{"action":"created","issue":{"url":"https://api.github.com/repos/tensorflow/tensorflow/issues/15323","repository_url":"https://api.github.com/repos/tensorflow/tensorflow","labels_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/15323/labels{/name}","comments_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/15323/comments","events_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/15323/events","html_url":"https://github.com/tensorflow/tensorflow/issues/15323","id":281548050,"node_id":"MDU6SXNzdWUyODE1NDgwNTA=","number":15323,"title":"Beam Search Decoder API","user":{"login":"eaplatanios","id":1294940,"node_id":"MDQ6VXNlcjEyOTQ5NDA=","avatar_url":"https://avatars2.githubusercontent.com/u/1294940?v=4","gravatar_id":"","url":"https://api.github.com/users/eaplatanios","html_url":"https://github.com/eaplatanios","followers_url":"https://api.github.com/users/eaplatanios/followers","following_url":"https://api.github.com/users/eaplatanios/following{/other_user}","gists_url":"https://api.github.com/users/eaplatanios/gists{/gist_id}","starred_url":"https://api.github.com/users/eaplatanios/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/eaplatanios/subscriptions","organizations_url":"https://api.github.com/users/eaplatanios/orgs","repos_url":"https://api.github.com/users/eaplatanios/repos","events_url":"https://api.github.com/users/eaplatanios/events{/privacy}","received_events_url":"https://api.github.com/users/eaplatanios/received_events","type":"User","site_admin":false},"labels":[{"id":473173272,"node_id":"MDU6TGFiZWw0NzMxNzMyNzI=","url":"https://api.github.com/repos/tensorflow/tensorflow/labels/type:feature","name":"type:feature","color":"159b2e","default":false}],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":10,"created_at":"2017-12-12T21:45:03Z","updated_at":"2018-11-01T17:49:05Z","closed_at":"2018-01-30T01:55:50Z","author_association":"CONTRIBUTOR","body":"@ebrevdo Why is it that the user needs to call `tile_batch` explicitly for beam search decoders when using attention models? Couldn't the beam search decoder internally tile the provided `initial_state` in its constructor? It seems that this API is prone to wrong usage so I'm trying to understand why it's necessary.\r\n\r\nThank you!"},"comment":{"url":"https://api.github.com/repos/tensorflow/tensorflow/issues/comments/435125823","html_url":"https://github.com/tensorflow/tensorflow/issues/15323#issuecomment-435125823","issue_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/15323","id":435125823,"node_id":"MDEyOklzc3VlQ29tbWVudDQzNTEyNTgyMw==","user":{"login":"ebrevdo","id":1794715,"node_id":"MDQ6VXNlcjE3OTQ3MTU=","avatar_url":"https://avatars0.githubusercontent.com/u/1794715?v=4","gravatar_id":"","url":"https://api.github.com/users/ebrevdo","html_url":"https://github.com/ebrevdo","followers_url":"https://api.github.com/users/ebrevdo/followers","following_url":"https://api.github.com/users/ebrevdo/following{/other_user}","gists_url":"https://api.github.com/users/ebrevdo/gists{/gist_id}","starred_url":"https://api.github.com/users/ebrevdo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ebrevdo/subscriptions","organizations_url":"https://api.github.com/users/ebrevdo/orgs","repos_url":"https://api.github.com/users/ebrevdo/repos","events_url":"https://api.github.com/users/ebrevdo/events{/privacy}","received_events_url":"https://api.github.com/users/ebrevdo/received_events","type":"User","site_admin":false},"created_at":"2018-11-01T17:49:04Z","updated_at":"2018-11-01T17:49:04Z","author_association":"CONTRIBUTOR","body":"Thanks for this suggestion!  I will see if we have some cycles to try this\nout.  We may try to make this change in the transition to TF 2.0; where we\ncan make backwards breaking changes in the API.\n\n+scott, james\n\nOn Thu, Nov 1, 2018 at 9:33 AM, Anthony Platanios <notifications@github.com>\nwrote:\n\n> @ebrevdo <https://github.com/ebrevdo> The actual commit is this\n> <https://github.com/eaplatanios/tensorflow_scala/commit/8efc1069d0a29ae92faf9a5eb3864c92ffbacc3b>,\n> however, it might be hard to follow if you are not familiar with Scala. In\n> summary, the changes were the following:\n>\n>    - When constructing attention mechanisms, no memory needs to be\n>    provided, as shown in this diff\n>    <https://github.com/eaplatanios/tensorflow_scala/commit/8efc1069d0a29ae92faf9a5eb3864c92ffbacc3b#diff-ef76330f5dbe0574036286de5f6430f8>.\n>    In order to construct the initial attention state, a batch size needs to be\n>    provided and in order to compute an alignment, some previous attention\n>    state needs to be provided, that contains the keys, values, previous\n>    attention state, and optionally sequence lengths. It is the job of the\n>    attention wrapper cell to provide those, as described next.\n>    - When constructed, the attention wrapper cell takes as input pairs of\n>    memories, and attention mechanisms (instead of just attention mechanisms),\n>    as shown in this diff\n>    <https://github.com/eaplatanios/tensorflow_scala/commit/8efc1069d0a29ae92faf9a5eb3864c92ffbacc3b#diff-e5b5a14ee310ebfe2ede73ae49f8412e>.\n>    These memories are used to initialize the attention states, as shown\n>    here\n>    <https://github.com/eaplatanios/tensorflow_scala/commit/8efc1069d0a29ae92faf9a5eb3864c92ffbacc3b#diff-e5b5a14ee310ebfe2ede73ae49f8412eR115>,\n>    which are part of the RNN cell state and can thus be tiled automatically by\n>    the beam search decoder.\n>\n> Note that for the tiling I do recursion on the type structures of the cell\n> state, at compile-time, but something similar could be done in Python using\n> the TF nest module.\n>\n> Does this help? Please let me know and if needed I could provide more\n> detail. :)\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/15323#issuecomment-435095669>,\n> or mute the thread\n> <https://github.com/notifications/unsubscribe-auth/ABtimzrJUXDEwnmvZ-TVqmCJ3hkkYif_ks5uqyJVgaJpZM4Q_reC>\n> .\n>\n"}}
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