def __init__(self, config):
super(TokenEmbeder, self).__init__()
self.conf = config
self.margin = config['margin']
self.n_token_words = config['n_token_words']
self.n_desc_words = config['n_desc_words']
self.emb_size = config['emb_size']
self.n_hidden = config['n_hidden']
self.dropout = config['dropout']
self.tok_encoder = SeqEncoder(self.n_token_words, self.emb_size,
self.n_hidden)
self.desc_encoder = SeqEncoder(self.n_desc_words, self.emb_size,
self.n_hidden)
#self.w_tok = nn.Linear(config['n_hidden'], config['n_hidden'])
#self.w_desc = nn.Linear(config['n_hidden'], config['n_hidden'])
#self.fuse = nn.Linear(config['n_hidden'], config['n_hidden'])
self.linear_attn_out = nn.Sequential(
nn.Linear(self.n_hidden, self.n_hidden), nn.Tanh(),
nn.Linear(self.n_hidden, self.n_hidden))
if self.conf['transform_every_modal']:
self.linear_single_modal = nn.Sequential(
nn.Linear(self.n_hidden, self.n_hidden), nn.Tanh(),
nn.Linear(self.n_hidden, self.n_hidden))
if self.conf['save_attn_weight']:
self.attn_weight_torch = []
self.node_mask_torch = []
self.self_atten = nn.Linear(self.n_hidden, self.n_hidden)
self.self_atten_scalar = nn.Linear(self.n_hidden, 1)
def __init__(self, config):
super(JointEmbeder, self).__init__()
self.conf = config
self.margin = config['margin']
self.name_encoder = SeqEncoder(config['n_words'], config['emb_size'],
config['lstm_dims'])
self.api_encoder = SeqEncoder(config['n_words'], config['emb_size'],
config['lstm_dims'])
self.tok_encoder = BOWEncoder(config['n_words'], config['emb_size'],
config['n_hidden'])
self.desc_encoder = SeqEncoder(config['n_words'], config['emb_size'],
config['lstm_dims'])
#self.fuse1=nn.Linear(config['emb_size']+4*config['lstm_dims'], config['n_hidden'])
#self.fuse2 = nn.Sequential(
# nn.Linear(config['emb_size']+4*config['lstm_dims'], config['n_hidden']),
# nn.BatchNorm1d(config['n_hidden'], eps=1e-05, momentum=0.1),
# nn.ReLU(),
# nn.Linear(config['n_hidden'], config['n_hidden']),
#)
self.w_name = nn.Linear(2 * config['lstm_dims'], config['n_hidden'])
self.w_api = nn.Linear(2 * config['lstm_dims'], config['n_hidden'])
self.w_tok = nn.Linear(config['emb_size'], config['n_hidden'])
self.fuse3 = nn.Linear(config['n_hidden'], config['n_hidden'])
self.init_weights()
def __init__(self, config):
super(MultiEmbeder, self).__init__()
self.conf = config
self.margin = config['margin']
self.emb_size = config['emb_size']
self.n_hidden = config['n_hidden']
self.dropout = config['dropout']
self.n_desc_words = config['n_desc_words']
self.n_token_words = config['n_token_words']
self.ast_encoder = TreeLSTM(self.conf)
self.cfg_encoder = GGNN(self.conf)
self.tok_encoder = SeqEncoder(self.n_token_words, self.emb_size,
self.n_hidden)
self.desc_encoder = SeqEncoder(self.n_desc_words, self.emb_size,
self.n_hidden)
self.tok_attn = nn.Linear(self.n_hidden, self.n_hidden)
self.tok_attn_scalar = nn.Linear(self.n_hidden, 1)
self.ast_attn = nn.Linear(self.n_hidden, self.n_hidden)
self.ast_attn_scalar = nn.Linear(self.n_hidden, 1)
self.cfg_attn = nn.Linear(self.n_hidden, self.n_hidden)
self.cfg_attn_scalar = nn.Linear(self.n_hidden, 1)
self.attn_modal_fusion = nn.Linear(self.n_hidden * 3, self.n_hidden)
def __init__(self, config):
super(JointEmbeder, self).__init__()
self.conf = config
self.margin = config['margin']
self.name_encoder = SeqEncoder(config['n_words'], config['emb_size'],
config['lstm_dims'])
self.api_encoder = SeqEncoder(config['n_words'], config['emb_size'],
config['lstm_dims'])
self.tok_encoder = BOWEncoder(config['n_words'], config['emb_size'],
config['n_hidden'])
self.desc_encoder = SeqEncoder(config['n_words'], config['emb_size'],
config['lstm_dims'])
self.fuse = nn.Linear(config['emb_size'] + 4 * config['lstm_dims'],
config['n_hidden'])
# create a model path to store model info
if not os.path.exists(config['workdir'] + 'models/'):
os.makedirs(config['workdir'] + 'models/')
def __init__(self, config):
super(JointEmbeder, self).__init__()
self.conf = config
self.margin = config['margin']
self.dropout = config['dropout']
self.n_hidden = config['n_hidden']
self.name_encoder = SeqEncoder(config['n_words'], config['emb_size'],
config['lstm_dims'])
self.tok_encoder = BOWEncoder(config['n_words'], config['emb_size'],
config['n_hidden'])
self.desc_encoder = SeqEncoder2(config['n_words'], config['emb_size'],
config['n_hidden'])
self.w_name = nn.Linear(2 * config['lstm_dims'], config['n_hidden'])
self.w_tok = nn.Linear(config['emb_size'], config['n_hidden'])
#self.w_desc = nn.Linear(2*config['lstm_dims'], config['n_hidden'])
self.fuse3 = nn.Linear(config['n_hidden'], config['n_hidden'])
self.self_attn2 = nn.Linear(self.n_hidden, self.n_hidden)
self.self_attn_scalar2 = nn.Linear(self.n_hidden, 1)
self.init_weights()