def __init__(self, hyperParams):
super(Encoder, self).__init__()
self.hyperParams = hyperParams
if hyperParams.charEmbFile == "":
self.charEmb = nn.Embedding(hyperParams.charNUM,
hyperParams.charEmbSize)
self.charDim = hyperParams.charEmbSize
else:
reader = Reader()
self.charEmb, self.charDim = reader.load_pretrain(
hyperParams.charEmbFile, hyperParams.charAlpha,
hyperParams.unk)
self.charEmb.weight.requires_grad = hyperParams.charFineTune
if hyperParams.bicharEmbFile == "":
self.bicharEmb = nn.Embedding(hyperParams.bicharNUM,
hyperParams.bicharEmbSize)
self.bicharDim = hyperParams.bicharEmbSize
else:
reader = Reader()
self.bicharEmb, self.bicharDim = reader.load_pretrain(
hyperParams.bicharEmbFile, hyperParams.bicharAlpha,
hyperParams.unk)
self.bicharEmb.weight.requires_grad = hyperParams.bicharFineTune
self.dropOut = nn.Dropout(hyperParams.dropProb)
self.bilstm = nn.LSTM(input_size=self.charDim + self.bicharDim,
hidden_size=hyperParams.rnnHiddenSize,
batch_first=True,
bidirectional=True,
num_layers=2,
dropout=hyperParams.dropProb)
def __init__(self, hyperParams):
super(Decoder, self).__init__()
reader = Reader()
self.wordEmb, self.wordDim = reader.load_pretrain(
hyperParams.wordEmbFile, hyperParams.wordAlpha, hyperParams.unk)
self.wordEmb.weight.requires_grad = hyperParams.wordFineTune
self.dropOut = torch.nn.Dropout(hyperParams.dropProb)
self.lastWords = []
self.hyperParams = hyperParams
#self.linearLayer = nn.Linear(hyperParams.rnnHiddenSize * 2, hyperParams.labelSize)
self.linearLayer = nn.Linear(
hyperParams.rnnHiddenSize * 2 + self.wordDim,
hyperParams.labelSize)
self.softmax = nn.LogSoftmax()
def __init__(self, hyperParams):
super(Encoder, self).__init__()
self.hyperParams = hyperParams
if hyperParams.wordEmbFile == "":
self.wordEmb = nn.Embedding(hyperParams.postWordNum,
hyperParams.wordEmbSize)
self.wordDim = hyperParams.wordEmbSize
else:
reader = Reader()
self.wordEmb, self.wordDim = reader.load_pretrain(
hyperParams.wordEmbFile, hyperParams.postWordAlpha,
hyperParams.unk)
self.wordEmb.weight.requires_grad = hyperParams.wordFineTune
self.dropOut = nn.Dropout(hyperParams.dropProb)
self.gru = nn.GRU(input_size=self.wordDim,
hidden_size=hyperParams.rnnHiddenSize,
batch_first=True,
dropout=hyperParams.dropProb)
