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#!/usr/bin/env python3

import numpy as np
import gym
from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv

env_name = 'Pendulum-v0'
nproc = 8
T = 10

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#!/usr/bin/env python3

import argparse

import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('bmh')
import numpy as np
import pandas as pd

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nan	
1	nn.Sequential {
  [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> (19) -> (20) -> (21) -> (22) -> (23) -> (24) -> output]
  (1): nn.SpatialConvolution(3 -> 64, 7x7, 2,2, 3,3)
  (2): nn.SpatialBatchNormalization
  (3): nn.ReLU
  (4): nn.SpatialMaxPooling(3x3, 2,2, 1,1)
  (5): nn.SpatialCrossMapLRN
  (6): nn.SpatialConvolution(64 -> 64, 1x1)
  (7): nn.SpatialBatchNormalization

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%22clamp%22
%27chiyaan%27-vikram
%27mama%27-cass-elliot
%27snub%27-pollard
%27superfly%27-jimmy-snuka
%27weird-al%27-yankovic
%28-estrella-lin-%29-lin-wei-ling
%C3%B8yvind-larsen-runestad
02:58:54      10244 .DS_Stor
12012

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$ tree data/mydataset/raw
person-1
├── image-1.jpg
├── image-2.png
...
└── image-p.png

...

person-m

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Mean: 0.21204432748784804
Stdev: 0.24856266429793847

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-- ignore option data	
nDonkeys	0	2	
-- ignore option optimState	
-- ignore option cache	
-- ignore option retrain	
{
  testBatchSize : 12
  LR : 0
  batchSize : 128
  data : "/home/bamos/fbcunn_imagenet/imagenet_raw_images"

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-- ignore option data	
-- ignore option optimState	
-- ignore option cache	
-- ignore option retrain	
{
  testBatchSize : 12
  LR : 0
  batchSize : 128
  data : "/home/bamos/fbcunn_imagenet/imagenet_raw_images"
  netType : "alexnet"

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# Manually running script

test1: test Tue Jul  7 09:41:01 EDT 2015
test2: test Tue Jul  7 09:41:01 EDT 2015
email: test Tue Jul  7 09:41:01 EDT 2015

# Running script in cron

Entry: * * * * * /home/bamos/tmp/test.sh