def __init__(self,
embed_size : int,
num_fields : int,
deep_output_size : int,
deep_layer_sizes : List[int],
reduction : int,
ffm_dropout_p : float = 0.0,
deep_dropout_p : List[float] = None,
deep_activation : Callable[[torch.Tensor], torch.Tensor] = nn.ReLU()):
super(FieldAttentiveDeepFieldAwareFactorizationMachineModel, self).__init__()
# initialize compose excitation network
self.cen = CENLayer(num_fields, reduction)
# ffm's input shape = (B, N * N, E)
# ffm's output shape = (B, NC2, E)
self.ffm = FFMLayer(num_fields=num_fields, dropout_p=ffm_dropout_p)
# calculate the output's size of ffm, i.e. inputs' size of DNNLayer
inputs_size = combination(num_fields, 2)
# deep's input shape = (B, NC2, E)
# deep's output shape = (B, 1, O)
self.deep = DNNLayer(
output_size = deep_output_size,
layer_sizes = deep_layer_sizes,
embed_size = embed_size,
num_fields = inputs_size,
dropout_p = deep_dropout_p,
activation = deep_activation
)
def __init__(self,
embed_size: int,
num_fields: int,
dropout_p: float = 0.0):
r"""Initialize FieldAwareFactorizationMachineModel
Args:
embed_size (int): Size of embedding tensor
num_fields (int): Number of inputs' fields
dropout_p (float, optional): Probability of Dropout in FFM.
Defaults to 0.0.
Attributes:
ffm (nn.Module): Module of field-aware factorization machine layer.
bias (nn.Parameter): Parameter of bias of field-aware factorization machine.
"""
# refer to parent class
super(FieldAwareFactorizationMachineModel, self).__init__()
# initialize ffm layer
self.ffm = FFMLayer(num_fields, dropout_p=dropout_p)
# initialize bias parameter
self.bias = nn.Parameter(torch.zeros((1, 1), names=("B", "O")))
nn.init.uniform_(self.bias.data)
def __init__(self,
embed_size: int,
num_fields: int,
deep_output_size: int,
deep_layer_sizes: List[int],
ffm_dropout_p: Optional[float] = None,
deep_dropout_p: Optional[List[float]] = None,
deep_activation: Optional[nn.Module] = nn.ReLU()):
"""
Initialize DeepFieldAwareFactorizationMachineModel
Args:
embed_size (int): size of embedding tensor
num_fields (int): number of inputs' fields
deep_output_size (int): output size of dense network
deep_layer_sizes (List[int]): layer sizes of dense network
ffm_dropout_p (float, optional): probability of Dropout in FFM. Defaults to 0.0
deep_dropout_p (List[float], optional): probability of Dropout in dense network. Defaults to None
deep_activation (torch.nn.Module, optional): activation function of dense network. Defaults to nn.ReLU()
"""
super().__init__()
self.ffm = FFMLayer(num_fields=num_fields, dropout_p=ffm_dropout_p)
inputs_size = combination(num_fields, 2)
inputs_size *= embed_size
self.deep = DNNLayer(inputs_size=inputs_size,
output_size=deep_output_size,
layer_sizes=deep_layer_sizes,
dropout_p=deep_dropout_p,
activation=deep_activation)
def __init__(self,
embed_size: int,
num_fields: int,
deep_output_size: int,
deep_layer_sizes: List[int],
reduction: int,
ffm_dropout_p: float = 0.0,
deep_dropout_p: List[float] = None,
deep_activation: Callable[[torch.Tensor],
torch.Tensor] = nn.ReLU()):
"""Initialize FieldAttentiveDeepFieldAwareFactorizationMachineModel
Args:
embed_size (int): Size of embedding tensor
num_fields (int): Number of inputs' fields
deep_output_size (int): Output size of dense network
deep_layer_sizes (List[int]): Layer sizes of dense network
reduction (int): Reduction of CIN layer
ffm_dropout_p (float, optional): Probability of Dropout in FFM.
Defaults to 0.0.
deep_dropout_p (List[float], optional): Probability of Dropout in dense network.
Defaults to None.
deep_activation (Callable[[T], T], optional): Activation function of dense network.
Defaults to nn.ReLU().
Attributes:
cen (nn.Module): Module of compose excitation network layer.
ffm (nn.Module): Module of field-aware factorization machine layer.
deep (nn.Module): Module of dense layer.
"""
# refer to parent class
super(FieldAttentiveDeepFieldAwareFactorizationMachineModel,
self).__init__()
# initialize compose excitation network
self.cen = CENLayer(num_fields, reduction)
# initialize ffm layer
self.ffm = FFMLayer(num_fields=num_fields, dropout_p=ffm_dropout_p)
# calculate the output's size of ffm, i.e. inputs' size of DNNLayer
inputs_size = combination(num_fields, 2)
inputs_size *= embed_size
# initialize dense layer
self.deep = DNNLayer(inputs_size=inputs_size,
output_size=deep_output_size,
layer_sizes=deep_layer_sizes,
dropout_p=deep_dropout_p,
activation=deep_activation)
def __init__(self,
num_fields: int,
dropout_p: Optional[float] = 0.0):
"""
Initialize FieldAwareFactorizationMachineModel
Args:
num_fields (int): number of inputs' fields
dropout_p (float, optional): probability of Dropout in FFM. Defaults to 0.0
"""
super().__init__()
self.ffm = FFMLayer(num_fields, dropout_p=dropout_p)
self.bias = nn.Parameter(torch.zeros((1, 1,), names=('B', 'O',)))
nn.init.uniform_(self.bias.data)
def __init__(self,
embed_size: int,
num_fields: int,
deep_output_size: int,
deep_layer_sizes: List[int],
output_size: int = 1,
ffm_dropout_p: float = 0.0,
deep_dropout_p: List[float] = None,
deep_activation: Callable[[torch.Tensor],
torch.Tensor] = nn.ReLU()):
r"""Initialize DeepFieldAwareFactorizationMachineModel
Args:
embed_size (int): Size of embedding tensor
num_fields (int): Number of inputs' fields
deep_output_size (int): Output size of dense network
deep_layer_sizes (List[int]): Layer sizes of dense network
output_size (int, optional): Output size of model,
i.e. output size of the projection layer.
Defaults to 1.
ffm_dropout_p (float, optional): Probability of Dropout in FFM.
Defaults to 0.0.
deep_dropout_p (List[float], optional): Probability of Dropout in dense network.
Defaults to None.
deep_activation (Callable[[T], T], optional): Activation function of dense network.
Defaults to nn.ReLU().
Attributes:
ffm (nn.Module): Module of field-aware factorization machine layer.
deep (nn.Module): Module of dense layer.
"""
# Refer to parent class
super(DeepFieldAwareFactorizationMachineModel, self).__init__()
# Initialize ffm layer
self.ffm = FFMLayer(num_fields=num_fields, dropout_p=ffm_dropout_p)
# Calculate inputs' size of DNNLayer, i.e. output's size of ffm (= NC2) * embed_size
inputs_size = combination(num_fields, 2)
inputs_size *= embed_size
# Initialize dense layer
self.deep = DNNLayer(inputs_size=inputs_size,
output_size=deep_output_size,
layer_sizes=deep_layer_sizes,
dropout_p=deep_dropout_p,
activation=deep_activation)
def __init__(self,
embed_size: int,
num_fields: int,
deep_output_size: int,
deep_layer_sizes: List[int],
ffm_dropout_p: float = 0.0,
deep_dropout_p: List[float] = None,
deep_activation: Callable[[torch.Tensor],
torch.Tensor] = nn.ReLU(),
output_size: int = 1):
r"""initialize Deep Field-aware Factorization Machine Model
Args:
embed_size (int): embedding size
num_fields (int): number of fields in inputs
deep_output_size (int): output size of deep neural network
deep_layer_sizes (List[int]): layer sizes of deep neural network
ffm_dropout_p (float, optional): dropout probability after ffm layer. Defaults to 0.0.
deep_dropout_p (List[float], optional): dropout probability after each deep neural network. Allow: [None, List[float]]. Defaults to None.
deep_activation (Callable[[T], T], optional): activation after each deep neural network. Allow: [None, Callable[[T], T]]. Defaults to nn.ReLU().
output_size (int, optional): output size of linear transformation after concatenate. Defaults to 1.
"""
# initialize nn.Module class
super(DeepFieldAwareFactorizationMachineModel, self).__init__()
# sequential of second-order part in inputs
self.second_order = nn.Sequential()
# ffm's input shape = (B, N * N, E)
# ffm's output shape = (B, NC2, E)
self.second_order.add_module(
"ffm", FFMLayer(num_fields=num_fields, dropout_p=ffm_dropout_p))
# calculate the output's size of ffm, i.e. inputs' size of DNNLayer
inputs_size = combination(num_fields, 2)
# deep's input shape = (B, NC2, E)
# deep's output shape = (B, 1, O)
self.second_order.add_module(
"deep",
DNNLayer(output_size=deep_output_size,
layer_sizes=deep_layer_sizes,
embed_size=embed_size,
num_fields=inputs_size,
dropout_p=deep_dropout_p,
activation=deep_activation))