Bidirectional LSTM (BiLSTM) Training Task

A Bidirectional LSTM (BiLSTM) Training Task is a Bidirectional Neural Network Training Task that contains LSTM units.

• AKA: BLSTM Training Task, BiLSTM Training Task.
• Example(s):
  • a Bidirectional LSTM-CNN Training Task,
  • a Bidirectional LSTM-CRF Training Task,
  • a Bidirectional LSTM-CNNs-CRF Training Task.
  • …
• Counter-Example(s):
  • an Unidirectional LSTM-based Language Modeling Task,
  • a Bidirectional RNN Training Task,
  • a Bidirectional GRU Training Task,
  • a BiLSTM WSD Classification Task,
  • a seq2seq-based Neural Modeling Task.
• See: Bidirectional LSTM (biLSTM) Network, LSTM Training System, RNN Training System, Artificial Neural Network, PyTorch.

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References

2018a

• (Cui, Ke & Wang, 2018) ⇒ Zhiyong Cui, Ruimin Ke, and Yinhai Wang (2018). "Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction" (PDF). arXiv preprint [arXiv:1801.02143].
  • QUOTE: ... the structure of an unfolded BDLSTM layer, containing a forward LSTM layer and a backward LSTM layer, is introduced and illustrated in Fig. 3.

Fig. 3 Unfolded architecture of bidirectional LSTM with three consecutive steps

2018b

• (Github, 2018) ⇒ Theano-Recurrence Training System: https://github.com/uyaseen/theano-recurrence training Retrieved: 2018-07-01
  • train.py provides a convenient method train(..) to train each model, you can select the recurrent model with the rec_model parameter, it is set to gru by default (possible options include rnn, gru, lstm, birnn, bigru & bilstm), number of hidden neurons in each layer (at the moment only single layer models are supported to keep the things simple, although adding more layers is very trivial) can be adjusted by n_h parameter in train(..), which by default is set to 100. As the model is trained it stores the current best state of the model i.e set of weights (best = least training error), the stored model is in the data\models\MODEL-NAME-best_model.pkl, also this stored model can later be used for resuming training from the last point or just for prediction/sampling. If you don't want to start training from scratch and instead use the already trained model then set use_existing_model=True in argument to train(..). Also optimization strategies can be specified to train(..) via optimizer parameter, currently supported optimizations are rmsprop, adam and vanilla stochastic gradient descent and can be found in utilities\optimizers.py. b_path, learning_rate, n_epochs in the train(..) specifies the 'base path to store model' (default = data\models\), 'initial learning rate of the optimizer', and 'number of epochs respectively'. During the training some logs (current epoch, sample, cross-entropy error etc) are shown on console to get an idea of how well learning is proceeding, logging frequencycan be specified via logging_freq in the train(..). At the end of training, a plot of cross-entropy error vs # of iterations gives an overview of overall training process and is also stored in the b_path.

2015

• (Fan et al., 2015) ⇒ Bo Fan, Lijuan Wang, Frank K. Soong, and Lei Xie (2015, April). "Photo-real talking head with deep bidirectional LSTM". In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 4884-4888). IEEE. DOI: 10.1109/ICASSP.2015.7178899
  • QUOTE: In our BLSTM network, as shown in Fig. 3, label sequence [math]\displaystyle{ L }[/math] is the input layer, and visual feature sequence [math]\displaystyle{ V }[/math] serves as the output layer and [math]\displaystyle{ H }[/math] denotes the hidden layer. In particular, at t-th frame, the input of the network is the t-th label vector [math]\displaystyle{ l_t }[/math] and the output is the [math]\displaystyle{ t }[/math]-th visual feature vector [math]\displaystyle{ v_t }[/math]. As described in (Graves, 2012), the basic idea of this bidirectional structure is to present each sequence forwards and backwards to two separate recurrent hidden layers, both of which are connected to the same output layer. This provides the network with complete, symmetrical, past and future context for every point in the input sequence. Please note that in Fig. 3, more hidden layers can be added in to construct a deep BLSTM.

    In the training stage, we have multiple sequence pairs of [math]\displaystyle{ L }[/math] and [math]\displaystyle{ V }[/math]. As we represent both sequences as continuous numerical vectors, the network is treated as a regression model to minimizing the SSE of predicting [math]\displaystyle{ \hat{V} }[/math] from [math]\displaystyle{ L }[/math].

    

    Fig. 3. BLSTM neural network in our talking head system.

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