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Added a LSTM full utterance training E2E test
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Tests/Speech/LSTM/FullUtterance/baseline.windows.cpu.txt
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Tests/Speech/LSTM/FullUtterance/baseline.windows.gpu.txt
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#!/bin/bash | ||
if [ "$TEST_DEVICE" == "cpu" ]; then | ||
CNTK_DEVICE_ID=-1 | ||
elif [ "$TEST_DEVICE" == "gpu" ]; then | ||
CNTK_DEVICE_ID=0 | ||
else | ||
echo "Error: Unknown TEST_DEVICE specified!" | ||
exit 3 | ||
fi | ||
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configFile=$TEST_DIR/../cntk.config | ||
RunDir=$TEST_RUN_DIR | ||
DataDir=$TEST_DATA_DIR | ||
NDLDir=$TEST_DIR/.. | ||
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if [ "$OS" == "Windows_NT" ]; then | ||
# When running on cygwin translating /cygdrive/xxx paths to proper windows paths: | ||
configFile=$(cygpath -aw $configFile) | ||
RunDir=$(cygpath -aw $RunDir) | ||
DataDir=$(cygpath -aw $DataDir) | ||
NDLDir=$(cygpath -aw $NDLDir) | ||
fi | ||
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CNTK_ARGS="configFile=$configFile RunDir=$RunDir DataDir=$DataDir DeviceId=$CNTK_DEVICE_ID NDLDir=$NDLDir Truncated=false speechTrain=[reader=[nbruttsineachrecurrentiter=1]] speechTrain=[SGD=[epochSize=2560]] speechTrain=[SGD=[maxEpochs=2]] speechTrain=[SGD=[numMBsToShowResult=1]]" | ||
MODELS_DIR=$TEST_RUN_DIR/models | ||
[ -d $MODELS_DIR ] && rm -rf $MODELS_DIR | ||
mkdir -p $MODELS_DIR || exit $? | ||
echo === Running $TEST_CNTK_BINARY $CNTK_ARGS | ||
$TEST_CNTK_BINARY $CNTK_ARGS || exit $? |
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dataDir: ../../Data | ||
tags: | ||
# running on every BVT job in 'L' (LSTM) leg in Debug-GPU and Release-CPU configurations: | ||
- bvt-l (flavor=='debug') ^ (device=='cpu') | ||
# running unconditionally on every Nightly job in 'L' leg | ||
- nightly-l | ||
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testCases: | ||
CNTK Run must be completed: | ||
patterns: | ||
- ^COMPLETED | ||
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Must train epochs in exactly same order and parameters: | ||
patterns: | ||
- ^Starting Epoch {{integer}} | ||
- learning rate per sample = {{float}} | ||
- momentum = {{float}} | ||
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Epochs must be finished with expected results: | ||
patterns: | ||
- ^Finished Epoch[{{integer}} of {{integer}}] | ||
- TrainLossPerSample = {{float,tolerance=.1%}} | ||
- EvalErrPerSample = {{float,tolerance=.1%}} | ||
- Ave LearnRatePerSample = {{float,tolerance=0%}} | ||
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Per-minibatch training results must match: | ||
patterns: | ||
- ^ Epoch[{{integer}} of {{integer}}]-Minibatch[{{integer}}-{{integer}} of {{integer}}] | ||
- SamplesSeen = {{integer}} | ||
- TrainLossPerSample = {{float,tolerance=.1%}} | ||
- EvalErr[0]PerSample = {{float,tolerance=.1%}} | ||
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