def __init__(self, _, **kwargs):
super(LookupTableEmbeddings, self).__init__(_, **kwargs)
self.vsz = kwargs.get('vsz')
self.dsz = kwargs.get('dsz')
self.finetune = kwargs.get('finetune', True)
weights = kwargs.get('weights')
if weights is None:
self.embeddings = nn.Embedding(self.vsz, self.dsz, padding_idx=0)
else:
self.embeddings = pytorch_embedding(weights, self.finetune)
def __init__(self, name, **kwargs):
super(CharConvEmbeddings, self).__init__()
self.vsz = kwargs.get('vsz')
self.dsz = kwargs.get('dsz')
self.finetune = kwargs.get('finetune', True)
weights = kwargs.get('weights')
if weights is None:
self.embeddings = nn.Embedding(self.vsz, self.dsz, padding_idx=0)
else:
self.embeddings = pytorch_embedding(weights)
char_filtsz = kwargs.get('cfiltsz', [3])
if is_sequence(char_filtsz[0]):
char_hsz = [pair[1] for pair in char_filtsz]
char_filtsz = [pair[0] for pair in char_filtsz]
else:
char_hsz = kwargs.get('wsz', 30)
activation_type = kwargs.get('activation', 'tanh')
pdrop = kwargs.get('pdrop', 0.5)
self.char_comp = ParallelConv(self.dsz, char_hsz, char_filtsz,
activation_type, pdrop)
wchsz = self.char_comp.outsz
self.linear = pytorch_linear(wchsz, wchsz)
gating = kwargs.get('gating', 'skip')
GatingConnection = SkipConnection if gating == 'skip' else Highway
num_gates = kwargs.get('num_gates', 1)
self.gating_seq = nn.Sequential(
OrderedDict([('gate-{}'.format(i), GatingConnection(wchsz))
for i in range(num_gates)]))
def __init__(self, name, **kwargs):
super(CharLSTMEmbeddings, self).__init__(name, **kwargs)
self.vsz = kwargs.get('vsz')
self.dsz = kwargs.get('dsz')
self.finetune = kwargs.get('finetune', True)
weights = kwargs.get('weights')
if weights is None:
self.embeddings = nn.Embedding(self.vsz,
self.dsz,
padding_idx=Offsets.PAD)
else:
self.embeddings = pytorch_embedding(weights)
self.lstmsz = kwargs.get('lstmsz', 50)
layers = kwargs.get('layers', 1)
pdrop = kwargs.get('pdrop', 0.5)
rnn_type = kwargs.get('rnn_type', 'blstm')
unif = kwargs.get('unif', 0)
weight_init = kwargs.get('weight_init', 'uniform')
self.char_comp = BiRNNWrapper(
pytorch_lstm(self.dsz,
self.lstmsz,
rnn_type,
layers,
pdrop,
unif=unif,
initializer=weight_init,
batch_first=False), layers)
def __init__(self, _, **kwargs):
super(PositionalLookupTableEmbeddings, self).__init__()
self.vsz = kwargs.get('vsz')
self.dsz = kwargs.get('dsz')
self.dropout = nn.Dropout(kwargs.get('dropout', 0.1))
self.finetune = kwargs.get('finetune', True)
# This could get us in trouble, if in doubt, pick something big
mxlen = kwargs.get('mxlen', 1000)
max_timescale = kwargs.get('max_timescale', 1.0e4)
weights = kwargs.get('weights')
if weights is None:
self.embeddings = nn.Embedding(self.vsz, self.dsz, padding_idx=0)
else:
self.embeddings = pytorch_embedding(weights, self.finetune)
log_timescale_increment = math.log(max_timescale) / self.dsz
inv_timescales = torch.exp(
torch.arange(0, self.dsz, 2).float() * -log_timescale_increment)
pe = torch.zeros(mxlen, self.dsz)
position = torch.arange(0, mxlen).float().unsqueeze(1)
pe[:, 0::2] = torch.sin(position * inv_timescales)
pe[:, 1::2] = torch.cos(position * inv_timescales)
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def __init__(self, _, **kwargs):
super(LookupTableEmbeddings, self).__init__(_, **kwargs)
self.vsz = kwargs.get('vsz')
self.dsz = kwargs.get('dsz')
self.finetune = kwargs.get('finetune', True)
weights = kwargs.get('weights')
if weights is None:
self.embeddings = nn.Embedding(self.vsz, self.dsz, padding_idx=0)
else:
self.embeddings = pytorch_embedding(weights, self.finetune)
def __init__(self, name, **kwargs):
super(CharLSTMEmbeddings, self).__init__(name, **kwargs)
self.vsz = kwargs.get('vsz')
self.dsz = kwargs.get('dsz')
self.finetune = kwargs.get('finetune', True)
weights = kwargs.get('weights')
if weights is None:
self.embeddings = nn.Embedding(self.vsz, self.dsz, padding_idx=Offsets.PAD)
else:
self.embeddings = pytorch_embedding(weights)
self.lstmsz = kwargs.get('lstmsz', 50)
layers = kwargs.get('layers', 1)
pdrop = kwargs.get('pdrop', 0.5)
rnn_type = kwargs.get('rnn_type', 'blstm')
unif = kwargs.get('unif', 0)
weight_init = kwargs.get('weight_init', 'uniform')
self.char_comp = BiRNNWrapper(pytorch_lstm(self.dsz, self.lstmsz, rnn_type, layers, pdrop, unif=unif, initializer=weight_init, batch_first=False), layers)
def __init__(self, name, **kwargs):
super(CharConvEmbeddings, self).__init__()
self.vsz = kwargs.get('vsz')
self.dsz = kwargs.get('dsz')
self.finetune = kwargs.get('finetune', True)
weights = kwargs.get('weights')
if weights is None:
self.embeddings = nn.Embedding(self.vsz, self.dsz, padding_idx=0)
else:
self.embeddings = pytorch_embedding(weights)
char_filtsz = kwargs.get('cfiltsz', [3])
char_hsz = kwargs.get('wsz', 30)
activation_type = kwargs.get('activation', 'tanh')
pdrop = kwargs.get('pdrop', 0.5)
self.char_comp = ParallelConv(self.dsz, char_hsz, char_filtsz, activation_type, pdrop)
wchsz = self.char_comp.outsz
self.linear = pytorch_linear(wchsz, wchsz)
gating = kwargs.get('gating', 'skip')
GatingConnection = SkipConnection if gating == 'skip' else Highway
num_gates = kwargs.get('num_gates', 1)
self.gating_seq = nn.Sequential(OrderedDict(
[('gate-{}'.format(i), GatingConnection(wchsz)) for i in range(num_gates)]
))