self.embedding_dict2 = self.create_embedding_matrix(self.sparse_feature_columns, feat_sizes, self.embedding_size, device=self.device) # Deep self.dropout = nn.Dropout(self._config['dnn_dropout']) self.dnn_input_size = self.embedding_size * len(self.sparse_feature_columns) + len(self.den...
Sparse型特征,为离散型特征建立Input层接收输入,然后需要先通过embedding层转成低维稠密向量,然后拼接起来放着,等变长离散那边处理好之后, 一块拼起来进DNN, 但是这里面要注意有个特征的embedding向量还得拿出来用,就是候选商品的embedding向量,这个还得和后面的计算相关性,对历史行为序列加权。 VarlenSparse型特征:这...
好了,那我们就可以把不同field的one-hot vectors先embedding成1维,和dense vectors对应。 # 这里代码只是示意,方便理解,具体可实施的请看参考3sparse_embedding_dict=nn.ModuleDict(feat:nn.Emebedding(feat_sizes[feat],1,sparse=False)forfeat in sparse_features)sparse_embd_lists=[sparse_embedding_dict[feat]...
LongTensor(train[sparse_features].values), torch.FloatTensor(train[dense_features].values), torch.FloatTensor(train['label'].values),) train_loader = Data.DataLoader(dataset=train_dataset, batch_size=2048, shuffle=True) valid_dataset = Data.TensorDataset(torch.LongTensor(valid[sparse_features]....
from torch_rechub.basic.features import DenseFeature, SparseFeature, SequenceFeature from torch_rechub.utils.match import generate_seq_feature_match, gen_model_input from torch_rechub.utils.data import df_to_dict, MatchDataGenerator # from movielens_utils import match_evaluation ...
导致索引存在间隙。 b、间隙索引在创建时应指定选项:{ sparse: true } c、间隙索引列上可以...
这一步是解析特征非常关键的一步,这里需要以json的格式对每一列特征进行配置,主要需要明确特征的数据类型(dtype):int,float还是string等类型,特征名称(key),特征类型(type):sparse特征或者dense特征。 这里我们需要保证特征配置的数量和特征的列数一致。我们在conf目录下提供了一份针对点击率预估数据的样例配置文件。
we want the gradient generated by the embedding to be sparse.Our philosophy on extensions also has...
self.embedding = nn.Embedding(10000,20,sparse=True) Here is a full reproducer: this only triggers the error on GPUs, on CPUs it works fine. optim.SGD is used in this example and internally in the step() function it uses .detach call in the following block of code: buf = param_state...