def get_config_rnn(vocab_size, n=16): config = net.Config(vocab_size) config.embedding_dim = n config.structure_type = 'rnn' config.rnn_hidden_layers = 1 config.rnn_hidden_size = n config.rnn_predict = True config.rnn_type = 'blstm' return config
def get_config_rnn(vocab_size): config = net.Config(vocab_size) config.embedding_dim = 200 config.structure_type = 'rnn' config.rnn_type = 'blstm' config.rnn_hidden_size = 200 config.rnn_hidden_layers = 2 # config.rnn_predict = True return config
def get_config_rnn_large(vocab_size): config = net.Config(vocab_size) config.embedding_dim = 512 config.structure_type = 'rnn' config.rnn_type = 'blstm' config.rnn_hidden_size = 512 config.rnn_hidden_layers = 2 config.rnn_predict = True config.rnn_share_emb = True return config
def get_config_rnn(vocab_size, n=200): from trf.common import net config = net.Config(vocab_size) config.embedding_dim = n config.structure_type = 'rnn' config.rnn_type = 'blstm' config.rnn_hidden_size = n config.rnn_hidden_layers = 1 config.rnn_predict = True config.rnn_share_emb = True return config
def get_config_cnn(vocab_size, n=32): config = net.Config(vocab_size) config.embedding_dim = n config.structure_type = 'cnn' config.cnn_filters = [(i, n) for i in range(1, 6)] config.cnn_hidden = n config.cnn_width = 3 config.cnn_layers = 3 config.cnn_skip_connection = True config.cnn_residual = False config.cnn_activation = 'relu' config.cnn_batch_normalize = False return config
def get_config_mix(vocab_size): config = net.Config(vocab_size) config.embedding_dim = 200 config.structure_type = 'mix' config.cnn_filters = [(i, 128) for i in range(1, 11)] config.cnn_hidden = 128 config.cnn_width = 3 config.cnn_layers = 3 config.cnn_activation = 'relu' config.cnn_skip_connection = True config.cnn_residual = False config.cnn_batch_normalize = False config.rnn_type = 'blstm' config.rnn_hidden_size = 128 config.rnn_hidden_layers = 1 # config.rnn_predict = True # config.rnn_share_emb = True config.attention = True return config