Class Seq2SeqModel

chatbotv4/seq2seq_model.py:32–317

Sequence-to-sequence model with attention and for multiple buckets. This class implements a multi-layer recurrent neural network as encoder, and an attention-based decoder. This is the same as the model described in this paper: http://arxiv.org/abs/1412.7449 - please look there for details,

import seq2seq_patch

class Seq2SeqModel(object):
  """Sequence-to-sequence model with attention and for multiple buckets.

  This class implements a multi-layer recurrent neural network as encoder,
  and an attention-based decoder. This is the same as the model described in
  this paper: http://arxiv.org/abs/1412.7449 - please look there for details,
  or into the seq2seq library for complete model implementation.
  This class also allows to use GRU cells in addition to LSTM cells, and
  sampled softmax to handle large output vocabulary size. A single-layer
  version of this model, but with bi-directional encoder, was presented in
    http://arxiv.org/abs/1409.0473
  and sampled softmax is described in Section 3 of the following paper.
    http://arxiv.org/abs/1412.2007
  """

  def __init__(self,
               source_vocab_size,
               target_vocab_size,
               buckets,
               size,
               num_layers,
               max_gradient_norm,
               batch_size,
               learning_rate,
               learning_rate_decay_factor,
               use_lstm=False,
               num_samples=512,
               forward_only=False,
               dtype=tf.float32):
    """Create the model.

    Args:
      source_vocab_size: size of the source vocabulary.
      target_vocab_size: size of the target vocabulary.
      buckets: a list of pairs (I, O), where I specifies maximum input length
        that will be processed in that bucket, and O specifies maximum output
        length. Training instances that have inputs longer than I or outputs
        longer than O will be pushed to the next bucket and padded accordingly.
        We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
      size: number of units in each layer of the model.
      num_layers: number of layers in the model.
      max_gradient_norm: gradients will be clipped to maximally this norm.
      batch_size: the size of the batches used during training;
        the model construction is independent of batch_size, so it can be
        changed after initialization if this is convenient, e.g., for decoding.
      learning_rate: learning rate to start with.
      learning_rate_decay_factor: decay learning rate by this much when needed.
      use_lstm: if true, we use LSTM cells instead of GRU cells.
      num_samples: number of samples for sampled softmax.
      forward_only: if set, we do not construct the backward pass in the model.
      dtype: the data type to use to store internal variables.
    """
    self.source_vocab_size = source_vocab_size
    self.target_vocab_size = target_vocab_size
    self.buckets = buckets
    self.batch_size = batch_size
    self.learning_rate = tf.Variable(
        float(learning_rate), trainable=False, dtype=dtype)