def __init__(
self,
mask_mat,
input_size,
rnn_hidden_size,
att_hidden_size=(80, 40),
activation='sigmoid',
softmax_stag=False,
gru='GRU'
):
super(InterestEvolvingLayer, self).__init__()
self.mask_mat = mask_mat
self.gru = gru
if gru == 'GRU':
self.attention_layer = SequenceAttLayer(mask_mat, att_hidden_size, activation, softmax_stag, False)
self.dynamic_rnn = nn.GRU(input_size=input_size, hidden_size=rnn_hidden_size, batch_first=True)
elif gru == 'AIGRU':
self.attention_layer = SequenceAttLayer(mask_mat, att_hidden_size, activation, softmax_stag, True)
self.dynamic_rnn = nn.GRU(input_size=input_size, hidden_size=rnn_hidden_size, batch_first=True)
elif gru == 'AGRU' or gru == 'AUGRU':
self.attention_layer = SequenceAttLayer(mask_mat, att_hidden_size, activation, softmax_stag, True)
self.dynamic_rnn = DynamicRNN(input_size=input_size, hidden_size=rnn_hidden_size, gru=gru)
def __init__(self, config, dataset):
super(DIN, self).__init__(config, dataset)
# get field names and parameter value from config
self.LABEL_FIELD = config['LABEL_FIELD']
self.embedding_size = config['embedding_size']
self.mlp_hidden_size = config['mlp_hidden_size']
self.device = config['device']
self.pooling_mode = config['pooling_mode']
self.dropout_prob = config['dropout_prob']
self.types = ['user', 'item']
self.user_feat = dataset.get_user_feature()
self.item_feat = dataset.get_item_feature()
# init MLP layers
# self.dnn_list = [(3 * self.num_feature_field['item'] + self.num_feature_field['user'])
# * self.embedding_size] + self.mlp_hidden_size
num_item_feature = len(self.item_feat.interaction.keys())
self.dnn_list = [3 * num_item_feature * self.embedding_size] + self.mlp_hidden_size
self.att_list = [4 * num_item_feature * self.embedding_size] + self.mlp_hidden_size
mask_mat = torch.arange(self.max_seq_length).to(self.device).view(1, -1) # init mask
self.attention = SequenceAttLayer(
mask_mat, self.att_list, activation='Sigmoid', softmax_stag=False, return_seq_weight=False
)
self.dnn_mlp_layers = MLPLayers(self.dnn_list, activation='Dice', dropout=self.dropout_prob, bn=True)
self.embedding_layer = ContextSeqEmbLayer(dataset, self.embedding_size, self.pooling_mode, self.device)
self.dnn_predict_layers = nn.Linear(self.mlp_hidden_size[-1], 1)
self.sigmoid = nn.Sigmoid()
self.loss = nn.BCELoss()
self.apply(self._init_weights)