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def __init__(self, vocab_size, ndim_feature, num_layers=2, use_tanh=True, dropout_embedding_softmax=0.75, dropout_rnn=0.2, variational_dropout=False):
self.vocab_size = vocab_size
self.ndim_feature = ndim_feature
self.num_layers = num_layers
self.dropout_softmax = dropout_embedding_softmax
self.dropout_rnn = dropout_rnn
self.variational_dropout = variational_dropout
self.model = nn.Module()
for _ in range(num_layers):
self.model.add(nn.SRU(ndim_feature, use_tanh, dropout_rnn if variational_dropout else 0))
self.model.embed = nn.EmbedID(vocab_size, ndim_feature)
self.model.fc = nn.Convolution1D(ndim_feature, vocab_size)
for param in self.model.params():
if param.name == "W" and param.data is not None:
param.data[...] = np.random.normal(0, 0.01, param.data.shape)
self.reset_state()