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99 lines
3.2 KiB
99 lines
3.2 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.trainer_config_helpers import *
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try:
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import cPickle as pickle
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except ImportError:
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import pickle
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is_predict = get_config_arg('is_predict', bool, False)
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META_FILE = 'data/meta.bin'
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with open(META_FILE, 'rb') as f:
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# load meta file
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meta = pickle.load(f)
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settings(
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batch_size=1600, learning_rate=1e-3, learning_method=RMSPropOptimizer())
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def construct_feature(name):
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"""
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Construct movie/user features.
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This method read from meta data. Then convert feature to neural network due
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to feature type. The map relation as follow.
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* id: embedding => fc
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* embedding:
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is_sequence: embedding => context_projection => fc => pool
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not sequence: embedding => fc
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* one_hot_dense: fc => fc
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Then gather all features vector, and use a fc layer to combined them as
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return.
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:param name: 'movie' or 'user'
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:type name: basestring
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:return: combined feature output
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:rtype: LayerOutput
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"""
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__meta__ = meta[name]['__meta__']['raw_meta']
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fusion = []
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for each_meta in __meta__:
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type_name = each_meta['type']
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slot_name = each_meta.get('name', '%s_id' % name)
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if type_name == 'id':
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slot_dim = each_meta['max']
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embedding = embedding_layer(
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input=data_layer(
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slot_name, size=slot_dim), size=256)
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fusion.append(fc_layer(input=embedding, size=256))
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elif type_name == 'embedding':
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is_seq = each_meta['seq'] == 'sequence'
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slot_dim = len(each_meta['dict'])
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din = data_layer(slot_name, slot_dim)
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embedding = embedding_layer(input=din, size=256)
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if is_seq:
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fusion.append(
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text_conv_pool(
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input=embedding, context_len=5, hidden_size=256))
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else:
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fusion.append(fc_layer(input=embedding, size=256))
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elif type_name == 'one_hot_dense':
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slot_dim = len(each_meta['dict'])
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hidden = fc_layer(input=data_layer(slot_name, slot_dim), size=256)
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fusion.append(fc_layer(input=hidden, size=256))
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return fc_layer(name="%s_fusion" % name, input=fusion, size=256)
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movie_feature = construct_feature("movie")
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user_feature = construct_feature("user")
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similarity = cos_sim(a=movie_feature, b=user_feature)
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if not is_predict:
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outputs(mse_cost(input=similarity, label=data_layer('rating', size=1)))
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define_py_data_sources2(
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'data/train.list',
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'data/test.list',
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module='dataprovider',
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obj='process',
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args={'meta': meta})
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else:
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outputs(similarity)
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