cs501r_f2016:lab14
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cs501r_f2016:lab14 [2017/11/17 19:40] jszendre [PyTorch Overview:] |
cs501r_f2016:lab14 [2017/11/20 20:06] jszendre [Deliverable:] |
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| In this lab you will use PyTorch to implement a vanilla autoencoder / decoder neural machine translation system from Spanish to English. | In this lab you will use PyTorch to implement a vanilla autoencoder / decoder neural machine translation system from Spanish to English. | ||
| - | Some of the resources for this lab include [[https://arxiv.org/pdf/1409.3215.pdf|Sequence to Sequence Learning with Neural Networks]] and [[https://arxiv.org/pdf/1409.0473.pdf|D Bahdanau, 2015]]. State of the art NMT systems use Badanau's attention mechanism, but context alone should be enough for our dataset. | + | Some of the resources for this lab include [[https://arxiv.org/pdf/1409.3215.pdf|Sequence to Sequence Learning with Neural Networks]] and [[https://arxiv.org/pdf/1409.0473.pdf|D Bahdanau, 2015]]. The former will be of more use in implementing the lab. State of the art NMT systems use Badanau's attention mechanism, but context alone should be enough for our dataset. |
| Seq2seq and encoder/decoder are nearly synonymous architectures and represent the first major breakthrough using RNNs to map between source and target sequences of differing lengths. The encoder will map input sequences to a fixed length context vector and the decoder will then map that to the output sequence. Standard softmax / cross entropy is used on the scores output by the decoder and compared against the reference sequence. | Seq2seq and encoder/decoder are nearly synonymous architectures and represent the first major breakthrough using RNNs to map between source and target sequences of differing lengths. The encoder will map input sequences to a fixed length context vector and the decoder will then map that to the output sequence. Standard softmax / cross entropy is used on the scores output by the decoder and compared against the reference sequence. | ||
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| **Part 3:** Implementing the Decoder | **Part 3:** Implementing the Decoder | ||
| - | The decoder will be more involved, but will be similar to the encoder. This time there will be an additional intertemporal connection between each layer’s output and the subsequent first layer’s input between time steps. | + | Again implement a standard GRU using GRUCell with the exception that for the first timestep embed a tensor containing the SOS index. That and the context vector will serve as the input and initial hidden state. |
| - | For the first timestep embed a tensor containing the SOS index. That and the context vector will serve as the input and initial hidden state. Call GRUCell n_layers times like before, but for proceeding time steps use the prediction of the previous time step as the initial input. Like the autoencoder the initial hidden state at each time step will be the last hidden state from the previous time step. | + | Unlike the encoder, for each time step take the output (GRUCell calls it h') and run it through a linear layer and then softmax to get probabilities over the english corpus. Use the word with the highest probability as the input for the next timestep. |
| - | Use a linear layer and then softmax to convert the output at each time step to a tensor of probabilities over all words in your target corpus and use those probabilities to create the prediction for the next word. | + | You may want to consider using a method called teacher forcing to begin connecting source/reference words together. If you decide to use this, for a set probability at each iteration input the embedding of the correct word it should translate instead of the prediction from the previous time step. |
| - | Stop the first time that EOS is predicted. Return the probabilities at each time step and the indices of predicted words. | + | Compute and return the prediction probabilities in either case to be used by the loss function. |
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| + | Continue running the decoder GRU until the max sentence length or EOS is first predicted. Return the probabilities at each time step regardless of whether teacher forcing was used. | ||
| **Part 4:** Loss, test metrics | **Part 4:** Loss, test metrics | ||
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| Calculate accuracy by something similar to (target==reference).data.numpy(), but make sure to compensate for when the target and reference sequences are of different lengths. | Calculate accuracy by something similar to (target==reference).data.numpy(), but make sure to compensate for when the target and reference sequences are of different lengths. | ||
| - | Perplexity is a standard measure for NMT and Language Modelling, it is equivalent to 2^cross_entropy. | + | Consider using perplexity in addition to cross entropy as a test metric. It's standard practice for NMT and Language Modelling and is 2^cross_entropy. |
| **Part 5:** Optimizer | **Part 5:** Optimizer | ||
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| <code python> | <code python> | ||
| - | # compute loss | ||
| - | |||
| loss.backward() | loss.backward() | ||
| - | if j % n == 0: | + | |
| + | if j % batch_size == 0: | ||
| for p in all_parameters: | for p in all_parameters: | ||
| p.grad.div_(n) # in-place | p.grad.div_(n) # in-place | ||
cs501r_f2016/lab14.txt · Last modified: 2021/06/30 23:42 (external edit)
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