RuyiLuo
72 lines
2.8 KiB
Python
72 lines
2.8 KiB
Python
from tensorflow.keras.models import Model
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from tensorflow.keras.layers import Flatten, Concatenate
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from features import FeatureEncoder
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from layers import DNN, CosinSimilarity, PredictLayer
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def process_feature(user_feature_columns, item_feature_columns, feature_encode):
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"""
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根据FeatureEncoder获取所有输入的Input层或者Embedding层，然后根据自己
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实际场景的业务数据，对不同的特征进行处理.
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user_feature_columns = [
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SparseFeat('user_id', feature_max_index_dict['user_id'], embedding_dim=4),
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SparseFeat('gender', feature_max_index_dict['gender'], embedding_dim=4),
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SparseFeat('occupation', feature_max_index_dict['occupation'], embedding_dim=4),
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SparseFeat('zip', feature_max_index_dict['zip'], embedding_dim=4),
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VarLenSparseFeat(SparseFeat('hist_movie_id', feature_max_idx['movie_id'], embedding_dim,
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embedding_name="movie_id"), SEQ_LEN, 'mean', 'hist_len'),
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]
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item_feature_columns = [SparseFeat('movie_id', feature_max_index_dict['movie_id'], embedding_dim=4)]
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"""
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group_embedding_dict = feature_encode.sparse_feature_dict
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user_emb_name = [fc.embedding_name for fc in user_feature_columns]
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item_emb_name = [fc.embedding_name for fc in item_feature_columns]
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user_dnn_input = [v for k, v in group_embedding_dict['default_group'].items()
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if k in user_emb_name]
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item_dnn_input = [v for k, v in group_embedding_dict['default_group'].items()
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if k in item_emb_name]
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return user_dnn_input, item_dnn_input
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def DSSM(user_feature_columns, item_feature_columns, dnn_units=[64, 32],
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temp=10, task='binary'):
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# 构建所有特征的Input层和Embedding层
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feature_encode = FeatureEncoder(user_feature_columns + item_feature_columns)
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feature_input_layers_list = list(feature_encode.feature_input_layer_dict.values())
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# 特征处理
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user_dnn_input, item_dnn_input = process_feature(user_feature_columns,\
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item_feature_columns, feature_encode)
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# 构建模型的核心层
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if len(user_dnn_input) >= 2:
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user_dnn_input = Concatenate(axis=1)(user_dnn_input)
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else:
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user_dnn_input = user_dnn_input[0]
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if len(item_dnn_input) >= 2:
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item_dnn_input = Concatenate(axis=1)(item_dnn_input)
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else:
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item_dnn_input = item_dnn_input[0]
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user_dnn_input = Flatten()(user_dnn_input)
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item_dnn_input = Flatten()(item_dnn_input)
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user_dnn_out = DNN(dnn_units)(user_dnn_input)
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item_dnn_out = DNN(dnn_units)(item_dnn_input)
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# 计算相似度
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scores = CosinSimilarity(temp)([user_dnn_out, item_dnn_out]) # (B,1)
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# 确定拟合目标
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output = PredictLayer()(scores)
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# 根据输入输出构建模型
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model = Model(feature_input_layers_list, output)
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# model.summary()
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return model
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