#!/bin/bash
# Script for running icml gridworld experiments with VIN networks
# 16x16 map

#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied_02_data.pk --epochs 30 --model valIterBatchUntied --stepsize 0.01 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --data_fraction 0.2 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied_02_data.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied_02_data.pk --epochs 30 --model valIterBatchUntied --stepsize 0.005 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied_02_data.pk --data_fraction 0.2 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied_02_data.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied_02_data.pk --epochs 30 --model valIterBatchUntied --stepsize 0.002 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied_02_data.pk --data_fraction 0.2 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied_02_data.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied_02_data.pk --epochs 30 --model valIterBatchUntied --stepsize 0.001 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied_02_data.pk --data_fraction 0.2 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied_02_data.txt ;

#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_02_data.pk --epochs 30 --model valIterBatch --stepsize 0.01 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --data_fraction 0.2 | tee -a ./nips16results/gridworld/out_grid16_VIN_02_data.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_02_data.pk --epochs 30 --model valIterBatch --stepsize 0.005 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_02_data.pk --data_fraction 0.2 | tee -a ./nips16results/gridworld/out_grid16_VIN_02_data.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_02_data.pk --epochs 30 --model valIterBatch --stepsize 0.002 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_02_data.pk --data_fraction 0.2 | tee -a ./nips16results/gridworld/out_grid16_VIN_02_data.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_02_data.pk --epochs 30 --model valIterBatch --stepsize 0.001 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_02_data.pk --data_fraction 0.2 | tee -a ./nips16results/gridworld/out_grid16_VIN_02_data.txt ;

THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied_05_data.pk --epochs 30 --model valIterBatchUntied --stepsize 0.01 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied_05_data.txt ; 
THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied_05_data.pk --epochs 30 --model valIterBatchUntied --stepsize 0.005 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied_05_data.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied_05_data.txt ; 
THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied_05_data.pk --epochs 30 --model valIterBatchUntied --stepsize 0.005 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied_05_data.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied_05_data.txt ; 
THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_untied_05_data.pk --epochs 30 --model valIterBatchUntied --stepsize 0.001 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_untied_05_data.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_VIN_untied_05_data.txt ;

THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_05_data.pk --epochs 30 --model valIterBatch --stepsize 0.01 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_VIN_05_data.txt ; 
THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_05_data.pk --epochs 30 --model valIterBatch --stepsize 0.005 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_05_data.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_VIN_05_data.txt ; 
THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_05_data.pk --epochs 30 --model valIterBatch --stepsize 0.005 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_05_data.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_VIN_05_data.txt ; 
THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_VIN_05_data.pk --epochs 30 --model valIterBatch --stepsize 0.001 --imsize 16 --reg 0.0 --k 20 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_VIN_05_data.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_VIN_05_data.txt ;
