Source: texcla/models/sequence_encoders.py#L0

SequenceEncoderBase

SequenceEncoderBase.__init__

__init__(self, dropout_rate=0.5)

Creates a new instance of sequence encoder.

Args:

  • dropout_rate: The final encoded output dropout.

YoonKimCNN

YoonKimCNN.__init__

__init__(self, num_filters=64, filter_sizes=[3, 4, 5], dropout_rate=0.5, **conv_kwargs)

Yoon Kim's shallow cnn model: https://arxiv.org/pdf/1408.5882.pdf

Args:

  • num_filters: The number of filters to use per filter_size. (Default value = 64)
  • filter_sizes: The filter sizes for each convolutional layer. (Default value = [3, 4, 5]) **cnn_kwargs: Additional args for building the Conv1D layer.

AlexCNN

AlexCNN.__init__

__init__(self, num_filters=20, filter_sizes=[3, 8], dropout_rate=[0.5, 0.8], hidden_dims=20, \
    **conv_kwargs)

Alexander Rakhlin's CNN model: https://github.com/alexander-rakhlin/CNN-for-Sentence-Classification-in-Keras/

Args:

  • num_filters: The number of filters to use per filter_size. (Default value = 64)
  • filter_sizes: The filter sizes for each convolutional layer. (Default value = [3, 4, 5])
  • dropout_rate: Array for one dropout layer after the embedding and one before the final dense layer (Default value = [0.5, 0.8])

StackedRNN

StackedRNN.__init__

__init__(self, rnn_class=<class 'keras.layers.recurrent.LSTM'>, hidden_dims=[50, 50], \
    bidirectional=True, dropout_rate=0.5, **rnn_kwargs)

Creates a stacked RNN.

Args:

  • rnn_class: The type of RNN to use. (Default Value = LSTM)
  • encoder_dims: The number of hidden units of RNN. (Default Value: 50)
  • bidirectional: Whether to use bidirectional encoding. (Default Value = True) **rnn_kwargs: Additional args for building the RNN.

BasicRNN

BasicRNN.__init__

__init__(self, rnn_class=<class 'keras.layers.recurrent.LSTM'>, hidden_dims=50, \
    bidirectional=True, dropout_rate=0.5, **rnn_kwargs)

Creates a stacked RNN.

Args:

  • rnn_class: The type of RNN to use. (Default Value = LSTM)
  • encoder_dims: The number of hidden units of RNN. (Default Value: 50)
  • bidirectional: Whether to use bidirectional encoding. (Default Value = True) **rnn_kwargs: Additional args for building the RNN.

AttentionRNN

AttentionRNN.__init__

__init__(self, rnn_class=<class 'keras.layers.recurrent.LSTM'>, encoder_dims=50, \
    bidirectional=True, dropout_rate=0.5, **rnn_kwargs)

Creates an RNN model with attention. The attention mechanism is implemented as described in https://www.cs.cmu.edu/~hovy/papers/16HLT-hierarchical-attention-networks.pdf, but without sentence level attention.

Args:

  • rnn_class: The type of RNN to use. (Default Value = LSTM)
  • encoder_dims: The number of hidden units of RNN. (Default Value: 50)
  • bidirectional: Whether to use bidirectional encoding. (Default Value = True) **rnn_kwargs: Additional args for building the RNN.

AveragingEncoder

AveragingEncoder.__init__

__init__(self, dropout_rate=0)

An encoder that averages sequence inputs.