Merge pull request #219 from xinghai-sun/network_bug

Fixed a serious error of bidirectional simple rnn for DS2.
pull/2/head
Cao Ying 7 years ago committed by GitHub
commit 3b12525008

@ -1,6 +1,6 @@
TRAIN_MANIFEST="cloud/cloud.manifest.train" TRAIN_MANIFEST="cloud/cloud.manifest.train"
DEV_MANIFEST="cloud/cloud.manifest.dev" DEV_MANIFEST="cloud/cloud.manifest.dev"
CLOUD_MODEL_DIR="/pfs/dlnel/home/USERNAME/deepspeech2/model" CLOUD_MODEL_DIR="./checkpoints"
BATCH_SIZE=256 BATCH_SIZE=256
NUM_GPU=8 NUM_GPU=8
NUM_NODE=1 NUM_NODE=1
@ -11,7 +11,7 @@ DS2_PATH=${PWD%/*}
cp -f pcloud_train.sh ${DS2_PATH} cp -f pcloud_train.sh ${DS2_PATH}
paddlecloud submit \ paddlecloud submit \
-image bootstrapper:5000/wanghaoshuang/pcloud_ds2:latest \ -image bootstrapper:5000/paddlepaddle/pcloud_ds2:latest \
-jobname ${JOB_NAME} \ -jobname ${JOB_NAME} \
-cpu ${NUM_GPU} \ -cpu ${NUM_GPU} \
-gpu ${NUM_GPU} \ -gpu ${NUM_GPU} \

@ -55,16 +55,20 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, act):
:rtype: LayerOutput :rtype: LayerOutput
""" """
# input-hidden weights shared across bi-direcitonal rnn. # input-hidden weights shared across bi-direcitonal rnn.
input_proj = paddle.layer.fc( input_proj_forward = paddle.layer.fc(
input=input, size=size, act=paddle.activation.Linear(), bias_attr=False) input=input, size=size, act=paddle.activation.Linear(), bias_attr=False)
# batch norm is only performed on input-state projection input_proj_backward = paddle.layer.fc(
input_proj_bn = paddle.layer.batch_norm( input=input, size=size, act=paddle.activation.Linear(), bias_attr=False)
input=input_proj, act=paddle.activation.Linear()) # batch norm is only performed on input-state projection
input_proj_bn_forward = paddle.layer.batch_norm(
input=input_proj_forward, act=paddle.activation.Linear())
input_proj_bn_backward = paddle.layer.batch_norm(
input=input_proj_backward, act=paddle.activation.Linear())
# forward and backward in time # forward and backward in time
forward_simple_rnn = paddle.layer.recurrent( forward_simple_rnn = paddle.layer.recurrent(
input=input_proj_bn, act=act, reverse=False) input=input_proj_bn_forward, act=act, reverse=False)
backward_simple_rnn = paddle.layer.recurrent( backward_simple_rnn = paddle.layer.recurrent(
input=input_proj_bn, act=act, reverse=True) input=input_proj_bn_backward, act=act, reverse=True)
return paddle.layer.concat(input=[forward_simple_rnn, backward_simple_rnn]) return paddle.layer.concat(input=[forward_simple_rnn, backward_simple_rnn])

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