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* Modified parameter order of DecoderRNN.forward (#85)
* Updated TopKDecoder (#86)
* Fixed topk decoder.
* Use torchtext from pipy (#87)
* Use torchtext from pipe.
* Fixed torch text sorting order.
* attention is not required when only using teacher forcing in decoder (#90)
* attention is not required when only using teacher forcing in decoder
* Updated docs and version.
* Fixed code style.
* bugfix (#92)
Fixed field arguments validation.
* Removed `initial_lr` when resuming optimizer with scheduler. (#95)
* shuffle the training data (#97)
* 0.1.5 (#91)
* Modified parameter order of DecoderRNN.forward (#85)
* Updated TopKDecoder (#86)
* Fixed topk decoder.
* Use torchtext from pipy (#87)
* Use torchtext from pipe.
* Fixed torch text sorting order.
* attention is not required when only using teacher forcing in decoder (#90)
* attention is not required when only using teacher forcing in decoder
* Updated docs and version.
* Fixed code style.
* shuffle the training data
* fix example of inflate function in TopKDecoer.py (#98)
* fix example of inflate function in TopKDecoer.py
* Fix hidden_layer size for one-directional decoder (#99)
* Fix hidden_layer size for one-directional decoder
Hidden layer size of the decoder was given `hidden_size * 2 if bidirectional else 1`, resulting in a dimensionality error for non-bidirectional decoders.
Changed `1` to `hidden_size`.
* Adapt load to allow CPU loading of GPU models (#100)
* Adapt load to allow CPU loading of GPU models
Add storage parameter to torch.load to allow loading
models on a CPU that are trained on the GPU, depending
on availability of cuda.
* Fix wrong parameter use on DecoderRNN (#103)
* Fix wrong parameter use on DecoderRNN
* Upgrade to pytorch-0.3.0 (#111)
* Upgrade to pytorch-0.3.0
* Use pytorch 3.0 in travis env.
* Make sure tensor contiguous when attention's not used. (#112)
* Implementing the predict_n method. Using the beam search outputs it returns several seqs for a given seq (#116)
* Adding a predictor method to return n predicted seqs for a src_seq input
(intended to be used along to Beam Search using TopKDecoder)
* Checkpoint after batches not epochs (#119)
* Pytorch 0.4 (#134)
* add contiguous call to tensor (#127)
when attention is turned off, pytorch (well, 0.4 at least) gets angry about calling view on a non-contiguous tensor
* Fixed shape documentation (#131)
* Update to pytorch-0.4
* Remove pytorch manual install in travis.
* Allow using pre-trained embedding (#135)
* updated docs
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