def __init__(self, opt):
super(DeepFM, self).__init__(opt)
self.embed_output_dim = len(self.field_dims) * self.latent_dim
self.mlp_dims = opt['mlp_dims']
self.mlp = MultiLayerPerceptron(self.embed_output_dim,
self.mlp_dims,
dropout=0.2)
def __init__(self,
field_dims,
embed_dim,
atten_embed_dim,
num_heads,
num_layers,
mlp_dims,
dropouts,
has_residual=True):
super().__init__()
self.num_fields = len(field_dims)
self.linear = FeaturesLinear(field_dims)
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.atten_embedding = torch.nn.Linear(embed_dim, atten_embed_dim)
self.embed_output_dim = len(field_dims) * embed_dim
self.atten_output_dim = len(field_dims) * atten_embed_dim
self.has_residual = has_residual
self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims,
dropouts[1])
self.self_attns = torch.nn.ModuleList([
torch.nn.MultiheadAttention(atten_embed_dim,
num_heads,
dropout=dropouts[0])
for _ in range(num_layers)
])
self.attn_fc = torch.nn.Linear(self.atten_output_dim, 1)
if self.has_residual:
self.V_res_embedding = torch.nn.Linear(embed_dim, atten_embed_dim)
def __init__(self, field_dims, embed_dim, num_layers, mlp_dims, dropout):
super().__init__()
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.embed_output_dim = len(field_dims) * embed_dim
self.cn = CrossNetwork(self.embed_output_dim, num_layers)
self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout, output_layer=False)
self.linear = torch.nn.Linear(mlp_dims[-1] + self.embed_output_dim, 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, dropout):
super().__init__()
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, user_field_idx, item_field_idx, embed_dim, mlp_dims, dropout):
super().__init__()
self.user_field_idx = user_field_idx
self.item_field_idx = item_field_idx
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, output_layer=False)
self.fc = torch.nn.Linear(mlp_dims[-1] + embed_dim, 1)
def __init__(self, field_dims, embed_dim, LNN_dim, mlp_dims, dropouts):
super().__init__()
self.num_fields = len(field_dims)
self.linear = FeaturesLinear(field_dims) # Linear
self.embedding = FeaturesEmbedding(field_dims, embed_dim) # Embedding
self.LNN_dim = LNN_dim
self.LNN_output_dim = self.LNN_dim * embed_dim
self.LNN = LNN(self.num_fields, embed_dim, LNN_dim)
self.mlp = MultiLayerPerceptron(self.LNN_output_dim, mlp_dims,
dropouts[0])
def __init__(self, field_dims, embed_dim, mlp_dims, dropouts):
super().__init__()
self.linear = LogisticRegressionModel(field_dims)
self.ffm = torch.nn.Sequential(
FieldAwareFactorizationMachine(field_dims, embed_dim),
torch.nn.BatchNorm1d(embed_dim), torch.nn.Dropout(dropouts[0]))
self.ffm_output_dim = len(field_dims) * (len(field_dims) -
1) // 2 * embed_dim
self.mlp = MultiLayerPerceptron(self.ffm_output_dim, mlp_dims,
dropouts[1])
def __init__(self, field_dims, embed_dim, mlp_dims, dropouts):
super().__init__()
self.linear = FeaturesLinear(field_dims)
self.ffm = FieldAwareFactorizationMachine(field_dims, embed_dim)
self.ffm_output_dim = len(field_dims) * (len(field_dims) -
1) // 2 * embed_dim
self.bn = torch.nn.BatchNorm1d(self.ffm_output_dim)
self.dropout = torch.nn.Dropout(dropouts[0])
self.mlp = MultiLayerPerceptron(self.ffm_output_dim, mlp_dims,
dropouts[1])
def __init__(self, field_dims, embed_dim, mlp_dims, dropout, method='inner'):
super().__init__()
num_fields = len(field_dims)
if method == 'inner':
self.pn = InnerProductNetwork()
elif method == 'outer':
self.pn = OuterProductNetwork(num_fields, embed_dim)
else:
raise ValueError('unknown product type: ' + method)
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.linear = FeaturesLinear(field_dims, embed_dim)
self.embed_output_dim = num_fields * embed_dim
self.mlp = MultiLayerPerceptron(num_fields * (num_fields - 1) // 2 + self.embed_output_dim, mlp_dims, dropout)
class DeepFM(FM):
def __init__(self, opt):
super(DeepFM, self).__init__(opt)
self.embed_output_dim = len(self.field_dims) * self.latent_dim
self.mlp_dims = opt['mlp_dims']
self.mlp = MultiLayerPerceptron(self.embed_output_dim, self.mlp_dims, dropout=0.2)
def forward(self, x):
linear_score = self.linear.forward(x)
xv = self.embedding(x)
fm_score = self.fm.forward(xv)
dnn_score = self.mlp.forward(xv.view(-1, self.embed_output_dim))
score = linear_score + fm_score + dnn_score
return score.squeeze(1)
def __init__(self,
field_dims,
embed_dim,
mlp_dims,
dropout,
cross_layer_sizes,
split_half=True):
super().__init__()
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.embed_output_dim = len(field_dims) * embed_dim
self.cin = CompressedInteractionNetwork(len(field_dims),
cross_layer_sizes, split_half)
self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims,
dropout)
self.linear = FeaturesLinear(field_dims)
def __init__(self, field_dims, embed_dim, num_heads, num_layers, mlp_dims,
dropouts):
super().__init__()
self.embed_dim = embed_dim
self.num_fields = len(field_dims)
self.linear = FeaturesLinear(field_dims)
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.embed_output_dim = len(field_dims) * embed_dim
# self.res = torch.nn.Linear(self.embed_output_dim,self.embed_output_dim)
self.mlp = MultiLayerPerceptron(self.embed_output_dim + 399, mlp_dims,
dropouts[1])
self.self_attns = torch.nn.ModuleList([
torch.nn.MultiheadAttention(embed_dim,
num_heads,
dropout=dropouts[0])
for _ in range(num_layers)
])
self.attn_fc = torch.nn.Linear(self.embed_output_dim, 1)
