def __init__(self, field_dims, embed_dim, mlp_layers=(400, 400, 400), dropouts=(0.5, 0.5)):
super(NeuralFactorizationMachineModel, self).__init__()
self.linear = FeaturesLinear(field_dims) # 线性部分
self.embedding = FeaturesEmbedding(field_dims, embed_dim) # 交互部分
self.fm = FactorizationMachine(embed_dim, reduce_sum=False)
self.mlp = MultiLayerPerceptron(embed_dim, mlp_layers, dropouts[1])
def __init__(self, field_dims, embed_dim, mlp_dims, dropout):
super().__init__()
self.linear = FeaturesLinear(field_dims)
self.fm = FactorizationMachine(reduce_sum=True)
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.embed_output_dim = len(field_dims) * embed_dim
self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout)
def __init__(self, field_dims, embed_dim, mlp_dims, dropouts):
super().__init__()
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.linear = FeaturesLinear(field_dims)
self.fm = torch.nn.Sequential(FactorizationMachine(reduce_sum=False),
torch.nn.BatchNorm1d(embed_dim),
torch.nn.Dropout(dropouts[0]))
self.mlp = MultiLayerPerceptron(embed_dim, mlp_dims, dropouts[1])
def __init__(self, field_dims, embed_dim, mlp_layers=(400,400,400),dropouts=(0.5, 0.5),type="glu"):
super(GFRLNFM, self).__init__()
self.linear = FeaturesLinear(field_dims) # 线性部分
self.embedding_gate = FeaturesEmbeddingWithGlobalIn(field_dims,embed_dim,type=type)
self.fm = FactorizationMachine(reduce_sum=False)
self.mlp = MultiLayerPerceptron(embed_dim, mlp_layers, dropouts[1])
def __init__(self, field_dims, embed_dim, type_c="hard"):
"""
:param field_dims: list, 每个field的
:param embed_dim:
"""
super(MyIFM, self).__init__()
self.fc = FeaturesLinear(field_dims)
# 特征
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.myFM = MyIFMLayer(field_dims, embed_dim, type_c=type_c)
self.fm = FactorizationMachine(embed_dim, reduce_sum=True)
def __init__(self,
field_dims,
embed_dim,
mlp_layers=(128, 64),
dropout=0.5,
field_len=10):
super(DeepFactorizationMachineModel, self).__init__()
self.linear = FeaturesLinear(field_dims)
self.fm = FactorizationMachine(embed_dim=embed_dim, reduce_sum=True)
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.embed_output_size = field_len * embed_dim
self.mlp = MultiLayerPerceptron(self.embed_output_size, mlp_layers,
dropout)
def __init__(self, field_dims, embed_dim, embed_dims, field_len=10):
"""
:param field_dims: list, 每个field的
:param embed_dim:
"""
super(IFM, self).__init__()
self.fc = FeaturesLinear(field_dims)
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.ifm = IFMLayer(field_dims,
embed_dim,
embed_dims=embed_dims,
field_len=field_len)
self.fm = FactorizationMachine(embed_dim, reduce_sum=True)
def __init__(self,
field_dims,
embed_dim,
mlp_layers=(128, 64),
dropout=0.5,
type="glu"):
super(GELDFM, self).__init__()
self.linear = FeaturesLinear(field_dims)
self.fm = FactorizationMachine(reduce_sum=True)
self.embedding = FeaturesEmbeddingWithGlobalIn(field_dims,
embed_dim,
type=type)
self.embed_output_size = len(field_dims) * embed_dim
self.mlp = MultiLayerPerceptron(self.embed_output_size, mlp_layers,
dropout)
def __init__(self, field_dims, embed_dim):
super().__init__()
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.linear = FeaturesLinear(field_dims)
self.fm = FactorizationMachine(reduce_sum = True)