.io.batch(
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
```
......@@ -521,13 +521,13 @@ In this prediction example, we try to predict the score given by user with ID1 f
```python
infer_movie_id = 783
infer_movie_name = paddle.dataset.movielens.movie_info()[infer_movie_id].title
user_id = np.array([1]).astype("int64").reshape(-1)
gender_id = np.array([1]).astype("int64").reshape(-1)
age_id = np.array([0]).astype("int64").reshape(-1)
job_id = np.array([10]).astype("int64").reshape(-1)
movie_id = np.array([783]).astype("int64").reshape(-1) # Hunchback of Notre Dame
category_id = fluid.create_lod_tensor(np.array([10, 8, 9], dtype='int64'), [[3]], place) # Animation, Children's, Musical
movie_title = fluid.create_lod_tensor(np.array([1069, 4140, 2923, 710, 988], dtype='int64'), [[5]],
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) # 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) # 'hunchback','of','notre','dame','the'
```
......
05.recommender_system/index.cn.html
浏览文件 @ 21bd7a30
......@@ -296,9 +296,9 @@ def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = fluid.data(name='user_id', shape=[None], dtype='int64')
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = fluid.embedding(
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
......@@ -309,9 +309,9 @@ def get_usr_combined_features():
USR_GENDER_DICT_SIZE = 2
usr_gender_id = fluid.data(name='gender_id', shape=[None], dtype='int64')
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = fluid.embedding(
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
......@@ -320,9 +320,9 @@ def get_usr_combined_features():
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = fluid.data(name='age_id', shape=[None], dtype="int64")
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = fluid.embedding(
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
......@@ -331,9 +331,9 @@ def get_usr_combined_features():
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = fluid.data(name='job_id', shape=[None], dtype="int64")
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = fluid.embedding(
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
......@@ -362,9 +362,9 @@ def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = fluid.data(name='movie_id', shape=[None], dtype='int64')
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = fluid.embedding(
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
......@@ -375,10 +375,10 @@ def get_mov_combined_features():
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = fluid.data(
name='category_id', shape=[None], dtype='int64', lod_level=1)
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = fluid.embedding(
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
......@@ -386,10 +386,10 @@ def get_mov_combined_features():
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = fluid.data(
name='movie_title', shape=[None], dtype='int64', lod_level=1)
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = fluid.embedding(
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
......@@ -432,7 +432,7 @@ def train_program():
scale_infer = inference_program()
label = fluid.data(name='score', shape=[None, 1], dtype='float32')
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(square_cost)
......@@ -458,12 +458,12 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
下一步是为训练和测试定义数据提供器。提供器读入一个大小为 `BATCH_SIZE`的数据。`paddle.dataset.movielens.train` 每次会在乱序化后提供一个大小为`BATCH_SIZE`的数据，乱序化的大小为缓存大小`buf_size`。
```python
train_reader = fluid.io.batch(
fluid.io.shuffle(
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
test_reader = fluid.io.batch(
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
```
......@@ -575,13 +575,13 @@ train_loop()
```python
infer_movie_id = 783
infer_movie_name = paddle.dataset.movielens.movie_info()[infer_movie_id].title
user_id = np.array([1]).astype("int64").reshape(-1)
gender_id = np.array([1]).astype("int64").reshape(-1)
age_id = np.array([0]).astype("int64").reshape(-1)
job_id = np.array([10]).astype("int64").reshape(-1)
movie_id = np.array([783]).astype("int64").reshape(-1) # Hunchback of Notre Dame
category_id = fluid.create_lod_tensor(np.array([10, 8, 9], dtype='int64'), [[3]], place) # Animation, Children's, Musical
movie_title = fluid.create_lod_tensor(np.array([1069, 4140, 2923, 710, 988], dtype='int64'), [[5]],
user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place) # Hunchback of Notre Dame
category_id = fluid.create_lod_tensor([np.array([10, 8, 9], dtype='int64')], [[3]], place) # Animation, Children's, Musical
movie_title = fluid.create_lod_tensor([np.array([1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
place) # 'hunchback','of','notre','dame','the'
```
......
05.recommender_system/index.html
浏览文件 @ 21bd7a30
......@@ -283,9 +283,9 @@ def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = fluid.data(name='user_id', shape=[None], dtype='int64')
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = fluid.embedding(
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
......@@ -296,9 +296,9 @@ def get_usr_combined_features():
USR_GENDER_DICT_SIZE = 2
usr_gender_id = fluid.data(name='gender_id', shape=[None], dtype='int64')
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = fluid.embedding(
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
......@@ -307,9 +307,9 @@ def get_usr_combined_features():
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = fluid.data(name='age_id', shape=[None], dtype="int64")
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = fluid.embedding(
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
......@@ -318,9 +318,9 @@ def get_usr_combined_features():
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = fluid.data(name='job_id', shape=[None], dtype="int64")
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = fluid.embedding(
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
......@@ -349,9 +349,9 @@ def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = fluid.data(name='movie_id', shape=[None], dtype='int64')
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = fluid.embedding(
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
......@@ -362,10 +362,10 @@ def get_mov_combined_features():
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = fluid.data(
name='category_id', shape=[None], dtype='int64', lod_level=1)
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = fluid.embedding(
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
......@@ -373,10 +373,10 @@ def get_mov_combined_features():
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = fluid.data(
name='movie_title', shape=[None], dtype='int64', lod_level=1)
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = fluid.embedding(
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
......@@ -420,7 +420,7 @@ def train_program():
scale_infer = inference_program()
label = fluid.data(name='score', shape=[None, 1], dtype='float32')
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(square_cost)
......@@ -446,12 +446,12 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
The next step is to define a data provider for training and testing. The provider reads in a data of size `BATCH_SIZE`. `paddle.dataset.movielens.train` will provide a data of size `BATCH_SIZE` after each scribbling, and the size of the out-of-order is the cache size `buf_size`.
```python
train_reader = fluid.io.batch(
fluid.io.shuffle(
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
test_reader = fluid.io.batch(
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
```
......@@ -563,13 +563,13 @@ In this prediction example, we try to predict the score given by user with ID1 f
```python
infer_movie_id = 783
infer_movie_name = paddle.dataset.movielens.movie_info()[infer_movie_id].title
user_id = np.array([1]).astype("int64").reshape(-1)
gender_id = np.array([1]).astype("int64").reshape(-1)
age_id = np.array([0]).astype("int64").reshape(-1)
job_id = np.array([10]).astype("int64").reshape(-1)
movie_id = np.array([783]).astype("int64").reshape(-1) # Hunchback of Notre Dame
category_id = fluid.create_lod_tensor(np.array([10, 8, 9], dtype='int64'), [[3]], place) # Animation, Children's, Musical
movie_title = fluid.create_lod_tensor(np.array([1069, 4140, 2923, 710, 988], dtype='int64'), [[5]],
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) # 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) # 'hunchback','of','notre','dame','the'
```
......
05.recommender_system/train.py
浏览文件 @ 21bd7a30
......@@ -44,9 +44,9 @@ def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = fluid.data(name='user_id', shape=[None], dtype='int64')
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = fluid.embedding(
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
......@@ -57,9 +57,9 @@ def get_usr_combined_features():
USR_GENDER_DICT_SIZE = 2
usr_gender_id = fluid.data(name='gender_id', shape=[None], dtype='int64')
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = fluid.embedding(
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
......@@ -68,9 +68,9 @@ def get_usr_combined_features():
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = fluid.data(name='age_id', shape=[None], dtype="int64")
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = fluid.embedding(
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
......@@ -79,9 +79,9 @@ def get_usr_combined_features():
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = fluid.data(name='job_id', shape=[None], dtype="int64")
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = fluid.embedding(
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
......@@ -101,9 +101,9 @@ def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = fluid.data(name='movie_id', shape=[None], dtype='int64')
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = fluid.embedding(
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
......@@ -114,10 +114,10 @@ def get_mov_combined_features():
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = fluid.data(
name='category_id', shape=[None], dtype='int64', lod_level=1)
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = fluid.embedding(
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
......@@ -125,10 +125,10 @@ def get_mov_combined_features():
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = fluid.data(
name='movie_title', shape=[None], dtype='int64', lod_level=1)
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = fluid.embedding(
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
......@@ -153,7 +153,7 @@ def inference_program():
inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
scale_infer = layers.scale(x=inference, scale=5.0)
label = fluid.data(name='score', shape=[None, 1], dtype='float32')
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(square_cost)
......@@ -168,15 +168,16 @@ def train(use_cuda, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
if args.enable_ce:
train_reader = fluid.io.batch(
train_reader = paddle.batch(
paddle.dataset.movielens.train(), batch_size=BATCH_SIZE)
test_reader = fluid.io.batch(
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
else:
train_reader = fluid.io.batch(
fluid.io.shuffle(paddle.dataset.movielens.train(), buf_size=8192),
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
test_reader = fluid.io.batch(
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
feed_order = [
......@@ -292,27 +293,28 @@ def infer(use_cuda, params_dirname):
# Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
# level of detail info, indicating that `data` consists of two sequences
# of length 3 and 2, respectively.
user_id = np.array([1]).astype("int64").reshape(-1)
user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
assert feed_target_names[1] == "gender_id"
gender_id = np.array([1]).astype("int64").reshape(-1)
gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
assert feed_target_names[2] == "age_id"
age_id = np.array([0]).astype("int64").reshape(-1)
age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
assert feed_target_names[3] == "job_id"
job_id = np.array([10]).astype("int64").reshape(-1)
job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
assert feed_target_names[4] == "movie_id"
movie_id = np.array([783]).astype("int64").reshape(-1)
movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)
assert feed_target_names[5] == "category_id"
category_id = fluid.create_lod_tensor(
np.array([10, 8, 9], dtype='int64'), [[3]], place)
[np.array([10, 8, 9], dtype='int64')], [[3]], place)
assert feed_target_names[6] == "movie_title"
movie_title = fluid.create_lod_tensor(
np.array([1069, 4140, 2923, 710, 988], dtype='int64'), [[5]], place)
[np.array([1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
place)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
......
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