{"action":"opened","issue":{"url":"https://api.github.com/repos/OpenNMT/OpenNMT-py/issues/820","repository_url":"https://api.github.com/repos/OpenNMT/OpenNMT-py","labels_url":"https://api.github.com/repos/OpenNMT/OpenNMT-py/issues/820/labels{/name}","comments_url":"https://api.github.com/repos/OpenNMT/OpenNMT-py/issues/820/comments","events_url":"https://api.github.com/repos/OpenNMT/OpenNMT-py/issues/820/events","html_url":"https://github.com/OpenNMT/OpenNMT-py/issues/820","id":340283857,"node_id":"MDU6SXNzdWUzNDAyODM4NTc=","number":820,"title":"Documentation out of date for summarization","user":{"login":"silent-vim","id":5408709,"node_id":"MDQ6VXNlcjU0MDg3MDk=","avatar_url":"https://avatars1.githubusercontent.com/u/5408709?v=4","gravatar_id":"","url":"https://api.github.com/users/silent-vim","html_url":"https://github.com/silent-vim","followers_url":"https://api.github.com/users/silent-vim/followers","following_url":"https://api.github.com/users/silent-vim/following{/other_user}","gists_url":"https://api.github.com/users/silent-vim/gists{/gist_id}","starred_url":"https://api.github.com/users/silent-vim/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/silent-vim/subscriptions","organizations_url":"https://api.github.com/users/silent-vim/orgs","repos_url":"https://api.github.com/users/silent-vim/repos","events_url":"https://api.github.com/users/silent-vim/events{/privacy}","received_events_url":"https://api.github.com/users/silent-vim/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-07-11T15:04:36Z","updated_at":"2018-07-11T15:04:36Z","closed_at":null,"author_association":"NONE","body":"Hi, when trying to run the summarization example with CNN/DM dataset the training abruptly finishes without any errors. The documentation seems to be working with commit [0397acd](https://github.com/OpenNMT/OpenNMT-py/commit/0397acda33531889dd2d62984b162305d48d9d10#diff-86631deba6b1df4b7aab7c6da70c7c5d) when it was last updated. \r\n\r\nThe same command mentioned on the examples page proceeds gradually through training and gives expected results through inference. \r\n\r\nIs there an example of summarization with the recent code changes since commit 0397acd. \r\n\r\nThank you.\r\n\r\n## Logs for latest master branch \r\nTraining finishes within 5 min at the last statement and model is not saved unless you change save_checkpoint flag\r\n \r\n```\r\n[2018-07-11 06:57:39,737 INFO] Loading train dataset from data/cnndm/CNNDM.train.1.pt, number of examples: 126095\r\n[2018-07-11 06:57:39,737 INFO] Loading train dataset from data/cnndm/CNNDM.train.1.pt, number of examples: 126095\r\n[2018-07-11 06:57:40,103 INFO]  * vocabulary size. source = 50004; target = 50004\r\n[2018-07-11 06:57:40,103 INFO]  * vocabulary size. source = 50004; target = 50004\r\n[2018-07-11 06:57:40,103 INFO] Building model...\r\n[2018-07-11 06:57:40,103 INFO] Building model...\r\n[2018-07-11 06:57:45,213 INFO] NMTModel(\r\n  (encoder): RNNEncoder(\r\n    (embeddings): Embeddings(\r\n      (make_embedding): Sequential(\r\n        (emb_luts): Elementwise(\r\n          (0): Embedding(50004, 128, padding_idx=1)\r\n        )\r\n      )\r\n    )\r\n    (rnn): LSTM(128, 256, bidirectional=True)\r\n    (bridge): ModuleList(\r\n      (0): Linear(in_features=256, out_features=256, bias=True)\r\n      (1): Linear(in_features=256, out_features=256, bias=True)\r\n    )\r\n  )\r\n  (decoder): InputFeedRNNDecoder(\r\n    (embeddings): Embeddings(\r\n      (make_embedding): Sequential(\r\n        (emb_luts): Elementwise(\r\n          (0): Embedding(50004, 128, padding_idx=1)\r\n        )\r\n      )\r\n    )\r\n    (dropout): Dropout(p=0.0)\r\n    (rnn): StackedLSTM(\r\n      (dropout): Dropout(p=0.0)\r\n      (layers): ModuleList(\r\n        (0): LSTMCell(640, 512)\r\n      )\r\n    )\r\n    (attn): GlobalAttention(\r\n      (linear_context): Linear(in_features=512, out_features=512, bias=False)\r\n      (linear_query): Linear(in_features=512, out_features=512, bias=True)\r\n      (v): Linear(in_features=512, out_features=1, bias=False)\r\n      (linear_out): Linear(in_features=1024, out_features=512, bias=True)\r\n      (softmax): Softmax()\r\n      (tanh): Tanh()\r\n    )\r\n  )\r\n  (generator): CopyGenerator(\r\n    (linear): Linear(in_features=512, out_features=50004, bias=True)\r\n    (linear_copy): Linear(in_features=512, out_features=1, bias=True)\r\n    (softmax): Softmax()\r\n    (sigmoid): Sigmoid()\r\n  )\r\n)\r\n[2018-07-11 06:57:45,213 INFO] NMTModel(\r\n  (encoder): RNNEncoder(\r\n    (embeddings): Embeddings(\r\n      (make_embedding): Sequential(\r\n        (emb_luts): Elementwise(\r\n          (0): Embedding(50004, 128, padding_idx=1)\r\n        )\r\n      )\r\n    )\r\n    (rnn): LSTM(128, 256, bidirectional=True)\r\n    (bridge): ModuleList(\r\n      (0): Linear(in_features=256, out_features=256, bias=True)\r\n      (1): Linear(in_features=256, out_features=256, bias=True)\r\n    )\r\n  )\r\n  (decoder): InputFeedRNNDecoder(\r\n    (embeddings): Embeddings(\r\n      (make_embedding): Sequential(\r\n        (emb_luts): Elementwise(\r\n          (0): Embedding(50004, 128, padding_idx=1)\r\n        )\r\n      )\r\n    )\r\n    (dropout): Dropout(p=0.0)\r\n    (rnn): StackedLSTM(\r\n      (dropout): Dropout(p=0.0)\r\n      (layers): ModuleList(\r\n        (0): LSTMCell(640, 512)\r\n      )\r\n    )\r\n    (attn): GlobalAttention(\r\n      (linear_context): Linear(in_features=512, out_features=512, bias=False)\r\n      (linear_query): Linear(in_features=512, out_features=512, bias=True)\r\n      (v): Linear(in_features=512, out_features=1, bias=False)\r\n      (linear_out): Linear(in_features=1024, out_features=512, bias=True)\r\n      (softmax): Softmax()\r\n      (tanh): Tanh()\r\n    )\r\n  )\r\n  (generator): CopyGenerator(\r\n    (linear): Linear(in_features=512, out_features=50004, bias=True)\r\n    (linear_copy): Linear(in_features=512, out_features=1, bias=True)\r\n    (softmax): Softmax()\r\n    (sigmoid): Sigmoid()\r\n  )\r\n)\r\n[2018-07-11 06:57:45,215 INFO] encoder: 7322624\r\n[2018-07-11 06:57:45,215 INFO] encoder: 7322624\r\n[2018-07-11 06:57:45,215 INFO] decoder: 35466581\r\n[2018-07-11 06:57:45,215 INFO] decoder: 35466581\r\n[2018-07-11 06:57:45,215 INFO] * number of parameters: 42789205\r\n[2018-07-11 06:57:45,215 INFO] * number of parameters: 42789205\r\n[2018-07-11 06:57:45,219 INFO] Start training...\r\n[2018-07-11 06:57:45,219 INFO] Start training...\r\n[2018-07-11 06:58:31,024 INFO] Loading train dataset from data/cnndm/CNNDM.train.1.pt, number of examples: 126095\r\n[2018-07-11 06:58:31,024 INFO] Loading train dataset from data/cnndm/CNNDM.train.1.pt, number of examples: 126095\r\n[2018-07-11 06:59:38,632 INFO] Loading train dataset from data/cnndm/CNNDM.train.1.pt, number of examples: 126095\r\n[2018-07-11 06:59:38,632 INFO] Loading train dataset from data/cnndm/CNNDM.train.1.pt, number of examples: 126095\r\n```\r\n\r\n## Logs for working commit\r\nTraining continues through all epochs and model checkpoints are saved.\r\n```\r\n* number of parameters: 42789205\r\nencoder:  7322624\r\ndecoder:  35466581\r\nMaking optimizer for training.\r\n\r\nStart training...\r\n * number of epochs: 20, starting from Epoch 1\r\n * batch size: 16\r\nEpoch  1,    50/ 7881; acc:   4.81; ppl: 860.66; xent:   6.76; 6527 src tok/s; 975 tgt tok/s;     47 s elapsed\r\nEpoch  1,   100/ 7881; acc:  10.06; ppl: 379.20; xent:   5.94; 6780 src tok/s; 971 tgt tok/s;     92 s elapsed\r\nEpoch  1,   150/ 7881; acc:  16.65; ppl: 259.27; xent:   5.56; 6801 src tok/s; 975 tgt tok/s;    137 s elapsed\r\nEpoch  1,   200/ 7881; acc:  17.30; ppl: 251.41; xent:   5.53; 6361 src tok/s; 957 tgt tok/s;    185 s elapsed\r\nEpoch  1,   250/ 7881; acc:  17.91; ppl: 227.53; xent:   5.43; 6621 src tok/s; 942 tgt tok/s;    231 s elapsed\r\nEpoch  1,   300/ 7881; acc:  17.92; ppl: 238.38; xent:   5.47; 6573 src tok/s; 975 tgt tok/s;    278 s elapsed\r\nEpoch  1,   350/ 7881; acc:  17.83; ppl: 253.62; xent:   5.54; 6478 src tok/s; 983 tgt tok/s;    326 s elapsed\r\nEpoch  1,   400/ 7881; acc:  18.05; ppl: 213.80; xent:   5.37; 6726 src tok/s; 962 tgt tok/s;    371 s elapsed\r\nEpoch  1,   450/ 7881; acc:  18.07; ppl: 213.01; xent:   5.36; 6697 src tok/s; 958 tgt tok/s;    416 s elapsed\r\nEpoch  1,   500/ 7881; acc:  18.20; ppl: 217.72; xent:   5.38; 6520 src tok/s; 975 tgt tok/s;    464 s elapsed\r\nEpoch  1,   550/ 7881; acc:  18.56; ppl: 188.91; xent:   5.24; 6521 src tok/s; 958 tgt tok/s;    510 s elapsed\r\n```"}}
{"actor":{"display_login":"silent-vim"}}