neuralmonkey.decoders package¶
Submodules¶
neuralmonkey.decoders.decoder module¶
neuralmonkey.decoders.encoder_projection module¶
This module contains different variants of projection of encoders into the initial state of the decoder.
-
neuralmonkey.decoders.encoder_projection.concat_encoder_projection(train_mode: tensorflow.python.framework.ops.Tensor, rnn_size: typing.Union[int, NoneType] = None, encoders: typing.Union[typing.List[typing.Any], NoneType] = None) → tensorflow.python.framework.ops.Tensor¶ Create the initial state by concatenating the encoders’ encoded values
Parameters: - train_mode – tf 0-D bool Tensor specifying the training mode (not used)
- rnn_size – The size of the resulting vector (not used)
- encoders – The list of encoders
-
neuralmonkey.decoders.encoder_projection.empty_initial_state(train_mode: tensorflow.python.framework.ops.Tensor, rnn_size: typing.Union[int, NoneType], encoders: typing.Union[typing.List[typing.Any], NoneType] = None) → tensorflow.python.framework.ops.Tensor¶ Return an empty vector
Parameters: - train_mode – tf 0-D bool Tensor specifying the training mode (not used)
- rnn_size – The size of the resulting vector
- encoders – The list of encoders (not used)
-
neuralmonkey.decoders.encoder_projection.linear_encoder_projection(dropout_keep_prob: float) → typing.Callable[[tensorflow.python.framework.ops.Tensor, typing.Union[int, NoneType], typing.Union[typing.List[typing.Any], NoneType]], tensorflow.python.framework.ops.Tensor]¶ Return a projection function which applies dropout on concatenated encoder final states and returns a linear projection to a rnn_size-sized tensor.
Parameters: dropout_keep_prob – The dropout keep probability
neuralmonkey.decoders.multi_decoder module¶
neuralmonkey.decoders.output_projection module¶
This module contains different variants of projection functions for RNN outputs.
-
neuralmonkey.decoders.output_projection.maxout_output(maxout_size)¶ Compute RNN output out of the previous state and output, and the context tensors returned from attention mechanisms, as described in the article
This function corresponds to the equations for computation the t_tilde in the Bahdanau et al. (2015) paper, on page 14, with the maxout projection, before the last linear projection.
Parameters: maxout_size – The size of the hidden maxout layer in the deep output Returns: Returns the maxout projection of the concatenated inputs
-
neuralmonkey.decoders.output_projection.mlp_output(layer_sizes, dropout_plc=None, activation=<function tanh>)¶ Compute RNN deep output using the multilayer perceptron with a specified activation function. (Pascanu et al., 2013 [https://arxiv.org/pdf/1312.6026v5.pdf])
Parameters: - layer_sizes – A list of sizes of the hiddel layers of the MLP
- dropout_plc – Dropout placeholder. TODO this is not going to work with current configuration
- activation – The activation function to use in each layer.
-
neuralmonkey.decoders.output_projection.no_deep_output(prev_state, prev_output, ctx_tensors)¶ Compute RNN output out of the previous state and output, and the context tensors returned from attention mechanisms.
This function corresponds to the equations for computation the t_tilde in the Bahdanau et al. (2015) paper, on page 14, before the linear projection.
Parameters: - prev_state – Previous decoder RNN state. (Denoted s_i-1)
- prev_output – Embedded output of the previous step. (y_i-1)
- ctx_tensors – Context tensors computed by the attentions. (c_i)
Returns: This function returns the concatenation of all its inputs.