def __init__(self, vocab_size, layers=1, input_dim=None, lstm_dim=None, mlp_hidden_dim=None, trg_embed_dim=None, dropout=None,
rnn_spec="lstm", residual_to_output=False):
lstm_dim = lstm_dim or model_globals.get("default_layer_dim")
mlp_hidden_dim = mlp_hidden_dim or model_globals.get("default_layer_dim")
trg_embed_dim = trg_embed_dim or model_globals.get("default_layer_dim")
input_dim = input_dim or model_globals.get("default_layer_dim")
self.fwd_lstm = RnnDecoder.rnn_from_spec(rnn_spec, layers, trg_embed_dim, lstm_dim, model_globals.dynet_param_collection.param_col, residual_to_output)
self.mlp = MLP(input_dim + lstm_dim, mlp_hidden_dim, vocab_size, model_globals.dynet_param_collection.param_col)
self.dropout = dropout or model_globals.get("dropout")
self.state = None
def __init__(self, input_dim=None, state_dim=None, hidden_dim=None):
input_dim = input_dim or model_globals.get("default_layer_dim")
state_dim = state_dim or model_globals.get("default_layer_dim")
hidden_dim = hidden_dim or model_globals.get("default_layer_dim")
self.input_dim = input_dim
self.state_dim = state_dim
self.hidden_dim = hidden_dim
param_collection = model_globals.dynet_param_collection.param_col
self.pW = param_collection.add_parameters((hidden_dim, input_dim))
self.pV = param_collection.add_parameters((hidden_dim, state_dim))
self.pb = param_collection.add_parameters(hidden_dim)
self.pU = param_collection.add_parameters((1, hidden_dim))
self.curr_sent = None
def __init__(self,
input_dim=512,
layers=1,
hidden_dim=None,
downsampling_method="skip",
reduce_factor=2,
dropout=None):
hidden_dim = hidden_dim or model_globals.get("default_layer_dim")
dropout = dropout or model_globals.get("dropout")
self.dropout = dropout
self.builder = pyramidal.PyramidalRNNBuilder(
layers, input_dim, hidden_dim,
model_globals.dynet_param_collection.param_col,
dy.VanillaLSTMBuilder, downsampling_method, reduce_factor)
def init_builder(self,
input_dim,
layers,
hidden_dim=None,
chn_dim=3,
num_filters=32,
filter_size_time=3,
filter_size_freq=3,
stride=(2, 2),
dropout=None):
model = model_globals.dynet_param_collection.param_col
hidden_dim = hidden_dim or model_globals.get("default_layer_dim")
dropout = dropout or model_globals.get("dropout")
self.dropout = dropout
self.builder = conv_encoder.ConvBiRNNBuilder(
layers, input_dim, hidden_dim, model, dy.VanillaLSTMBuilder,
chn_dim, num_filters, filter_size_time, filter_size_freq, stride)
def __init__(self,
input_dim=512,
layers=1,
hidden_dim=None,
residual_to_output=False,
dropout=None,
bidirectional=True):
model = model_globals.dynet_param_collection.param_col
hidden_dim = hidden_dim or model_globals.get("default_layer_dim")
dropout = dropout or model_globals.get("dropout")
self.dropout = dropout
if bidirectional:
self.builder = residual.ResidualBiRNNBuilder(
layers, input_dim, hidden_dim, model, dy.VanillaLSTMBuilder,
residual_to_output)
else:
self.builder = residual.ResidualRNNBuilder(layers, input_dim,
hidden_dim, model,
dy.VanillaLSTMBuilder,
residual_to_output)
def __init__(self,
input_dim=None,
layers=1,
hidden_dim=None,
dropout=None,
bidirectional=True):
model = model_globals.dynet_param_collection.param_col
input_dim = input_dim or model_globals.get("default_layer_dim")
hidden_dim = hidden_dim or model_globals.get("default_layer_dim")
dropout = dropout or model_globals.get("dropout")
self.input_dim = input_dim
self.layers = layers
self.hidden_dim = hidden_dim
self.dropout = dropout
if bidirectional:
self.builder = dy.BiRNNBuilder(layers, input_dim, hidden_dim,
model, dy.VanillaLSTMBuilder)
else:
self.builder = dy.VanillaLSTMBuilder(layers, input_dim, hidden_dim,
model)
def __init__(self, vocab_size, emb_dim=None):
self.vocab_size = vocab_size
if emb_dim is None: emb_dim = model_globals.get("default_layer_dim")
self.emb_dim = emb_dim
self.embeddings = model_globals.dynet_param_collection.param_col.add_lookup_parameters(
(vocab_size, emb_dim))