def __init__(self, embedding_dim, hidden_dim, max_length):
super(Decoder, self).__init__()
self.embedding_dim = embedding_dim
self.hidden_dim = hidden_dim
self.max_length = max_length
self.input_weights = nn.Linear(hidden_dim, 3 * hidden_dim)
self.input_weights.weight.readable_name = 'Decoder-ih-w'
self.input_weights.bias.readable_name = 'Decoder-ih-b'
self.hidden_weights = nn.Linear(hidden_dim, 3 * hidden_dim)
self.hidden_weights.weight.readable_name = 'Decoder-hh-w'
self.hidden_weights.bias.readable_name = 'Decoder-hh-b'
# self.input_reduction = nn.Linear(embedding_dim, hidden_dim)
self.pointer_s = Attention(hidden_dim, modulename='Head')
self.pointer_e = Attention(hidden_dim, modulename='Tail')
# self.encoding_s = torch.nn.Conv1d(hidden_dim, hidden_dim, kernel_size=2, stride=1, padding=0)
# set_readable_param_names(self.encoding_s, 'Decoder-encoding-s')
self.cls = torch.nn.Conv1d(hidden_dim,
2,
kernel_size=3,
stride=1,
padding=0)
def __init__(self,
embedding_dim,
hidden_dim,
max_length):
super(Decoder, self).__init__()
self.embedding_dim = embedding_dim
self.hidden_dim = hidden_dim
self.max_length = max_length
self.input_weights = nn.Linear(hidden_dim, 3 * hidden_dim)
self.input_weights.weight.readable_name = 'Decoder-ih-w'
self.input_weights.bias.readable_name = 'Decoder-ih-b'
self.hidden_weights = nn.Linear(hidden_dim, 3 * hidden_dim)
self.hidden_weights.weight.readable_name = 'Decoder-hh-w'
self.hidden_weights.bias.readable_name = 'Decoder-hh-b'
# self.input_reduction = nn.Linear(embedding_dim, hidden_dim)
self.pointer_s = Attention(hidden_dim, modulename='Head')
self.pointer_e = Attention(hidden_dim, modulename='Tail')
self.cls = torch.nn.Conv1d(2, 2, kernel_size=hidden_dim, stride=1, padding=0)
self.cls.weight.readable_name = 'Decoder-ScoreConv-w'
self.cls.bias.readable_name = 'Decoder-ScoreConv-b'
self.sm = nn.Softmax(dim=1)
# prev_v records previous pointed encoding features...
# self.prev_s_enc = nn.Parameter(torch.zeros(hidden_dim), requires_grad=False)
self.prev_e_enc = nn.Parameter(torch.zeros(hidden_dim), requires_grad=False)
def __init__(self, embedding_dim, hidden_dim, max_length):
super(Decoder, self).__init__()
self.embedding_dim = embedding_dim
self.hidden_dim = hidden_dim
self.max_length = max_length
self.input_weights = nn.Linear(embedding_dim, 4 * hidden_dim)
self.hidden_weights = nn.Linear(hidden_dim, 4 * hidden_dim)
self.pointer_s = Attention(hidden_dim)
self.pointer_e = Attention(hidden_dim)
self.cls = torch.nn.Conv1d(2,
2,
kernel_size=hidden_dim,
stride=1,
padding=0)
self.sm = nn.Softmax(dim=1)
def __init__(self, hidden_dim, max_length):
super(Decoder, self).__init__()
self.hidden_dim = hidden_dim
self.max_length = max_length
self.input_weights = nn.Linear(hidden_dim, 3 * hidden_dim)
self.input_weights.weight.readable_name = 'Decoder-ih-w'
self.input_weights.bias.readable_name = 'Decoder-ih-b'
self.hidden_weights = nn.Linear(hidden_dim, 3 * hidden_dim)
self.hidden_weights.weight.readable_name = 'Decoder-hh-w'
self.hidden_weights.bias.readable_name = 'Decoder-hh-b'
# self.input_reduction = nn.Linear(embedding_dim, hidden_dim)
self.pointer = Attention(hidden_dim, modulename='pointer')
self.cls = torch.nn.Conv1d(2,
2,
kernel_size=hidden_dim,
stride=1,
padding=0)
self.cls.weight.readable_name = 'Decoder-ScoreConv-w'
self.cls.bias.readable_name = 'Decoder-ScoreConv-b'
self.sm = nn.Softmax(dim=1)