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Lin Yuan updated MXNET-867: --------------------------- Status: Done (was: In Review) > 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: 4h > 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 > === -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@mxnet.apache.org For additional commands, e-mail: issues-h...@mxnet.apache.org