{"action":"created","issue":{"url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf/issues/3","repository_url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf","labels_url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf/issues/3/comments","events_url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf/issues/3/events","html_url":"https://github.com/OpenNMT/OpenNMT-tf/issues/3","id":270652539,"number":3,"title":"Convolutional sequence to sequence","user":{"login":"guillaumekln","id":4805513,"avatar_url":"https://avatars3.githubusercontent.com/u/4805513?v=4","gravatar_id":"","url":"https://api.github.com/users/guillaumekln","html_url":"https://github.com/guillaumekln","followers_url":"https://api.github.com/users/guillaumekln/followers","following_url":"https://api.github.com/users/guillaumekln/following{/other_user}","gists_url":"https://api.github.com/users/guillaumekln/gists{/gist_id}","starred_url":"https://api.github.com/users/guillaumekln/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/guillaumekln/subscriptions","organizations_url":"https://api.github.com/users/guillaumekln/orgs","repos_url":"https://api.github.com/users/guillaumekln/repos","events_url":"https://api.github.com/users/guillaumekln/events{/privacy}","received_events_url":"https://api.github.com/users/guillaumekln/received_events","type":"User","site_admin":false},"labels":[{"id":738488477,"url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf/labels/stat:contributions%20welcome","name":"stat:contributions welcome","color":"33aa3f","default":false},{"id":738488476,"url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf/labels/type:feature","name":"type:feature","color":"84b6eb","default":false}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2017-11-02T13:27:54Z","updated_at":"2018-04-10T13:31:58Z","closed_at":null,"author_association":"OWNER","body":"Add a new encoder-decoder model to implement \"Convolutional Sequence to Sequence Learning\" (https://arxiv.org/abs/1705.03122)."},"comment":{"url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf/issues/comments/380099008","html_url":"https://github.com/OpenNMT/OpenNMT-tf/issues/3#issuecomment-380099008","issue_url":"https://api.github.com/repos/OpenNMT/OpenNMT-tf/issues/3","id":380099008,"user":{"login":"guillaumekln","id":4805513,"avatar_url":"https://avatars3.githubusercontent.com/u/4805513?v=4","gravatar_id":"","url":"https://api.github.com/users/guillaumekln","html_url":"https://github.com/guillaumekln","followers_url":"https://api.github.com/users/guillaumekln/followers","following_url":"https://api.github.com/users/guillaumekln/following{/other_user}","gists_url":"https://api.github.com/users/guillaumekln/gists{/gist_id}","starred_url":"https://api.github.com/users/guillaumekln/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/guillaumekln/subscriptions","organizations_url":"https://api.github.com/users/guillaumekln/orgs","repos_url":"https://api.github.com/users/guillaumekln/repos","events_url":"https://api.github.com/users/guillaumekln/events{/privacy}","received_events_url":"https://api.github.com/users/guillaumekln/received_events","type":"User","site_admin":false},"created_at":"2018-04-10T13:31:58Z","updated_at":"2018-04-10T13:31:58Z","author_association":"OWNER","body":"Yes it does. You can provide a model configuration that looks like this:\r\n\r\n```python\r\nimport tensorflow as tf\r\nimport opennmt as onmt\r\n\r\ndef model():\r\n  return onmt.models.SequenceToSequence(\r\n      source_inputter=onmt.inputters.WordEmbedder(\r\n          vocabulary_file_key=\"source_words_vocabulary\",\r\n          embedding_size=512),\r\n      target_inputter=onmt.inputters.WordEmbedder(\r\n          vocabulary_file_key=\"target_words_vocabulary\",\r\n          embedding_size=512),\r\n      encoder=onmt.encoders.ConvEncoder(\r\n          num_layers=4,\r\n          num_units=1024,\r\n          kernel_size=3,\r\n          dropout=0.3,\r\n          position_encoder=onmt.layers.PositionEmbedder(maximum_position=128)),\r\n      decoder=onmt.decoders.AttentionalRNNDecoder(\r\n          num_layers=4,\r\n          num_units=1024,\r\n          bridge=onmt.layers.DenseBridge(),\r\n          attention_mechanism_class=tf.contrib.seq2seq.LuongAttention,\r\n          cell_class=tf.contrib.rnn.LSTMCell,\r\n          dropout=0.3,\r\n          residual_connections=False))\r\n```\r\n\r\nSee the [documentation](http://opennmt.net/OpenNMT-tf/package/opennmt.html) for more information about each building blocks."}}
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