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)