def __init__(self, options):
super(Decoder_deRNNG, self).__init__()
# stack-LSTM
self.stack_init = Initializer(options['stack_init'])
self.stack = LSTM(options['stack'])
# history-LSTM
self.history_init = Initializer(options['history_init'])
self.history = LSTM(options['history'])
# buffer-LSTM
self.buffer_init = Initializer(options['buffer_init'])
self.buffer = MaskLSTM(options['buffer'])
# Attention
self.attention = Attention(options['attention'])
# Prediction
self.hidden_act = Linear(options['hidden_act'])
self.hidden_tok = Linear(options['hidden_tok'])
self.predict_act = Linear(options['predict_act'])
self.predict_tok = Linear(options['predict_tok'])
# Copy Mechanism
self.switcher_copy = Linear(options['switcher_copy'])
def __init__(self, options):
super(Decoder_FlatLVT, self).__init__()
self.init = Initializer(options['init'])
self.main = LSTM(options['main'])
self.attention = Attention(options['attention'])
self.hidden = Linear(options['hidden'])
self.predict = Linear(options['predict'])
self.switcher_copy = Linear(options['switcher_copy'])
def __init__(self, options):
super(TreeNode_De, self).__init__()
self.layer = Linear(options)
def __init__(self, options):
super(Last_Two, self).__init__()
self.birectional = options['birectional']
self.n_dim = options['n_in']
self.layer_1 = Linear(options)
self.layer_2 = Linear(options)
def __init__(self, options):
super(Mean_Two, self).__init__()
self.layer_1 = Linear(options)
self.layer_2 = Linear(options)
def __init__(self, options):
super(Mean_One, self).__init__()
self.layer = Linear(options)
def __init__(self, options):
super(Decoder_Flat, self).__init__()
self.init = Initializer(options['init'])
self.main = LSTM(options['main'])
self.predict = Linear(options['predict'])