simplity network configure

recommender_system/trainer_config.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+
+is_predict = get_config_arg('is_predict', bool, False)
+
+META_FILE = 'data/meta.bin'
+
+with open(META_FILE, 'rb') as f:
+    # load meta file
+    meta = pickle.load(f)
+
+settings(
+    batch_size=1600, learning_rate=1e-3, learning_method=RMSPropOptimizer())
+
+movie_meta = meta['movie']['__meta__']['raw_meta']
+user_meta = meta['user']['__meta__']['raw_meta']
+
+movie_id = data_layer('movie_id', size=movie_meta[0]['max'])
+title = data_layer('title', size=len(movie_meta[1]['dict']))
+genres = data_layer('genres', size=len(movie_meta[2]['dict']))
+user_id = data_layer('user_id', size=user_meta[0]['max'])
+gender = data_layer('gender', size=len(user_meta[1]['dict']))
+age = data_layer('age', size=len(user_meta[2]['dict']))
+occupation = data_layer('occupation', size=len(user_meta[3]['dict']))
+
+embsize = 256
+
+# construct movie feature
+movie_id_emb = embedding_layer(input=movie_id, size=embsize)
+
+title_emb = embedding_layer(input=title, size=embsize)
+title_hidden = text_conv_pool(
+    input=title_emb, context_len=5, hidden_size=embsize)
+
+genres_emb = fc_layer(input=genres, size=embsize)
+
+movie_feature = fc_layer(
+    input=[movie_id_emb, title_hidden, genres_emb], size=embsize)
+
+# construct user feature
+user_id_emb = embedding_layer(input=user_id, size=embsize)
+
+gender_emb = embedding_layer(input=gender, size=embsize)
+gender_hidden = fc_layer(input=gender_emb, size=embsize)
+
+age_emb = embedding_layer(input=age, size=embsize)
+age_hidden = fc_layer(input=age_emb, size=embsize)
+
+occup_emb = embedding_layer(input=occupation, size=embsize)
+occup_hidden = fc_layer(input=occup_emb, size=embsize)
+
+user_feature = fc_layer(
+    input=[user_id_emb, gender_hidden, age_hidden, occup_hidden], size=embsize)
+
+similarity = cos_sim(a=movie_feature, b=user_feature)
+
+if not is_predict:
+    outputs(
+        regression_cost(
+            input=similarity, label=data_layer(
+                'rating', size=1)))
+
+    define_py_data_sources2(
+        'data/train.list',
+        'data/test.list',
+        module='dataprovider',
+        obj='process',
+        args={'meta': meta})
+else:
+    outputs(similarity)