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#!/bin/bash |
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model="forward" |
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T=5 |
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num_obs=1 |
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var=0.1 |
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n=4 |
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lr=0.0001 |
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bound="fivo-aux" |
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q_type="normal" |
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resampling_method="multinomial" |
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rgrad="true" |
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p_type="unimodal" |
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use_bs=false |
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LOGDIR=/tmp/fivo/model-$model-$bound-$resampling_method-resampling-rgrad-$rgrad-T-$T-var-$var-n-$n-lr-$lr-q-$q_type-p-$p_type |
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python train.py \ |
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--logdir=$LOGDIR \ |
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--model=$model \ |
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--bound=$bound \ |
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--q_type=$q_type \ |
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--p_type=$p_type \ |
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--variance=$var \ |
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--use_resampling_grads=$rgrad \ |
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--resampling=always \ |
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--resampling_method=$resampling_method \ |
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--batch_size=4 \ |
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--num_samples=$n \ |
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--num_timesteps=$T \ |
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--num_eval_samples=256 \ |
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--summarize_every=100 \ |
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--learning_rate=$lr \ |
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--decay_steps=1000000 \ |
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--max_steps=1000000000 \ |
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--random_seed=1234 \ |
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--train_p=false \ |
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--use_bs=$use_bs \ |
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--alsologtostderr |
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