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Added num_encoder_layers/num_decoder_layers to WMT16 standard hparams. #269
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to allow for flexibility in extending models. Clean up and factor train.py PiperOrigin-RevId: 180703151
…points PiperOrigin-RevId: 180960478
(a) During inference, given --ckpt, we can try to load hparams in the same dir (b) When loading models and override_loaded_hparams=False, we still overwrite ["beam_width", "length_penalty_weight", "sampling_temperature", "num_translations_per_input"] (c) Introduce _add_argument to smartly add argument to hparams, so extend_hparams can be called when loading hparams. This is useful for old checkpoints. (d) Handle old checkpoints before the separation of num_layers into num_encoder_layers and num_decoder_layers. Minor clean-ups of misc_utils.py. PiperOrigin-RevId: 180989949
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Update attention_model.py so that we can specify GNMT encoder without attention. PiperOrigin-RevId: 181117151
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…ferCheckpoint(); Rename ckpt into ckpt_path in inference.py and model_helper.py PiperOrigin-RevId: 181260899
op.device actually returns what the user requested not the actual device. This can be misleading as it can return "GPU0" even if no GPU is available. For context see: tensorflow/tensorflow#1344 PiperOrigin-RevId: 181261953
Rename _get_best_results to get_best_results. Update avg_grad_norm computation to divide by the number of examples instead. PiperOrigin-RevId: 181346178
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…oder. PiperOrigin-RevId: 182426171
…inference. PiperOrigin-RevId: 182960914
Add an option include_embeddings to allow for appending embedding layer in front of encoder state list. Properly handle the case when time_major=True. PiperOrigin-RevId: 183117301
Useful when vocab size is very large. PiperOrigin-RevId: 183184262
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…ders tensors. PiperOrigin-RevId: 183781004
- Allow the construction of encoders from sequences different from the default source sequence. - Cleanups. PiperOrigin-RevId: 184301964
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Minor updates to nmt.py to print logging info on embedding files PiperOrigin-RevId: 185313574
…e entry that doesn't have the correct size. Handle attention_architecture == "" same as attention_architecture == "standard". Use separate embedding partitioner for encoder and decoder. PiperOrigin-RevId: 185489121
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Added a implicit flag extract_encoder_layers to get intermediate layers from GNMT models and skip decoder. PiperOrigin-RevId: 191678516
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This is based on the fix #265