def MIND(user_feature_columns, item_feature_columns, num_sampled=5, k_max=2, p=1.0, dynamic_k=False, user_dnn_hidden_units=(64, 32), dnn_activation='relu', dnn_use_bn=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, init_std=0.0001, seed=1024): """Instantiates the MIND Model architecture. :param user_feature_columns: An iterable containing user's features used by the model. :param item_feature_columns: An iterable containing item's features used by the model. :param num_sampled: int, the number of classes to randomly sample per batch. :param k_max: int, the max size of user interest embedding :param p: float,the parameter for adjusting the attention distribution in LabelAwareAttention. :param dynamic_k: bool, whether or not use dynamic interest number :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net :param user_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of user tower :param dnn_activation: Activation function to use in deep net :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net :param l2_reg_dnn: L2 regularizer strength applied to DNN :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param init_std: float,to use as the initialize std of embedding vector :param seed: integer ,to use as random seed. :return: A Keras model instance. """ if len(item_feature_columns) > 1: raise ValueError("Now MIND only support 1 item feature like item_id") item_feature_column = item_feature_columns[0] item_feature_name = item_feature_column.name item_vocabulary_size = item_feature_columns[0].vocabulary_size item_embedding_dim = item_feature_columns[0].embedding_dim # item_index = Input(tensor=tf.constant([list(range(item_vocabulary_size))])) history_feature_list = [item_feature_name] features = build_input_features(user_feature_columns) sparse_feature_columns = list( filter(lambda x: isinstance(x, SparseFeat), user_feature_columns)) if user_feature_columns else [] dense_feature_columns = list( filter(lambda x: isinstance(x, DenseFeat), user_feature_columns)) if user_feature_columns else [] varlen_sparse_feature_columns = list( filter(lambda x: isinstance(x, VarLenSparseFeat), user_feature_columns)) if user_feature_columns else [] history_feature_columns = [] sparse_varlen_feature_columns = [] history_fc_names = list(map(lambda x: "hist_" + x, history_feature_list)) for fc in varlen_sparse_feature_columns: feature_name = fc.name if feature_name in history_fc_names: history_feature_columns.append(fc) else: sparse_varlen_feature_columns.append(fc) seq_max_len = history_feature_columns[0].maxlen inputs_list = list(features.values()) embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding, init_std, seed, prefix="") item_features = build_input_features(item_feature_columns) query_emb_list = embedding_lookup(embedding_matrix_dict, item_features, item_feature_columns, history_feature_list, history_feature_list, to_list=True) keys_emb_list = embedding_lookup(embedding_matrix_dict, features, history_feature_columns, history_fc_names, history_fc_names, to_list=True) dnn_input_emb_list = embedding_lookup(embedding_matrix_dict, features, sparse_feature_columns, mask_feat_list=history_feature_list, to_list=True) dense_value_list = get_dense_input(features, dense_feature_columns) sequence_embed_dict = varlen_embedding_lookup( embedding_matrix_dict, features, sparse_varlen_feature_columns) sequence_embed_list = get_varlen_pooling_list( sequence_embed_dict, features, sparse_varlen_feature_columns, to_list=True) dnn_input_emb_list += sequence_embed_list # keys_emb = concat_func(keys_emb_list, mask=True) # query_emb = concat_func(query_emb_list, mask=True) history_emb = PoolingLayer()(NoMask()(keys_emb_list)) target_emb = PoolingLayer()(NoMask()(query_emb_list)) # target_emb_size = target_emb.get_shape()[-1].value # max_len = history_emb.get_shape()[1].value hist_len = features['hist_len'] high_capsule = CapsuleLayer(input_units=item_embedding_dim, out_units=item_embedding_dim, max_len=seq_max_len, k_max=k_max)((history_emb, hist_len)) if len(dnn_input_emb_list) > 0 or len(dense_value_list) > 0: user_other_feature = combined_dnn_input(dnn_input_emb_list, dense_value_list) other_feature_tile = tf.keras.layers.Lambda( tile_user_otherfeat, arguments={'k_max': k_max})(user_other_feature) user_deep_input = Concatenate()( [NoMask()(other_feature_tile), high_capsule]) else: user_deep_input = high_capsule user_embeddings = DNN(user_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed, name="user_embedding")(user_deep_input) item_inputs_list = list(item_features.values()) item_embedding_matrix = embedding_matrix_dict[item_feature_name] item_index = EmbeddingIndex(list(range(item_vocabulary_size)))( item_features[item_feature_name]) item_embedding_weight = NoMask()(item_embedding_matrix(item_index)) pooling_item_embedding_weight = PoolingLayer()([item_embedding_weight]) if dynamic_k: user_embedding_final = LabelAwareAttention( k_max=k_max, pow_p=p, )((user_embeddings, target_emb, hist_len)) else: user_embedding_final = LabelAwareAttention( k_max=k_max, pow_p=p, )((user_embeddings, target_emb)) output = SampledSoftmaxLayer(num_sampled=num_sampled)([ pooling_item_embedding_weight, user_embedding_final, item_features[item_feature_name] ]) model = Model(inputs=inputs_list + item_inputs_list, outputs=output) model.__setattr__("user_input", inputs_list) model.__setattr__("user_embedding", user_embeddings) model.__setattr__("item_input", item_inputs_list) model.__setattr__( "item_embedding", get_item_embedding(pooling_item_embedding_weight, item_features[item_feature_name])) return model
def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, init_std=0.0001, seed=1024, metric='cos'): """Instantiates the FM architecture. :param user_feature_columns: An iterable containing user's features used by the model. :param item_feature_columns: An iterable containing item's features used by the model. :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param init_std: float,to use as the initialize std of embedding vector :param seed: integer ,to use as random seed. :param metric: str, ``"cos"`` for cosine or ``"ip"`` for inner product :return: A Keras model instance. """ embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding, init_std, seed, seq_mask_zero=True) user_features = build_input_features(user_feature_columns) user_inputs_list = list(user_features.values()) user_sparse_embedding_list, user_dense_value_list = input_from_feature_columns( user_features, user_feature_columns, l2_reg_embedding, init_std, seed, support_dense=False, embedding_matrix_dict=embedding_matrix_dict) item_features = build_input_features(item_feature_columns) item_inputs_list = list(item_features.values()) item_sparse_embedding_list, item_dense_value_list = input_from_feature_columns( item_features, item_feature_columns, l2_reg_embedding, init_std, seed, support_dense=False, embedding_matrix_dict=embedding_matrix_dict) user_dnn_input = concat_func(user_sparse_embedding_list, axis=1) user_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))( user_dnn_input) item_dnn_input = concat_func(item_sparse_embedding_list, axis=1) item_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))( item_dnn_input) score = Similarity(type=metric)([user_vector_sum, item_vector_sum]) output = PredictionLayer("binary", False)(score) model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output) model.__setattr__("user_input", user_inputs_list) model.__setattr__("user_embedding", user_vector_sum) model.__setattr__("item_input", item_inputs_list) model.__setattr__("item_embedding", item_vector_sum) return model
def DSSM(user_feature_columns, item_feature_columns, user_dnn_hidden_units=(64, 32), item_dnn_hidden_units=(64, 32), dnn_activation='tanh', dnn_use_bn=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, init_std=0.0001, seed=1024, metric='cos'): """Instantiates the Deep Structured Semantic Model architecture. :param user_feature_columns: An iterable containing user's features used by the model. :param item_feature_columns: An iterable containing item's features used by the model. :param user_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of user tower :param item_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of item tower :param dnn_activation: Activation function to use in deep net :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param init_std: float,to use as the initialize std of embedding vector :param seed: integer ,to use as random seed. :param metric: str, ``"cos"`` for cosine or ``"ip"`` for inner product :return: A Keras model instance. """ embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding, init_std, seed, seq_mask_zero=True) user_features = build_input_features(user_feature_columns) user_inputs_list = list(user_features.values()) user_sparse_embedding_list, user_dense_value_list = input_from_feature_columns( user_features, user_feature_columns, l2_reg_embedding, init_std, seed, embedding_matrix_dict=embedding_matrix_dict) user_dnn_input = combined_dnn_input(user_sparse_embedding_list, user_dense_value_list) item_features = build_input_features(item_feature_columns) item_inputs_list = list(item_features.values()) item_sparse_embedding_list, item_dense_value_list = input_from_feature_columns( item_features, item_feature_columns, l2_reg_embedding, init_std, seed, embedding_matrix_dict=embedding_matrix_dict) item_dnn_input = combined_dnn_input(item_sparse_embedding_list, item_dense_value_list) user_dnn_out = DNN( user_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed, )(user_dnn_input) item_dnn_out = DNN(item_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed)(item_dnn_input) score = Similarity(type=metric)([user_dnn_out, item_dnn_out]) output = PredictionLayer("binary", False)(score) model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output) model.__setattr__("user_input", user_inputs_list) model.__setattr__("item_input", item_inputs_list) model.__setattr__("user_embedding", user_dnn_out) model.__setattr__("item_embedding", item_dnn_out) return model
def NCF(user_feature_columns, item_feature_columns, user_gmf_embedding_dim=20, item_gmf_embedding_dim=20, user_mlp_embedding_dim=20, item_mlp_embedding_dim=20, dnn_use_bn=False, dnn_hidden_units=(64, 32), dnn_activation='relu', l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, init_std=0.0001, seed=1024): """Instantiates the NCF Model architecture. :param user_feature_columns: A dict containing user's features and features'dim. :param item_feature_columns: A dict containing item's features and features'dim. :param user_gmf_embedding_dim: int. :param item_gmf_embedding_dim: int. :param user_mlp_embedding_dim: int. :param item_mlp_embedding_dim: int. :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net :param dnn_activation: Activation function to use in deep net :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param init_std: float,to use as the initialize std of embedding vector :param seed: integer ,to use as random seed. :return: A Keras model instance. """ user_dim = len(user_feature_columns) * user_gmf_embedding_dim item_dim = len(item_feature_columns) * item_gmf_embedding_dim dim = (user_dim * item_dim) / (math.gcd(user_dim, item_dim)) user_gmf_embedding_dim = int(dim / len(user_feature_columns)) item_gmf_embedding_dim = int(dim / len(item_feature_columns)) # Generalized Matrix Factorization (GMF) Part user_gmf_feature_columns = [ SparseFeat(feat, vocabulary_size=size, embedding_dim=user_gmf_embedding_dim) for feat, size in user_feature_columns.items() ] user_features = build_input_features(user_gmf_feature_columns) user_inputs_list = list(user_features.values()) user_gmf_sparse_embedding_list, user_gmf_dense_value_list = input_from_feature_columns( user_features, user_gmf_feature_columns, l2_reg_embedding, init_std, seed, prefix='gmf_') user_gmf_input = combined_dnn_input(user_gmf_sparse_embedding_list, []) user_gmf_out = Lambda(lambda x: x, name="user_gmf_embedding")(user_gmf_input) item_gmf_feature_columns = [ SparseFeat(feat, vocabulary_size=size, embedding_dim=item_gmf_embedding_dim) for feat, size in item_feature_columns.items() ] item_features = build_input_features(item_gmf_feature_columns) item_inputs_list = list(item_features.values()) item_gmf_sparse_embedding_list, item_gmf_dense_value_list = input_from_feature_columns( item_features, item_gmf_feature_columns, l2_reg_embedding, init_std, seed, prefix='gmf_') item_gmf_input = combined_dnn_input(item_gmf_sparse_embedding_list, []) item_gmf_out = Lambda(lambda x: x, name="item_gmf_embedding")(item_gmf_input) gmf_out = Multiply()([user_gmf_out, item_gmf_out]) # Multi-Layer Perceptron (MLP) Part user_mlp_feature_columns = [ SparseFeat(feat, vocabulary_size=size, embedding_dim=user_mlp_embedding_dim) for feat, size in user_feature_columns.items() ] user_mlp_sparse_embedding_list, user_mlp_dense_value_list = input_from_feature_columns( user_features, user_mlp_feature_columns, l2_reg_embedding, init_std, seed, prefix='mlp_') user_mlp_input = combined_dnn_input(user_mlp_sparse_embedding_list, user_mlp_dense_value_list) user_mlp_out = Lambda(lambda x: x, name="user_mlp_embedding")(user_mlp_input) item_mlp_feature_columns = [ SparseFeat(feat, vocabulary_size=size, embedding_dim=item_mlp_embedding_dim) for feat, size in item_feature_columns.items() ] item_mlp_sparse_embedding_list, item_mlp_dense_value_list = input_from_feature_columns( item_features, item_mlp_feature_columns, l2_reg_embedding, init_std, seed, prefix='mlp_') item_mlp_input = combined_dnn_input(item_mlp_sparse_embedding_list, item_mlp_dense_value_list) item_mlp_out = Lambda(lambda x: x, name="item_mlp_embedding")(item_mlp_input) mlp_input = Concatenate(axis=1)([user_mlp_out, item_mlp_out]) mlp_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed, name="mlp_embedding")(mlp_input) # Fusion of GMF and MLP neumf_input = Concatenate(axis=1)([gmf_out, mlp_out]) neumf_out = DNN(hidden_units=[1], activation='sigmoid')(neumf_input) output = Lambda(lambda x: x, name='neumf_out')(neumf_out) # output = PredictionLayer(task, False)(neumf_out) model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output) return model
def SDM(user_feature_columns, item_feature_columns, history_feature_list, num_sampled=5, units=64, rnn_layers=2, dropout_rate=0.2, rnn_num_res=1, num_head=4, l2_reg_embedding=1e-6, dnn_activation='tanh', init_std=0.0001, seed=1024): """Instantiates the Sequential Deep Matching Model architecture. :param user_feature_columns: An iterable containing user's features used by the model. :param item_feature_columns: An iterable containing item's features used by the model. :param history_feature_list: list,to indicate short and prefer sequence sparse field :param num_sampled: int, the number of classes to randomly sample per batch. :param units: int, dimension for each output layer :param rnn_layers: int, layer number of rnn :param dropout_rate: float in [0,1), the probability we will drop out a given DNN coordinate. :param rnn_num_res: int. The number of residual layers in rnn layers :param num_head: int int, the number of attention head :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param dnn_activation: Activation function to use in deep net :param init_std: float,to use as the initialize std of embedding vector :param seed: integer ,to use as random seed. :return: A Keras model instance. """ if len(item_feature_columns) > 1: raise ValueError("Now MIND only support 1 item feature like item_id") item_feature_column = item_feature_columns[0] item_feature_name = item_feature_column.name item_vocabulary_size = item_feature_columns[0].vocabulary_size features = build_input_features(user_feature_columns) user_inputs_list = list(features.values()) sparse_feature_columns = list( filter(lambda x: isinstance(x, SparseFeat), user_feature_columns)) if user_feature_columns else [] dense_feature_columns = list( filter(lambda x: isinstance(x, DenseFeat), user_feature_columns)) if user_feature_columns else [] if len(dense_feature_columns) != 0: raise ValueError("Now SDM don't support dense feature") varlen_sparse_feature_columns = list( filter(lambda x: isinstance(x, VarLenSparseFeat), user_feature_columns)) if user_feature_columns else [] sparse_varlen_feature_columns = [] prefer_history_columns = [] short_history_columns = [] prefer_fc_names = list(map(lambda x: "prefer_" + x, history_feature_list)) short_fc_names = list(map(lambda x: "short_" + x, history_feature_list)) for fc in varlen_sparse_feature_columns: feature_name = fc.name if feature_name in prefer_fc_names: prefer_history_columns.append(fc) elif feature_name in short_fc_names: short_history_columns.append(fc) else: sparse_varlen_feature_columns.append(fc) embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding, init_std, seed, prefix="") item_features = build_input_features(item_feature_columns) item_inputs_list = list(item_features.values()) prefer_emb_list = embedding_lookup(embedding_matrix_dict, features, prefer_history_columns, prefer_fc_names, prefer_fc_names, to_list=True) # L^u short_emb_list = embedding_lookup(embedding_matrix_dict, features, short_history_columns, short_fc_names, short_fc_names, to_list=True) # S^u # dense_value_list = get_dense_input(features, dense_feature_columns) user_emb_list = embedding_lookup(embedding_matrix_dict, features, sparse_feature_columns, to_list=True) sequence_embed_dict = varlen_embedding_lookup( embedding_matrix_dict, features, sparse_varlen_feature_columns) sequence_embed_list = get_varlen_pooling_list( sequence_embed_dict, features, sparse_varlen_feature_columns, to_list=True) user_emb_list += sequence_embed_list # e^u # if len(user_emb_list) > 0 or len(dense_value_list) > 0: # user_emb_feature = combined_dnn_input(user_emb_list, dense_value_list) user_emb = concat_func(user_emb_list) user_emb_output = Dense(units, activation=dnn_activation, name="user_emb_output")(user_emb) prefer_sess_length = features['prefer_sess_length'] prefer_att_outputs = [] for i, prefer_emb in enumerate(prefer_emb_list): prefer_attention_output = AttentionSequencePoolingLayer( dropout_rate=0)([user_emb_output, prefer_emb, prefer_sess_length]) prefer_att_outputs.append(prefer_attention_output) prefer_att_concat = concat_func(prefer_att_outputs) prefer_output = Dense(units, activation=dnn_activation, name="prefer_output")(prefer_att_concat) short_sess_length = features['short_sess_length'] short_emb_concat = concat_func(short_emb_list) short_emb_input = Dense(units, activation=dnn_activation, name="short_emb_input")(short_emb_concat) short_rnn_output = DynamicMultiRNN( num_units=units, return_sequence=True, num_layers=rnn_layers, num_residual_layers=rnn_num_res, dropout_rate=dropout_rate)([short_emb_input, short_sess_length]) short_att_output = SelfMultiHeadAttention( num_units=units, head_num=num_head, dropout_rate=dropout_rate, future_binding=True, use_layer_norm=True)([short_rnn_output, short_sess_length ]) # [batch_size, time, num_units] short_output = UserAttention(num_units=units, activation=dnn_activation, use_res=True, dropout_rate=dropout_rate) \ ([user_emb_output, short_att_output, short_sess_length]) gate_input = concat_func([prefer_output, short_output, user_emb_output]) gate = Dense(units, activation='sigmoid')(gate_input) gate_output = Lambda( lambda x: tf.multiply(x[0], x[1]) + tf.multiply(1 - x[0], x[2]))( [gate, short_output, prefer_output]) gate_output_reshape = Lambda(lambda x: tf.squeeze(x, 1))(gate_output) item_index = EmbeddingIndex(list(range(item_vocabulary_size)))( item_features[item_feature_name]) item_embedding_matrix = embedding_matrix_dict[item_feature_name] item_embedding_weight = NoMask()(item_embedding_matrix(item_index)) pooling_item_embedding_weight = PoolingLayer()([item_embedding_weight]) output = SampledSoftmaxLayer(num_sampled=num_sampled)([ pooling_item_embedding_weight, gate_output_reshape, item_features[item_feature_name] ]) model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output) model.__setattr__("user_input", user_inputs_list) model.__setattr__("user_embedding", gate_output_reshape) model.__setattr__("item_input", item_inputs_list) model.__setattr__( "item_embedding", get_item_embedding(pooling_item_embedding_weight, item_features[item_feature_name])) return model
def YoutubeDNN( user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, 32), dnn_activation='relu', dnn_use_bn=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, init_std=0.0001, seed=1024, ): """Instantiates the YoutubeDNN Model architecture. :param user_feature_columns: An iterable containing user's features used by the model. :param item_feature_columns: An iterable containing item's features used by the model. :param num_sampled: int, the number of classes to randomly sample per batch. :param user_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of user tower :param dnn_activation: Activation function to use in deep net :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param init_std: float,to use as the initialize std of embedding vector :param seed: integer ,to use as random seed. :return: A Keras model instance. """ if len(item_feature_columns) > 1: raise ValueError( "Now YoutubeNN only support 1 item feature like item_id") item_feature_name = item_feature_columns[0].name item_vocabulary_size = item_feature_columns[0].vocabulary_size #构建User和 item 的 Embedding dict embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding, init_std, seed, prefix="") #构建 User Input dict user_features = build_input_features(user_feature_columns) user_inputs_list = list(user_features.values()) user_sparse_embedding_list, user_dense_value_list = input_from_feature_columns( user_features, user_feature_columns, l2_reg_embedding, init_std, seed, embedding_matrix_dict=embedding_matrix_dict) user_dnn_input = combined_dnn_input(user_sparse_embedding_list, user_dense_value_list) # 构建 item Input dict item_features = build_input_features(item_feature_columns) item_inputs_list = list(item_features.values()) user_dnn_out = DNN( user_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed, )(user_dnn_input) print(item_features[item_feature_name]) print(list(range(item_vocabulary_size))) item_index = EmbeddingIndex(list(range(item_vocabulary_size)))( item_features[item_feature_name]) item_embedding_matrix = embedding_matrix_dict[item_feature_name] print(item_embedding_matrix(item_index)) item_embedding_weight = NoMask()(item_embedding_matrix(item_index)) pooling_item_embedding_weight = PoolingLayer()([item_embedding_weight]) # tf.nn.sampled_softmax_loss,在类别很多的情况下训练softmax分类器的高效的方法。 # 注意:仅在训练时使用这一采样操作,测试时还是用全部的类别 output = SampledSoftmaxLayer(num_sampled=num_sampled)([ pooling_item_embedding_weight, user_dnn_out, item_features[item_feature_name] ]) model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output) model.__setattr__("user_input", user_inputs_list) model.__setattr__("user_embedding", user_dnn_out) model.__setattr__("item_input", item_inputs_list) model.__setattr__( "item_embedding", get_item_embedding(pooling_item_embedding_weight, item_features[item_feature_name])) return model