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https://issues.apache.org/jira/browse/MXNET-867?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Lin Yuan updated MXNET-867:
---------------------------
Status: In Review (was: In Progress)
> 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: Chaitanya Prakash Bapat
> 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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