diff --git a/05.recommender_system/README.cn.md b/05.recommender_system/README.cn.md index f5d07326821670df103718a879963ccf6b13874a..ffd07a1252e4ab58e81198d2dfbab78f9bd7c120 100644 --- a/05.recommender_system/README.cn.md +++ b/05.recommender_system/README.cn.md @@ -254,7 +254,7 @@ 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 = fluid.data(name='user_id', shape=[-1], dtype='int64') usr_emb = fluid.embedding( input=uid, @@ -267,7 +267,7 @@ 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 = fluid.data(name='gender_id', shape=[-1], dtype='int64') usr_gender_emb = fluid.embedding( input=usr_gender_id, @@ -278,7 +278,7 @@ 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 = fluid.data(name='age_id', shape=[-1], dtype="int64") usr_age_emb = fluid.embedding( input=usr_age_id, @@ -289,7 +289,7 @@ 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 = fluid.data(name='job_id', shape=[-1], dtype="int64") usr_job_emb = fluid.embedding( input=usr_job_id, @@ -320,7 +320,7 @@ 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 = fluid.data(name='movie_id', shape=[-1], dtype='int64') mov_emb = fluid.embedding( input=mov_id, @@ -334,7 +334,7 @@ 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) + name='category_id', shape=[-1], dtype='int64', lod_level=1) mov_categories_emb = fluid.embedding( input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -345,7 +345,7 @@ 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) + name='movie_title', shape=[-1], dtype='int64', lod_level=1) mov_title_emb = fluid.embedding( input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -390,7 +390,7 @@ def train_program(): scale_infer = inference_program() - label = fluid.data(name='score', shape=[None, 1], dtype='float32') + label = fluid.data(name='score', shape=[-1, 1], dtype='float32') square_cost = layers.square_error_cost(input=scale_infer, label=label) avg_cost = layers.mean(square_cost) diff --git a/05.recommender_system/README.md b/05.recommender_system/README.md index 2a8233c7279fbbe4bdb273b1f3152cc611e1095b..abf032c6a513c1f7a95a687d38ca355a578c22e8 100644 --- a/05.recommender_system/README.md +++ b/05.recommender_system/README.md @@ -241,7 +241,7 @@ 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 = fluid.data(name='user_id', shape=[-1], dtype='int64') usr_emb = fluid.embedding( input=uid, @@ -254,7 +254,7 @@ 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 = fluid.data(name='gender_id', shape=[-1], dtype='int64') usr_gender_emb = fluid.embedding( input=usr_gender_id, @@ -265,7 +265,7 @@ 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 = fluid.data(name='age_id', shape=[-1], dtype="int64") usr_age_emb = fluid.embedding( input=usr_age_id, @@ -276,7 +276,7 @@ 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 = fluid.data(name='job_id', shape=[-1], dtype="int64") usr_job_emb = fluid.embedding( input=usr_job_id, @@ -307,7 +307,7 @@ 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 = fluid.data(name='movie_id', shape=[-1], dtype='int64') mov_emb = fluid.embedding( input=mov_id, @@ -321,7 +321,7 @@ 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) + name='category_id', shape=[-1], dtype='int64', lod_level=1) mov_categories_emb = fluid.embedding( input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -332,7 +332,7 @@ 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) + name='movie_title', shape=[-1], dtype='int64', lod_level=1) mov_title_emb = fluid.embedding( input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -378,7 +378,7 @@ def train_program(): scale_infer = inference_program() - label = fluid.data(name='score', shape=[None, 1], dtype='float32') + label = fluid.data(name='score', shape=[-1, 1], dtype='float32') square_cost = layers.square_error_cost(input=scale_infer, label=label) avg_cost = layers.mean(square_cost) diff --git a/05.recommender_system/index.cn.html b/05.recommender_system/index.cn.html index b75bae0a93451775b78f03b2bec64b74e48aee8a..17191a58795ea4c0879958fd0ca5ce639877f678 100644 --- a/05.recommender_system/index.cn.html +++ b/05.recommender_system/index.cn.html @@ -296,7 +296,7 @@ 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 = fluid.data(name='user_id', shape=[-1], dtype='int64') usr_emb = fluid.embedding( input=uid, @@ -309,7 +309,7 @@ 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 = fluid.data(name='gender_id', shape=[-1], dtype='int64') usr_gender_emb = fluid.embedding( input=usr_gender_id, @@ -320,7 +320,7 @@ 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 = fluid.data(name='age_id', shape=[-1], dtype="int64") usr_age_emb = fluid.embedding( input=usr_age_id, @@ -331,7 +331,7 @@ 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 = fluid.data(name='job_id', shape=[-1], dtype="int64") usr_job_emb = fluid.embedding( input=usr_job_id, @@ -362,7 +362,7 @@ 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 = fluid.data(name='movie_id', shape=[-1], dtype='int64') mov_emb = fluid.embedding( input=mov_id, @@ -376,7 +376,7 @@ 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) + name='category_id', shape=[-1], dtype='int64', lod_level=1) mov_categories_emb = fluid.embedding( input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -387,7 +387,7 @@ 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) + name='movie_title', shape=[-1], dtype='int64', lod_level=1) mov_title_emb = fluid.embedding( input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -432,7 +432,7 @@ def train_program(): scale_infer = inference_program() - label = fluid.data(name='score', shape=[None, 1], dtype='float32') + label = fluid.data(name='score', shape=[-1, 1], dtype='float32') square_cost = layers.square_error_cost(input=scale_infer, label=label) avg_cost = layers.mean(square_cost) diff --git a/05.recommender_system/index.html b/05.recommender_system/index.html index c854cd253bf28fa88cf8ea2be485cbef42bb1acd..808d891cf58a077e35bd42a2739c708d540257a3 100644 --- a/05.recommender_system/index.html +++ b/05.recommender_system/index.html @@ -283,7 +283,7 @@ 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 = fluid.data(name='user_id', shape=[-1], dtype='int64') usr_emb = fluid.embedding( input=uid, @@ -296,7 +296,7 @@ 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 = fluid.data(name='gender_id', shape=[-1], dtype='int64') usr_gender_emb = fluid.embedding( input=usr_gender_id, @@ -307,7 +307,7 @@ 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 = fluid.data(name='age_id', shape=[-1], dtype="int64") usr_age_emb = fluid.embedding( input=usr_age_id, @@ -318,7 +318,7 @@ 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 = fluid.data(name='job_id', shape=[-1], dtype="int64") usr_job_emb = fluid.embedding( input=usr_job_id, @@ -349,7 +349,7 @@ 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 = fluid.data(name='movie_id', shape=[-1], dtype='int64') mov_emb = fluid.embedding( input=mov_id, @@ -363,7 +363,7 @@ 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) + name='category_id', shape=[-1], dtype='int64', lod_level=1) mov_categories_emb = fluid.embedding( input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -374,7 +374,7 @@ 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) + name='movie_title', shape=[-1], dtype='int64', lod_level=1) mov_title_emb = fluid.embedding( input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -420,7 +420,7 @@ def train_program(): scale_infer = inference_program() - label = fluid.data(name='score', shape=[None, 1], dtype='float32') + label = fluid.data(name='score', shape=[-1, 1], dtype='float32') square_cost = layers.square_error_cost(input=scale_infer, label=label) avg_cost = layers.mean(square_cost) diff --git a/05.recommender_system/train.py b/05.recommender_system/train.py index 457aa3eb1be666c032ac28e72d2ccf7ab584c9b2..5cf64acf0908c456420ef2d200af231a5de7f6ce 100644 --- a/05.recommender_system/train.py +++ b/05.recommender_system/train.py @@ -44,7 +44,7 @@ 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 = fluid.data(name='user_id', shape=[-1], dtype='int64') usr_emb = fluid.embedding( input=uid, @@ -57,7 +57,7 @@ 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 = fluid.data(name='gender_id', shape=[-1], dtype='int64') usr_gender_emb = fluid.embedding( input=usr_gender_id, @@ -68,7 +68,7 @@ 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 = fluid.data(name='age_id', shape=[-1], dtype="int64") usr_age_emb = fluid.embedding( input=usr_age_id, @@ -79,7 +79,7 @@ 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 = fluid.data(name='job_id', shape=[-1], dtype="int64") usr_job_emb = fluid.embedding( input=usr_job_id, @@ -101,7 +101,7 @@ 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 = fluid.data(name='movie_id', shape=[-1], dtype='int64') mov_emb = fluid.embedding( input=mov_id, @@ -115,7 +115,7 @@ 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) + name='category_id', shape=[-1], dtype='int64', lod_level=1) mov_categories_emb = fluid.embedding( input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -126,7 +126,7 @@ 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) + name='movie_title', shape=[-1], dtype='int64', lod_level=1) mov_title_emb = fluid.embedding( input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE) @@ -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 = fluid.data(name='score', shape=[-1, 1], dtype='float32') square_cost = layers.square_error_cost(input=scale_infer, label=label) avg_cost = layers.mean(square_cost)