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* Fixed topk decoder.
* Use torchtext from pipe. * Fixed torch text sorting order.
…BM#90) * attention is not required when only using teacher forcing in decoder
* 0.1.5 (IBM#91) * Modified parameter order of DecoderRNN.forward (IBM#85) * Updated TopKDecoder (IBM#86) * Fixed topk decoder. * Use torchtext from pipy (IBM#87) * Use torchtext from pipe. * Fixed torch text sorting order. * attention is not required when only using teacher forcing in decoder (IBM#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
* 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 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
# Conflicts: # seq2seq/models/TopKDecoder.py # seq2seq/trainer/supervised_trainer.py
* Modified parameter order of DecoderRNN.forward (IBM#85) * Updated TopKDecoder (IBM#86) * Fixed topk decoder. * Use torchtext from pipy (IBM#87) * Use torchtext from pipe. * Fixed torch text sorting order. * attention is not required when only using teacher forcing in decoder (IBM#90) * attention is not required when only using teacher forcing in decoder * Updated docs and version. * Fixed code style. * bugfix (IBM#92) Fixed field arguments validation. * Removed `initial_lr` when resuming optimizer with scheduler. (IBM#95) * shuffle the training data (IBM#97) * 0.1.5 (IBM#91) * Modified parameter order of DecoderRNN.forward (IBM#85) * Updated TopKDecoder (IBM#86) * Fixed topk decoder. * Use torchtext from pipy (IBM#87) * Use torchtext from pipe. * Fixed torch text sorting order. * attention is not required when only using teacher forcing in decoder (IBM#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 (IBM#98) * fix example of inflate function in TopKDecoer.py * Fix hidden_layer size for one-directional decoder (IBM#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 (IBM#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 (IBM#103) * Fix wrong parameter use on DecoderRNN
* Upgrade to pytorch-0.3.0 * Use pytorch 3.0 in travis env.
…eturns several seqs for a given seq (IBM#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)
when attention is turned off, pytorch (well, 0.4 at least) gets angry about calling view on a non-contiguous tensor
* Modified parameter order of DecoderRNN.forward (IBM#85) * Updated TopKDecoder (IBM#86) * Fixed topk decoder. * Use torchtext from pipy (IBM#87) * Use torchtext from pipe. * Fixed torch text sorting order. * attention is not required when only using teacher forcing in decoder (IBM#90) * attention is not required when only using teacher forcing in decoder * Updated docs and version. * Fixed code style. * bugfix (IBM#92) Fixed field arguments validation. * Removed `initial_lr` when resuming optimizer with scheduler. (IBM#95) * shuffle the training data (IBM#97) * 0.1.5 (IBM#91) * Modified parameter order of DecoderRNN.forward (IBM#85) * Updated TopKDecoder (IBM#86) * Fixed topk decoder. * Use torchtext from pipy (IBM#87) * Use torchtext from pipe. * Fixed torch text sorting order. * attention is not required when only using teacher forcing in decoder (IBM#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 (IBM#98) * fix example of inflate function in TopKDecoer.py * Fix hidden_layer size for one-directional decoder (IBM#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 (IBM#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 (IBM#103) * Fix wrong parameter use on DecoderRNN * Upgrade to pytorch-0.3.0 (IBM#111) * Upgrade to pytorch-0.3.0 * Use pytorch 3.0 in travis env. * Make sure tensor contiguous when attention's not used. (IBM#112) * Implementing the predict_n method. Using the beam search outputs it returns several seqs for a given seq (IBM#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 (IBM#119) * Pytorch 0.4 (IBM#134) * add contiguous call to tensor (IBM#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 (IBM#131) * Update to pytorch-0.4 * Remove pytorch manual install in travis. * Allow using pre-trained embedding (IBM#135) * updated docs
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copy_decoderdef and minor changes.TODO: fix dimensions for compatibility with
top_k_decoder.