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From "Lin Yuan (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MXNET-867) Pooling1D with "same" padding
Date Wed, 19 Sep 2018 05:26:00 GMT

     [ https://issues.apache.org/jira/browse/MXNET-867?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Lin Yuan updated MXNET-867:
---------------------------
    Assignee: Lin Yuan  (was: Chaitanya Prakash Bapat)
      Status: In Progress  (was: To Do)

> Pooling1D with "same" padding
> -----------------------------
>
>                 Key: MXNET-867
>                 URL: https://issues.apache.org/jira/browse/MXNET-867
>             Project: Apache MXNet
>          Issue Type: New Feature
>          Components: Apache MXNet Backend
>            Reporter: Lin Yuan
>            Assignee: Lin Yuan
>            Priority: Major
>          Time Spent: 0.5h
>  Remaining Estimate: 0h
>
> Hi, 
> I need to implement an encoder for a speech recognition model in MXNet that uses a 1D
temporal max pooling layer with 'same' padding between successive Bidirectional LSTM layers
(as below). Currently, there is no support for 1D max pooling with same padding in MXNet -
https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symb... . 
> Could you please implement the required max pooling with 'same' padding support and advise
on how to implement the following encoder model in MXNet? 
> Thanks, 
> Sundeep 
> === 
> # network 
> target = "classes" 
> EncKeyTotalDim = 1024 
> AttNumHeads = 1 
> EncKeyPerHeadDim = EncKeyTotalDim // AttNumHeads 
> EncValueTotalDim = 2048 
> EncValuePerHeadDim = EncValueTotalDim // AttNumHeads 
> LstmDim = EncValueTotalDim // 2 
> network = { 
> "source": {"class": "eval", "eval": "tf.clip_by_value(source(0), -3.0, 3.0)"}, 
> "lstm0_fw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
1, "from": ["source"] }, 
> "lstm0_bw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
-1, "from": ["source"] }, 
> "lstm0_pool": {"class": "pool", "mode": "max", "padding": "same", "pool_size": (2,),
"from": ["lstm0_fw", "lstm0_bw"], "trainable": False}, 
> "lstm1_fw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
1, "from": ["lstm0_pool"], "dropout": 0.3 }, 
> "lstm1_bw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
-1, "from": ["lstm0_pool"], "dropout": 0.3 }, 
> "lstm1_pool": {"class": "pool", "mode": "max", "padding": "same", "pool_size": (2,),
"from": ["lstm1_fw", "lstm1_bw"], "trainable": False}, 
> "lstm2_fw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
1, "from": ["lstm1_pool"], "dropout": 0.3 }, 
> "lstm2_bw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
-1, "from": ["lstm1_pool"], "dropout": 0.3 }, 
> "lstm2_pool": {"class": "pool", "mode": "max", "padding": "same", "pool_size": (2,),
"from": ["lstm2_fw", "lstm2_bw"], "trainable": False}, 
> "lstm3_fw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
1, "from": ["lstm2_pool"], "dropout": 0.3 }, 
> "lstm3_bw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
-1, "from": ["lstm2_pool"], "dropout": 0.3 }, 
> "lstm3_pool": {"class": "pool", "mode": "max", "padding": "same", "pool_size": (1,),
"from": ["lstm3_fw", "lstm3_bw"], "trainable": False}, 
> "lstm4_fw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
1, "from": ["lstm3_pool"], "dropout": 0.3 }, 
> "lstm4_bw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
-1, "from": ["lstm3_pool"], "dropout": 0.3 }, 
> "lstm4_pool": {"class": "pool", "mode": "max", "padding": "same", "pool_size": (1,),
"from": ["lstm4_fw", "lstm4_bw"], "trainable": False}, 
> "lstm5_fw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
1, "from": ["lstm4_pool"], "dropout": 0.3 }, 
> "lstm5_bw" : { "class": "rec", "unit": "nativelstm2", "n_out" : LstmDim, "direction":
-1, "from": ["lstm4_pool"], "dropout": 0.3 }, 
> "encoder": {"class": "copy", "from": ["lstm5_fw", "lstm5_bw"]}, # dim: EncValueTotalDim

> === 



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