#!/bin/bash
# Script for running icml gridworld experiments with FCN networks
# 8x8 map
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_8.mat --output ./nips16results/gridworld/grid8_FCN.pk --epochs 30 --model fcn --stepsize 0.01 --imsize 8 --reg 0.0 --batchsize 12 --statebatchsize 10 | tee -a ./nips16results/gridworld/out_grid8_FCN.txt ; 
THEANO_FLAGS='floatX=float32,device=cpu' python NN_run_training.py --input ./data/gridworld_8.mat --output ./nips16results/gridworld/grid8_FCN.pk --epochs 30 --model fcn --stepsize 0.005 --imsize 8 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid8_FCN.pk | tee -a ./nips16results/gridworld/out_grid8_FCN.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid8_FCN.pk --epochs 30 --model fcn --stepsize 0.002 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_FCN.pk | tee -a ./nips16results/gridworld/out_grid16_FCN.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid8_FCN.pk --epochs 30 --model fcn --stepsize 0.001 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_FCN.pk | tee -a ./nips16results/gridworld/out_grid16_FCN.txt ; 

# 16x16 map
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_FCN.pk --epochs 30 --model fcn --stepsize 0.01 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 | tee -a ./nips16results/gridworld/out_grid16_FCN.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_FCN.pk --epochs 30 --model fcn --stepsize 0.005 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_FCN.pk | tee -a ./nips16results/gridworld/out_grid16_FCN.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_FCN.pk --epochs 30 --model fcn --stepsize 0.002 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_FCN.pk | tee -a ./nips16results/gridworld/out_grid16_FCN.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_FCN.pk --epochs 30 --model fcn --stepsize 0.001 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_FCN.pk | tee -a ./nips16results/gridworld/out_grid16_FCN.txt ; 

# 28x28 map
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_28.mat --output ./nips16results/gridworld/grid28_FCN.pk --epochs 30 --model fcn --stepsize 0.01 --imsize 28 --reg 0.0 --batchsize 12 --statebatchsize 10 | tee -a ./nips16results/gridworld/out_grid28_FCN.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_28.mat --output ./nips16results/gridworld/grid28_FCN.pk --epochs 30 --model fcn --stepsize 0.005 --imsize 28 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid28_FCN.pk | tee -a ./nips16results/gridworld/out_grid28_FCN.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_28.mat --output ./nips16results/gridworld/grid28_FCN.pk --epochs 30 --model fcn --stepsize 0.002 --imsize 28 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid28_FCN.pk | tee -a ./nips16results/gridworld/out_grid28_FCN.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_28.mat --output ./nips16results/gridworld/grid28_FCN.pk --epochs 30 --model fcn --stepsize 0.001 --imsize 28 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid28_FCN.pk | tee -a ./nips16results/gridworld/out_grid28_FCN.txt ; 

# 16x16 map 0.5 data
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_FCN_05.pk --epochs 30 --model fcn --stepsize 0.01 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_FCN_05.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_FCN_05.pk --epochs 30 --model fcn --stepsize 0.005 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_FCN_05.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_FCN_05.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_FCN_05.pk --epochs 30 --model fcn --stepsize 0.002 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_FCN_05.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_FCN_05.txt ; 
#THEANO_FLAGS='floatX=float32,device=gpu' python NN_run_training.py --input ./data/gridworld_16.mat --output ./nips16results/gridworld/grid16_FCN_05.pk --epochs 30 --model fcn --stepsize 0.001 --imsize 16 --reg 0.0 --batchsize 12 --statebatchsize 10 --warmstart ./nips16results/gridworld/grid16_FCN_05.pk --data_fraction 0.5 | tee -a ./nips16results/gridworld/out_grid16_FCN_05.txt ; 

