def __init__(self, config, dataset):
super(FM, self).__init__(config, dataset)
# define layers and loss
self.fm = BaseFactorizationMachine(reduce_sum=True)
self.sigmoid = nn.Sigmoid()
self.loss = nn.BCELoss()
# parameters initialization
self.apply(self._init_weights)
def __init__(self, config, dataset):
super(NFM, self).__init__(config, dataset)
# load parameters info
self.mlp_hidden_size = config['mlp_hidden_size']
self.dropout_prob = config['dropout_prob']
# define layers and loss
size_list = [self.embedding_size] + self.mlp_hidden_size
self.fm = BaseFactorizationMachine(reduce_sum=False)
self.bn = nn.BatchNorm1d(num_features=self.embedding_size)
self.mlp_layers = MLPLayers(size_list, self.dropout_prob, activation='sigmoid', bn=True)
self.predict_layer = nn.Linear(self.mlp_hidden_size[-1], 1, bias=False)
self.sigmoid = nn.Sigmoid()
self.loss = nn.BCELoss()
# parameters initialization
self.apply(self._init_weights)
def __init__(self, config, dataset):
super(DeepFM, self).__init__(config, dataset)
# load parameters info
self.mlp_hidden_size = config['mlp_hidden_size']
self.dropout_prob = config['dropout_prob']
# define layers and loss
self.fm = BaseFactorizationMachine(reduce_sum=True)
size_list = [self.embedding_size * self.num_feature_field
] + self.mlp_hidden_size
self.mlp_layers = MLPLayers(size_list, self.dropout_prob)
self.deep_predict_layer = nn.Linear(
self.mlp_hidden_size[-1], 1) # Linear product to the final score
self.sigmoid = nn.Sigmoid()
self.loss = nn.BCELoss()
# parameters initialization
self.apply(self._init_weights)