Normalisation should now be implemented in dense_dataset.py. This notebook is going to investigate the difference between using per-pixel versus global normalisation. It's also going to act as a walkthrough for general experimentation using our code.

Setting up

To set up this notebook I've sourced my Python 2.7.8 virtualenv, set up as described in the README on the tools repo. Along with the recommended packages I've installed there I've installed some others. This is the exact list:



In [1]:

    
!pip freeze









    



Jinja2==2.7.3
MarkupSafe==0.23
Pillow==2.7.0
PyYAML==3.11
Pygments==2.0.2
Theano==0.6.0
argparse==1.3.0
backports.ssl-match-hostname==3.4.0.2
certifi==14.05.14
distribute==0.6.34
holoviews==0.7
ipython==3.0.0-rc1
jsonschema==2.4.0
mahotas==1.2.4
matplotlib==1.4.3
mistune==0.5
mock==1.0.1
-e git+git@github.com:Neuroglycerin/neukrill-net-tools.git@aa940cf57a38e8cd25fe84c9dfb5505112089c10#egg=neukrill_net-dev
nose==1.3.4
numpy==1.9.1
param==1.2.1
ptyprocess==0.4
py==1.4.26
-e git+https://github.com/lisa-lab/pylearn2.git@cf3999e7183f8dcaccccf4dfd2a31bbe3a948a97#egg=pylearn2-dev
pyparsing==2.0.3
pytest==2.6.4
python-dateutil==2.4.0
pytz==2014.10
pyzmq==14.5.0
scikit-image==0.10.1
scikit-learn==0.15.2
scipy==0.14.0
six==1.8.0
terminado==0.5
tornado==4.1
wsgiref==0.1.2

Also, I've sourced the start_script.sh to set the required environment variables for Theano on this machine:



In [2]:

    
cd ..









    



/afs/inf.ed.ac.uk/user/s11/s1145806/Documents/git/neukrill-net-work



In [3]:

    
!cat start_script.sh









    



#!/bin/bash
# execute it by typing 
# source start_script
# Courtesy of Krzysztof Geras
# Modified by Gavin Gray
# UI improved by Finlay Maguire

usage_1(){ echo "./start_script.sh GPU_CORES"; }

export gpu_number=$1

if [ -z "${gpu_number##*[!0-9]*}" ]
then
    usage_1
    return 1
fi

hostname=`hostname`

if [ $hostname = "stonesoup.inf.ed.ac.uk" ]; then
    echo "detected stonesoup, applying config"
	export LD_LIBRARY_PATH="/opt/cuda-5.0.35/lib:/opt/cuda-5.0.35/lib64"
	export CUDA_ROOT="/opt/cuda-5.0.35"
	export THEANO_FLAGS="device=gpu`echo -n $gpu_number`,floatX=float32,base_compiledir=~/.theano/stonesoup`echo -n $gpu_number`"
else
    echo "not stonesoup, applying default config"
	export THEANO_FLAGS="device=cpu,floatX=float32,base_compiledir=~/.theano/`echo -n $hostname`/`mktemp -u tmp.XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX`"
fi

# undef gpu_number
unset gpu_number

Before running this, check if any of the GPUs are in use:



In [4]:

    
!nvidia-smi









    



Thu Feb 26 18:12:14 2015       
+------------------------------------------------------+                       
| NVIDIA-SMI 340.24     Driver Version: 340.24         |                       
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K40c          Off  | 0000:02:00.0     Off |                    0 |
| 26%   51C    P0    62W / 235W |    113MiB / 11519MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Tesla K40c          Off  | 0000:04:00.0     Off |                    0 |
| 26%   51C    P0    62W / 235W |    145MiB / 11519MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   2  Tesla K40c          Off  | 0000:83:00.0     Off |                    0 |
| 23%   30C    P8    20W / 235W |     23MiB / 11519MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   3  Tesla K40c          Off  | 0000:84:00.0     Off |                    0 |
| 23%   26C    P8    18W / 235W |     23MiB / 11519MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Compute processes:                                               GPU Memory |
|  GPU       PID  Process name                                     Usage      |
|=============================================================================|
|    0     31158  ...s08/s0805516/repos/neukrill_venv2/bin/python2.7    88MiB |
|    1     81884  python                                               120MiB |
+-----------------------------------------------------------------------------+

Looking at that only one of the GPUs is in use, but the program isn't doing anything (because it's another program I'm debugging). Nothing is happening on GPU 0 so I've chosen that:



In [5]:

    
%%bash
echo $LD_LIBRARY_PATH
echo $CUDA_ROOT
echo $THEANO_FLAGS









    



/opt/cuda-5.0.35/lib:/opt/cuda-5.0.35/lib64
/opt/cuda-5.0.35
device=gpu0,floatX=float32,base_compiledir=~/.theano/stonesoup0

Settings

To provide settings for an experiment on these GPUs we use a json file loaded as a dictionary with a few specific keys. These are in the directory run_settings in the work repository:



In [6]:

    
ls run_settings/









    



alexnet_based.json             first_pylearn2_run.json  short_pylearn2.json
alexnet_based_norm_pixel.json  pylearn2_deep_test.json
default.json                   pylearn2_template.json

At the moment, our best performing model was the alexnet_based.json, a fairly unprocessed port of Matt's work on a vegetable detector to this problem.



In [7]:

    
!cat run_settings/alexnet_based.json









    



{
    "model type":"pylearn2",
    "yaml file": "alexnet_based.yaml",
    "preprocessing":{"resize":[48,48]},
    "final_shape":[48,48],
    "augmentation_factor":1,
    "train_split": 0.8
}

The most important part of the above file is the fact it points to the alexnet_based.yaml file, which is where the entire model is defined. Looking at that:



In [8]:

    
!cat yaml_templates/alexnet_based.yaml









    



!obj:pylearn2.train.Train {
    dataset: &train !obj:neukrill_net.dense_dataset.DensePNGDataset {
        settings_path: %(settings_path)s,
        run_settings: %(run_settings_path)s,
        training_set_mode: "train"
    },
    model: !obj:pylearn2.models.mlp.MLP {
        batch_size: &batch_size 128,
        input_space: !obj:pylearn2.space.Conv2DSpace {
            shape: %(final_shape)s,
            num_channels: 1,
            axes: ['c', 0, 1, 'b'],
        },
        layers: [ !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                     layer_name: h1,
                     output_channels: 48,
                     irange: .025,
                     init_bias: 0,
                     kernel_shape: [8, 8],
                     pool_shape: [2, 2],
                     pool_stride: [2, 2],
                     max_kernel_norm: 1.9365
                 },!obj:pylearn2.models.mlp.ConvRectifiedLinear {
                     layer_name: h2,
                     output_channels: 96,
                     irange: .025,
                     init_bias: 1,
                     kernel_shape: [5, 5],
                     pool_shape: [2, 2],
                     pool_stride: [2, 2],
                     max_kernel_norm: 1.9365
                 }, !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                     layer_name: h3,
                     output_channels: 128,
                     irange: .025,
                     init_bias: 0,
                     kernel_shape: [3, 3],
                     border_mode: full,
                     pool_shape: [1, 1],
                     pool_stride: [1, 1],
                     max_kernel_norm: 1.9365
                 }, !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                     layer_name: 'h4',
                     output_channels: 128,
                     irange: .025,
                     init_bias: 1,
                     kernel_shape: [3, 3],
                     border_mode: full,
                     pool_shape: [2, 2],
                     pool_stride: [2, 2],
                     max_kernel_norm: 1.9365
                 }, !obj:pylearn2.models.mlp.RectifiedLinear {
                     dim: 1024,
                     max_col_norm: 1.9,
                     layer_name: h5,
                     istdev: .05,
                     W_lr_scale: .25,
                     b_lr_scale: .25
                 }, !obj:pylearn2.models.mlp.Softmax {
                     n_classes: %(n_classes)i,
                     max_col_norm: 1.9365,
                     layer_name: y,
                     istdev: .05,
                     W_lr_scale: .25,
                     b_lr_scale: .25
                 }
                ],
    },
    algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
        train_iteration_mode: even_shuffled_sequential,
        monitor_iteration_mode: even_sequential,
        batch_size: *batch_size,
        learning_rate: .1,
        learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
            init_momentum: 0.5
        },
        monitoring_dataset: {
                'train': *train,
                'valid' : !obj:neukrill_net.dense_dataset.DensePNGDataset  {
                                settings_path: %(settings_path)s,
                                run_settings: %(run_settings_path)s,
                                training_set_mode: "validation"
            },
        },
        cost: !obj:pylearn2.costs.cost.SumOfCosts { costs: [ 
            !obj:pylearn2.costs.mlp.dropout.Dropout {
                input_include_probs: {
                    h1 : 1.,
                    h2 : 1.,
                    h3 : 1.,
                    h4 : 1.,
                    h5 : 0.5
                },
                input_scales: {
                    h1 : 1.,
                    h2 : 1.,
                    h3 : 1.,
                    h4 : 1.,
                    h5 : 2.
                }
             },
             !obj:pylearn2.costs.mlp.WeightDecay {
                 coeffs : {
                     h1 : .00005,
                     h2 : .00005,
                     h3 : .00005,
                     h4 : .00005,
                     h5 : .00005
                 }
             }
             ]
        },
        termination_criterion: !obj:pylearn2.termination_criteria.And {
            criteria: [
                !obj:pylearn2.termination_criteria.EpochCounter {
                    max_epochs: 500
                },
            ]
        }
    },
    extensions: [
        !obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor {
            start: 1,
            saturate: 25,
            final_momentum: 0.95
        },
        !obj:pylearn2.training_algorithms.sgd.LinearDecayOverEpoch {
            start: 1,
            saturate: 25,
            decay_factor: 0.025
        },
        !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
             channel_name: valid_y_misclass,
             save_path: '%(save_path)s'
        },
        !obj:pylearn2.training_algorithms.sgd.MonitorBasedLRAdjuster {
            high_trigger: 1.,
            low_trigger: 0.999,
            grow_amt: 1.1,
            shrink_amt: 0.9,
            max_lr: 0.2,
            min_lr: 1e-5,
            channel_name: valid_y_misclass
        }
    ],
}

Here's some of the information we can read out of this:

It's a six layer convolutional network, with an MLP layer at the end.
It has dropout implemented in layer h5. It has weight decay in the first 5 layers.
It won't finish until it's run 500 epochs (that's going to take a while).
It has extensions to adjust the momentum depending on the misclassification value.
It's monitoring the channel valid_y_misclass and saving the model with the best score (we would probably prefer log loss score for this).
It is modulating the momentum with MomentumAdjustor and LinearDecayOverEpoch.

Without normalisation, and with only resizing the images to 48 by 48 as described in the above json the score achieved in our saved model was:



In [9]:

    
%run ~/repos/pylearn2/pylearn2/scripts/print_monitor.py /disk/scratch/neuroglycerin/models/alexnet_based.pkl.backup









    



epochs seen:  487
time trained:  30254.874501
learning_rate : 0.00249999854714
momentum : 0.950000703335
total_seconds_last_epoch : 61.0847969055
train_h1_kernel_norms_max : 1.93650114536
train_h1_kernel_norms_mean : 0.852499127388
train_h1_kernel_norms_min : 0.0567898564041
train_h1_max_x_max_u : 5.39508914948
train_h1_max_x_mean_u : 0.70393550396
train_h1_max_x_min_u : 0.0
train_h1_mean_x_max_u : 2.26680850983
train_h1_mean_x_mean_u : 0.156515583396
train_h1_mean_x_min_u : 0.0
train_h1_min_x_max_u : 1.15339720249
train_h1_min_x_mean_u : 0.0199921280146
train_h1_min_x_min_u : 0.0
train_h1_range_x_max_u : 5.37765455246
train_h1_range_x_mean_u : 0.683943271637
train_h1_range_x_min_u : 0.0
train_h2_kernel_norms_max : 1.93650114536
train_h2_kernel_norms_mean : 1.65578627586
train_h2_kernel_norms_min : 0.777766108513
train_h2_max_x_max_u : 35.7470397949
train_h2_max_x_mean_u : 7.5576581955
train_h2_max_x_min_u : 0.0070008081384
train_h2_mean_x_max_u : 11.2434310913
train_h2_mean_x_mean_u : 1.31158018112
train_h2_mean_x_min_u : 0.000133931738674
train_h2_min_x_max_u : 1.61157393456
train_h2_min_x_mean_u : 0.013847976923
train_h2_min_x_min_u : 0.0
train_h2_range_x_max_u : 35.7204055786
train_h2_range_x_mean_u : 7.54381322861
train_h2_range_x_min_u : 0.0070008081384
train_h3_kernel_norms_max : 1.93650114536
train_h3_kernel_norms_mean : 1.26278531551
train_h3_kernel_norms_min : 0.261874675751
train_h3_max_x_max_u : 62.5857658386
train_h3_max_x_mean_u : 7.47495603561
train_h3_max_x_min_u : 0.0
train_h3_mean_x_max_u : 19.7139625549
train_h3_mean_x_mean_u : 1.09422492981
train_h3_mean_x_min_u : 0.0
train_h3_min_x_max_u : 4.93192768097
train_h3_min_x_mean_u : 0.012845932506
train_h3_min_x_min_u : 0.0
train_h3_range_x_max_u : 62.5399665833
train_h3_range_x_mean_u : 7.46211290359
train_h3_range_x_min_u : 0.0
train_h4_kernel_norms_max : 1.93650114536
train_h4_kernel_norms_mean : 1.41719603539
train_h4_kernel_norms_min : 0.709321200848
train_h4_max_x_max_u : 37.052154541
train_h4_max_x_mean_u : 2.40152573586
train_h4_max_x_min_u : 0.0
train_h4_mean_x_max_u : 8.60352706909
train_h4_mean_x_mean_u : 0.193274587393
train_h4_mean_x_min_u : 0.0
train_h4_min_x_max_u : 0.110938027501
train_h4_min_x_mean_u : 3.87041982322e-05
train_h4_min_x_min_u : 0.0
train_h4_range_x_max_u : 37.0458679199
train_h4_range_x_mean_u : 2.40148711205
train_h4_range_x_min_u : 0.0
train_h5_col_norms_max : 1.80762147903
train_h5_col_norms_mean : 1.72946929932
train_h5_col_norms_min : 1.66900110245
train_h5_max_x_max_u : 13.331536293
train_h5_max_x_mean_u : 3.19271445274
train_h5_max_x_min_u : 0.0
train_h5_mean_x_max_u : 4.27312994003
train_h5_mean_x_mean_u : 0.295631468296
train_h5_mean_x_min_u : 0.0
train_h5_min_x_max_u : 0.0420611724257
train_h5_min_x_mean_u : 6.59422730678e-05
train_h5_min_x_min_u : 0.0
train_h5_range_x_max_u : 13.3268642426
train_h5_range_x_mean_u : 3.1926484108
train_h5_range_x_min_u : 0.0
train_h5_row_norms_max : 1.14566957951
train_h5_row_norms_mean : 0.696925401688
train_h5_row_norms_min : 0.620610952377
train_objective : 0.675063252449
train_term_0 : 0.476964443922
train_term_1_weight_decay : 0.197502017021
train_y_col_norms_max : 1.93658590317
train_y_col_norms_mean : 1.88179862499
train_y_col_norms_min : 1.71785855293
train_y_max_max_class : 0.998831689358
train_y_mean_max_class : 0.885861456394
train_y_min_max_class : 0.446672290564
train_y_misclass : 0.0345155224204
train_y_nll : 0.162579715252
train_y_row_norms_max : 0.885181248188
train_y_row_norms_mean : 0.64335912466
train_y_row_norms_min : 0.481415987015
training_seconds_this_epoch : 39.8869400024
valid_h1_kernel_norms_max : 1.93649971485
valid_h1_kernel_norms_mean : 0.852497935295
valid_h1_kernel_norms_min : 0.0567899644375
valid_h1_max_x_max_u : 5.60019397736
valid_h1_max_x_mean_u : 0.768078386784
valid_h1_max_x_min_u : 0.0
valid_h1_mean_x_max_u : 2.13797736168
valid_h1_mean_x_mean_u : 0.155696392059
valid_h1_mean_x_min_u : 0.0
valid_h1_min_x_max_u : 1.06429243088
valid_h1_min_x_mean_u : 0.0148315615952
valid_h1_min_x_min_u : 0.0
valid_h1_range_x_max_u : 5.58431720734
valid_h1_range_x_mean_u : 0.753246843815
valid_h1_range_x_min_u : 0.0
valid_h2_kernel_norms_max : 1.93649971485
valid_h2_kernel_norms_mean : 1.65578198433
valid_h2_kernel_norms_min : 0.777766168118
valid_h2_max_x_max_u : 37.3208961487
valid_h2_max_x_mean_u : 8.19640636444
valid_h2_max_x_min_u : 0.0
valid_h2_mean_x_max_u : 10.2978839874
valid_h2_mean_x_mean_u : 1.29925835133
valid_h2_mean_x_min_u : 0.0
valid_h2_min_x_max_u : 1.31440508366
valid_h2_min_x_mean_u : 0.00758959027007
valid_h2_min_x_min_u : 0.0
valid_h2_range_x_max_u : 37.3208961487
valid_h2_range_x_mean_u : 8.18881702423
valid_h2_range_x_min_u : 0.0
valid_h3_kernel_norms_max : 1.93649971485
valid_h3_kernel_norms_mean : 1.26278448105
valid_h3_kernel_norms_min : 0.261874079704
valid_h3_max_x_max_u : 65.008682251
valid_h3_max_x_mean_u : 8.09002685547
valid_h3_max_x_min_u : 0.0
valid_h3_mean_x_max_u : 18.3018703461
valid_h3_mean_x_mean_u : 1.08458709717
valid_h3_mean_x_min_u : 0.0
valid_h3_min_x_max_u : 4.72949552536
valid_h3_min_x_mean_u : 0.00884997379035
valid_h3_min_x_min_u : 0.0
valid_h3_range_x_max_u : 64.9805221558
valid_h3_range_x_mean_u : 8.08117771149
valid_h3_range_x_min_u : 0.0
valid_h4_kernel_norms_max : 1.93649971485
valid_h4_kernel_norms_mean : 1.41719782352
valid_h4_kernel_norms_min : 0.709321022034
valid_h4_max_x_max_u : 37.0217170715
valid_h4_max_x_mean_u : 2.66809058189
valid_h4_max_x_min_u : 0.0
valid_h4_mean_x_max_u : 7.04785871506
valid_h4_mean_x_mean_u : 0.192644715309
valid_h4_mean_x_min_u : 0.0
valid_h4_min_x_max_u : 0.0649737268686
valid_h4_min_x_mean_u : 1.08261328933e-05
valid_h4_min_x_min_u : 0.0
valid_h4_range_x_max_u : 37.0217170715
valid_h4_range_x_mean_u : 2.66808009148
valid_h4_range_x_min_u : 0.0
valid_h5_col_norms_max : 1.80761814117
valid_h5_col_norms_mean : 1.72947120667
valid_h5_col_norms_min : 1.66900217533
valid_h5_max_x_max_u : 13.9688920975
valid_h5_max_x_mean_u : 3.93891096115
valid_h5_max_x_min_u : 0.0
valid_h5_mean_x_max_u : 3.40217018127
valid_h5_mean_x_mean_u : 0.292246431112
valid_h5_mean_x_min_u : 0.0
valid_h5_min_x_max_u : 0.0263353902847
valid_h5_min_x_mean_u : 2.57181545749e-05
valid_h5_min_x_min_u : 0.0
valid_h5_range_x_max_u : 13.9688920975
valid_h5_range_x_mean_u : 3.93888521194
valid_h5_range_x_min_u : 0.0
valid_h5_row_norms_max : 1.14566969872
valid_h5_row_norms_mean : 0.696923851967
valid_h5_row_norms_min : 0.62061047554
valid_objective : 2.19527244568
valid_term_0 : 1.9964928627
valid_term_1_weight_decay : 0.197501763701
valid_y_col_norms_max : 1.93658196926
valid_y_col_norms_mean : 1.88180267811
valid_y_col_norms_min : 1.71785604954
valid_y_max_max_class : 0.998774349689
valid_y_mean_max_class : 0.735651731491
valid_y_min_max_class : 0.182999402285
valid_y_misclass : 0.339673906565
valid_y_nll : 1.33173024654
valid_y_row_norms_max : 0.885183215141
valid_y_row_norms_mean : 0.643358230591
valid_y_row_norms_min : 0.481415838003






    



Using gpu device 0: Tesla K40c

Submitting this model to the Kaggle leaderboard achieved the best score we've been able to manage so far: 2.319966. Suspect that the valid_objective is our estimate of that score on the validation set, but I'm not actually sure yet.



In [10]:

    
%matplotlib inline
import holoviews as hl
%load_ext holoviews.ipython









    



Welcome to the HoloViews IPython extension! (http://ioam.github.io/holoviews/)
Available magics: %compositor, %opts, %params, %view, %%labels, %%opts, %%view






    



:0: FutureWarning: IPython widgets are experimental and may change in the future.






    





<matplotlib.figure.Figure at 0x7f880a9eb8d0>






    





<matplotlib.figure.Figure at 0x7f880aa05290>






    





<matplotlib.figure.Figure at 0x7f880aa05090>



In [11]:

    
%run ~/repos/pylearn2/pylearn2/scripts/plot_monitor.py /disk/scratch/neuroglycerin/models/alexnet_based.pkl.backup









    



generating names...
...done
set x_axis to example
A. learning_rate
B. momentum
C. total_seconds_last_epoch
D. train_h1_kernel_norms_max
E. train_h1_kernel_norms_mean
F. train_h1_kernel_norms_min
G. train_h1_max_x_max_u
H. train_h1_max_x_mean_u
I. train_h1_max_x_min_u
J. train_h1_mean_x_max_u
K. train_h1_mean_x_mean_u
L. train_h1_mean_x_min_u
M. train_h1_min_x_max_u
N. train_h1_min_x_mean_u
O. train_h1_min_x_min_u
P. train_h1_range_x_max_u
Q. train_h1_range_x_mean_u
R. train_h1_range_x_min_u
S. train_h2_kernel_norms_max
T. train_h2_kernel_norms_mean
U. train_h2_kernel_norms_min
V. train_h2_max_x_max_u
W. train_h2_max_x_mean_u
X. train_h2_max_x_min_u
Y. train_h2_mean_x_max_u
Z. train_h2_mean_x_mean_u
BA. train_h2_mean_x_min_u
BB. train_h2_min_x_max_u
BC. train_h2_min_x_mean_u
BD. train_h2_min_x_min_u
BE. train_h2_range_x_max_u
BF. train_h2_range_x_mean_u
BG. train_h2_range_x_min_u
BH. train_h3_kernel_norms_max
BI. train_h3_kernel_norms_mean
BJ. train_h3_kernel_norms_min
BK. train_h3_max_x_max_u
BL. train_h3_max_x_mean_u
BM. train_h3_max_x_min_u
BN. train_h3_mean_x_max_u
BO. train_h3_mean_x_mean_u
BP. train_h3_mean_x_min_u
BQ. train_h3_min_x_max_u
BR. train_h3_min_x_mean_u
BS. train_h3_min_x_min_u
BT. train_h3_range_x_max_u
BU. train_h3_range_x_mean_u
BV. train_h3_range_x_min_u
BW. train_h4_kernel_norms_max
BX. train_h4_kernel_norms_mean
BY. train_h4_kernel_norms_min
BZ. train_h4_max_x_max_u
CA. train_h4_max_x_mean_u
CB. train_h4_max_x_min_u
CC. train_h4_mean_x_max_u
CD. train_h4_mean_x_mean_u
CE. train_h4_mean_x_min_u
CF. train_h4_min_x_max_u
CG. train_h4_min_x_mean_u
CH. train_h4_min_x_min_u
CI. train_h4_range_x_max_u
CJ. train_h4_range_x_mean_u
CK. train_h4_range_x_min_u
CL. train_h5_col_norms_max
CM. train_h5_col_norms_mean
CN. train_h5_col_norms_min
CO. train_h5_max_x_max_u
CP. train_h5_max_x_mean_u
CQ. train_h5_max_x_min_u
CR. train_h5_mean_x_max_u
CS. train_h5_mean_x_mean_u
CT. train_h5_mean_x_min_u
CU. train_h5_min_x_max_u
CV. train_h5_min_x_mean_u
CW. train_h5_min_x_min_u
CX. train_h5_range_x_max_u
CY. train_h5_range_x_mean_u
CZ. train_h5_range_x_min_u
DA. train_h5_row_norms_max
DB. train_h5_row_norms_mean
DC. train_h5_row_norms_min
DD. train_objective
DE. train_term_0
DF. train_term_1_weight_decay
DG. train_y_col_norms_max
DH. train_y_col_norms_mean
DI. train_y_col_norms_min
DJ. train_y_max_max_class
DK. train_y_mean_max_class
DL. train_y_min_max_class
DM. train_y_misclass
DN. train_y_nll
DO. train_y_row_norms_max
DP. train_y_row_norms_mean
DQ. train_y_row_norms_min
DR. training_seconds_this_epoch
DS. valid_h1_kernel_norms_max
DT. valid_h1_kernel_norms_mean
DU. valid_h1_kernel_norms_min
DV. valid_h1_max_x_max_u
DW. valid_h1_max_x_mean_u
DX. valid_h1_max_x_min_u
DY. valid_h1_mean_x_max_u
DZ. valid_h1_mean_x_mean_u
EA. valid_h1_mean_x_min_u
EB. valid_h1_min_x_max_u
EC. valid_h1_min_x_mean_u
ED. valid_h1_min_x_min_u
EE. valid_h1_range_x_max_u
EF. valid_h1_range_x_mean_u
EG. valid_h1_range_x_min_u
EH. valid_h2_kernel_norms_max
EI. valid_h2_kernel_norms_mean
EJ. valid_h2_kernel_norms_min
EK. valid_h2_max_x_max_u
EL. valid_h2_max_x_mean_u
EM. valid_h2_max_x_min_u
EN. valid_h2_mean_x_max_u
EO. valid_h2_mean_x_mean_u
EP. valid_h2_mean_x_min_u
EQ. valid_h2_min_x_max_u
ER. valid_h2_min_x_mean_u
ES. valid_h2_min_x_min_u
ET. valid_h2_range_x_max_u
EU. valid_h2_range_x_mean_u
EV. valid_h2_range_x_min_u
EW. valid_h3_kernel_norms_max
EX. valid_h3_kernel_norms_mean
EY. valid_h3_kernel_norms_min
EZ. valid_h3_max_x_max_u
FA. valid_h3_max_x_mean_u
FB. valid_h3_max_x_min_u
FC. valid_h3_mean_x_max_u
FD. valid_h3_mean_x_mean_u
FE. valid_h3_mean_x_min_u
FF. valid_h3_min_x_max_u
FG. valid_h3_min_x_mean_u
FH. valid_h3_min_x_min_u
FI. valid_h3_range_x_max_u
FJ. valid_h3_range_x_mean_u
FK. valid_h3_range_x_min_u
FL. valid_h4_kernel_norms_max
FM. valid_h4_kernel_norms_mean
FN. valid_h4_kernel_norms_min
FO. valid_h4_max_x_max_u
FP. valid_h4_max_x_mean_u
FQ. valid_h4_max_x_min_u
FR. valid_h4_mean_x_max_u
FS. valid_h4_mean_x_mean_u
FT. valid_h4_mean_x_min_u
FU. valid_h4_min_x_max_u
FV. valid_h4_min_x_mean_u
FW. valid_h4_min_x_min_u
FX. valid_h4_range_x_max_u
FY. valid_h4_range_x_mean_u
FZ. valid_h4_range_x_min_u
GA. valid_h5_col_norms_max
GB. valid_h5_col_norms_mean
GC. valid_h5_col_norms_min
GD. valid_h5_max_x_max_u
GE. valid_h5_max_x_mean_u
GF. valid_h5_max_x_min_u
GG. valid_h5_mean_x_max_u
GH. valid_h5_mean_x_mean_u
GI. valid_h5_mean_x_min_u
GJ. valid_h5_min_x_max_u
GK. valid_h5_min_x_mean_u
GL. valid_h5_min_x_min_u
GM. valid_h5_range_x_max_u
GN. valid_h5_range_x_mean_u
GO. valid_h5_range_x_min_u
GP. valid_h5_row_norms_max
GQ. valid_h5_row_norms_mean
GR. valid_h5_row_norms_min
GS. valid_objective
GT. valid_term_0
GU. valid_term_1_weight_decay
GV. valid_y_col_norms_max
GW. valid_y_col_norms_mean
GX. valid_y_col_norms_min
GY. valid_y_max_max_class
GZ. valid_y_mean_max_class
HA. valid_y_min_max_class
HB. valid_y_misclass
HC. valid_y_nll
HD. valid_y_row_norms_max
HE. valid_y_row_norms_mean
HF. valid_y_row_norms_min

Put e, b, s or h in the list somewhere to plot epochs, batches, seconds, or hours, respectively.
Enter a list of channels to plot (example: A, C,F-G, h, <test_err>) or q to quit or o for options: GS






    












    



set x_axis to example
A. learning_rate
B. momentum
C. total_seconds_last_epoch
D. train_h1_kernel_norms_max
E. train_h1_kernel_norms_mean
F. train_h1_kernel_norms_min
G. train_h1_max_x_max_u
H. train_h1_max_x_mean_u
I. train_h1_max_x_min_u
J. train_h1_mean_x_max_u
K. train_h1_mean_x_mean_u
L. train_h1_mean_x_min_u
M. train_h1_min_x_max_u
N. train_h1_min_x_mean_u
O. train_h1_min_x_min_u
P. train_h1_range_x_max_u
Q. train_h1_range_x_mean_u
R. train_h1_range_x_min_u
S. train_h2_kernel_norms_max
T. train_h2_kernel_norms_mean
U. train_h2_kernel_norms_min
V. train_h2_max_x_max_u
W. train_h2_max_x_mean_u
X. train_h2_max_x_min_u
Y. train_h2_mean_x_max_u
Z. train_h2_mean_x_mean_u
BA. train_h2_mean_x_min_u
BB. train_h2_min_x_max_u
BC. train_h2_min_x_mean_u
BD. train_h2_min_x_min_u
BE. train_h2_range_x_max_u
BF. train_h2_range_x_mean_u
BG. train_h2_range_x_min_u
BH. train_h3_kernel_norms_max
BI. train_h3_kernel_norms_mean
BJ. train_h3_kernel_norms_min
BK. train_h3_max_x_max_u
BL. train_h3_max_x_mean_u
BM. train_h3_max_x_min_u
BN. train_h3_mean_x_max_u
BO. train_h3_mean_x_mean_u
BP. train_h3_mean_x_min_u
BQ. train_h3_min_x_max_u
BR. train_h3_min_x_mean_u
BS. train_h3_min_x_min_u
BT. train_h3_range_x_max_u
BU. train_h3_range_x_mean_u
BV. train_h3_range_x_min_u
BW. train_h4_kernel_norms_max
BX. train_h4_kernel_norms_mean
BY. train_h4_kernel_norms_min
BZ. train_h4_max_x_max_u
CA. train_h4_max_x_mean_u
CB. train_h4_max_x_min_u
CC. train_h4_mean_x_max_u
CD. train_h4_mean_x_mean_u
CE. train_h4_mean_x_min_u
CF. train_h4_min_x_max_u
CG. train_h4_min_x_mean_u
CH. train_h4_min_x_min_u
CI. train_h4_range_x_max_u
CJ. train_h4_range_x_mean_u
CK. train_h4_range_x_min_u
CL. train_h5_col_norms_max
CM. train_h5_col_norms_mean
CN. train_h5_col_norms_min
CO. train_h5_max_x_max_u
CP. train_h5_max_x_mean_u
CQ. train_h5_max_x_min_u
CR. train_h5_mean_x_max_u
CS. train_h5_mean_x_mean_u
CT. train_h5_mean_x_min_u
CU. train_h5_min_x_max_u
CV. train_h5_min_x_mean_u
CW. train_h5_min_x_min_u
CX. train_h5_range_x_max_u
CY. train_h5_range_x_mean_u
CZ. train_h5_range_x_min_u
DA. train_h5_row_norms_max
DB. train_h5_row_norms_mean
DC. train_h5_row_norms_min
DD. train_objective
DE. train_term_0
DF. train_term_1_weight_decay
DG. train_y_col_norms_max
DH. train_y_col_norms_mean
DI. train_y_col_norms_min
DJ. train_y_max_max_class
DK. train_y_mean_max_class
DL. train_y_min_max_class
DM. train_y_misclass
DN. train_y_nll
DO. train_y_row_norms_max
DP. train_y_row_norms_mean
DQ. train_y_row_norms_min
DR. training_seconds_this_epoch
DS. valid_h1_kernel_norms_max
DT. valid_h1_kernel_norms_mean
DU. valid_h1_kernel_norms_min
DV. valid_h1_max_x_max_u
DW. valid_h1_max_x_mean_u
DX. valid_h1_max_x_min_u
DY. valid_h1_mean_x_max_u
DZ. valid_h1_mean_x_mean_u
EA. valid_h1_mean_x_min_u
EB. valid_h1_min_x_max_u
EC. valid_h1_min_x_mean_u
ED. valid_h1_min_x_min_u
EE. valid_h1_range_x_max_u
EF. valid_h1_range_x_mean_u
EG. valid_h1_range_x_min_u
EH. valid_h2_kernel_norms_max
EI. valid_h2_kernel_norms_mean
EJ. valid_h2_kernel_norms_min
EK. valid_h2_max_x_max_u
EL. valid_h2_max_x_mean_u
EM. valid_h2_max_x_min_u
EN. valid_h2_mean_x_max_u
EO. valid_h2_mean_x_mean_u
EP. valid_h2_mean_x_min_u
EQ. valid_h2_min_x_max_u
ER. valid_h2_min_x_mean_u
ES. valid_h2_min_x_min_u
ET. valid_h2_range_x_max_u
EU. valid_h2_range_x_mean_u
EV. valid_h2_range_x_min_u
EW. valid_h3_kernel_norms_max
EX. valid_h3_kernel_norms_mean
EY. valid_h3_kernel_norms_min
EZ. valid_h3_max_x_max_u
FA. valid_h3_max_x_mean_u
FB. valid_h3_max_x_min_u
FC. valid_h3_mean_x_max_u
FD. valid_h3_mean_x_mean_u
FE. valid_h3_mean_x_min_u
FF. valid_h3_min_x_max_u
FG. valid_h3_min_x_mean_u
FH. valid_h3_min_x_min_u
FI. valid_h3_range_x_max_u
FJ. valid_h3_range_x_mean_u
FK. valid_h3_range_x_min_u
FL. valid_h4_kernel_norms_max
FM. valid_h4_kernel_norms_mean
FN. valid_h4_kernel_norms_min
FO. valid_h4_max_x_max_u
FP. valid_h4_max_x_mean_u
FQ. valid_h4_max_x_min_u
FR. valid_h4_mean_x_max_u
FS. valid_h4_mean_x_mean_u
FT. valid_h4_mean_x_min_u
FU. valid_h4_min_x_max_u
FV. valid_h4_min_x_mean_u
FW. valid_h4_min_x_min_u
FX. valid_h4_range_x_max_u
FY. valid_h4_range_x_mean_u
FZ. valid_h4_range_x_min_u
GA. valid_h5_col_norms_max
GB. valid_h5_col_norms_mean
GC. valid_h5_col_norms_min
GD. valid_h5_max_x_max_u
GE. valid_h5_max_x_mean_u
GF. valid_h5_max_x_min_u
GG. valid_h5_mean_x_max_u
GH. valid_h5_mean_x_mean_u
GI. valid_h5_mean_x_min_u
GJ. valid_h5_min_x_max_u
GK. valid_h5_min_x_mean_u
GL. valid_h5_min_x_min_u
GM. valid_h5_range_x_max_u
GN. valid_h5_range_x_mean_u
GO. valid_h5_range_x_min_u
GP. valid_h5_row_norms_max
GQ. valid_h5_row_norms_mean
GR. valid_h5_row_norms_min
GS. valid_objective
GT. valid_term_0
GU. valid_term_1_weight_decay
GV. valid_y_col_norms_max
GW. valid_y_col_norms_mean
GX. valid_y_col_norms_min
GY. valid_y_max_max_class
GZ. valid_y_mean_max_class
HA. valid_y_min_max_class
HB. valid_y_misclass
HC. valid_y_nll
HD. valid_y_row_norms_max
HE. valid_y_row_norms_mean
HF. valid_y_row_norms_min

Put e, b, s or h in the list somewhere to plot epochs, batches, seconds, or hours, respectively.
Enter a list of channels to plot (example: A, C,F-G, h, <test_err>) or q to quit or o for options: HC






    












    



set x_axis to example
A. learning_rate
B. momentum
C. total_seconds_last_epoch
D. train_h1_kernel_norms_max
E. train_h1_kernel_norms_mean
F. train_h1_kernel_norms_min
G. train_h1_max_x_max_u
H. train_h1_max_x_mean_u
I. train_h1_max_x_min_u
J. train_h1_mean_x_max_u
K. train_h1_mean_x_mean_u
L. train_h1_mean_x_min_u
M. train_h1_min_x_max_u
N. train_h1_min_x_mean_u
O. train_h1_min_x_min_u
P. train_h1_range_x_max_u
Q. train_h1_range_x_mean_u
R. train_h1_range_x_min_u
S. train_h2_kernel_norms_max
T. train_h2_kernel_norms_mean
U. train_h2_kernel_norms_min
V. train_h2_max_x_max_u
W. train_h2_max_x_mean_u
X. train_h2_max_x_min_u
Y. train_h2_mean_x_max_u
Z. train_h2_mean_x_mean_u
BA. train_h2_mean_x_min_u
BB. train_h2_min_x_max_u
BC. train_h2_min_x_mean_u
BD. train_h2_min_x_min_u
BE. train_h2_range_x_max_u
BF. train_h2_range_x_mean_u
BG. train_h2_range_x_min_u
BH. train_h3_kernel_norms_max
BI. train_h3_kernel_norms_mean
BJ. train_h3_kernel_norms_min
BK. train_h3_max_x_max_u
BL. train_h3_max_x_mean_u
BM. train_h3_max_x_min_u
BN. train_h3_mean_x_max_u
BO. train_h3_mean_x_mean_u
BP. train_h3_mean_x_min_u
BQ. train_h3_min_x_max_u
BR. train_h3_min_x_mean_u
BS. train_h3_min_x_min_u
BT. train_h3_range_x_max_u
BU. train_h3_range_x_mean_u
BV. train_h3_range_x_min_u
BW. train_h4_kernel_norms_max
BX. train_h4_kernel_norms_mean
BY. train_h4_kernel_norms_min
BZ. train_h4_max_x_max_u
CA. train_h4_max_x_mean_u
CB. train_h4_max_x_min_u
CC. train_h4_mean_x_max_u
CD. train_h4_mean_x_mean_u
CE. train_h4_mean_x_min_u
CF. train_h4_min_x_max_u
CG. train_h4_min_x_mean_u
CH. train_h4_min_x_min_u
CI. train_h4_range_x_max_u
CJ. train_h4_range_x_mean_u
CK. train_h4_range_x_min_u
CL. train_h5_col_norms_max
CM. train_h5_col_norms_mean
CN. train_h5_col_norms_min
CO. train_h5_max_x_max_u
CP. train_h5_max_x_mean_u
CQ. train_h5_max_x_min_u
CR. train_h5_mean_x_max_u
CS. train_h5_mean_x_mean_u
CT. train_h5_mean_x_min_u
CU. train_h5_min_x_max_u
CV. train_h5_min_x_mean_u
CW. train_h5_min_x_min_u
CX. train_h5_range_x_max_u
CY. train_h5_range_x_mean_u
CZ. train_h5_range_x_min_u
DA. train_h5_row_norms_max
DB. train_h5_row_norms_mean
DC. train_h5_row_norms_min
DD. train_objective
DE. train_term_0
DF. train_term_1_weight_decay
DG. train_y_col_norms_max
DH. train_y_col_norms_mean
DI. train_y_col_norms_min
DJ. train_y_max_max_class
DK. train_y_mean_max_class
DL. train_y_min_max_class
DM. train_y_misclass
DN. train_y_nll
DO. train_y_row_norms_max
DP. train_y_row_norms_mean
DQ. train_y_row_norms_min
DR. training_seconds_this_epoch
DS. valid_h1_kernel_norms_max
DT. valid_h1_kernel_norms_mean
DU. valid_h1_kernel_norms_min
DV. valid_h1_max_x_max_u
DW. valid_h1_max_x_mean_u
DX. valid_h1_max_x_min_u
DY. valid_h1_mean_x_max_u
DZ. valid_h1_mean_x_mean_u
EA. valid_h1_mean_x_min_u
EB. valid_h1_min_x_max_u
EC. valid_h1_min_x_mean_u
ED. valid_h1_min_x_min_u
EE. valid_h1_range_x_max_u
EF. valid_h1_range_x_mean_u
EG. valid_h1_range_x_min_u
EH. valid_h2_kernel_norms_max
EI. valid_h2_kernel_norms_mean
EJ. valid_h2_kernel_norms_min
EK. valid_h2_max_x_max_u
EL. valid_h2_max_x_mean_u
EM. valid_h2_max_x_min_u
EN. valid_h2_mean_x_max_u
EO. valid_h2_mean_x_mean_u
EP. valid_h2_mean_x_min_u
EQ. valid_h2_min_x_max_u
ER. valid_h2_min_x_mean_u
ES. valid_h2_min_x_min_u
ET. valid_h2_range_x_max_u
EU. valid_h2_range_x_mean_u
EV. valid_h2_range_x_min_u
EW. valid_h3_kernel_norms_max
EX. valid_h3_kernel_norms_mean
EY. valid_h3_kernel_norms_min
EZ. valid_h3_max_x_max_u
FA. valid_h3_max_x_mean_u
FB. valid_h3_max_x_min_u
FC. valid_h3_mean_x_max_u
FD. valid_h3_mean_x_mean_u
FE. valid_h3_mean_x_min_u
FF. valid_h3_min_x_max_u
FG. valid_h3_min_x_mean_u
FH. valid_h3_min_x_min_u
FI. valid_h3_range_x_max_u
FJ. valid_h3_range_x_mean_u
FK. valid_h3_range_x_min_u
FL. valid_h4_kernel_norms_max
FM. valid_h4_kernel_norms_mean
FN. valid_h4_kernel_norms_min
FO. valid_h4_max_x_max_u
FP. valid_h4_max_x_mean_u
FQ. valid_h4_max_x_min_u
FR. valid_h4_mean_x_max_u
FS. valid_h4_mean_x_mean_u
FT. valid_h4_mean_x_min_u
FU. valid_h4_min_x_max_u
FV. valid_h4_min_x_mean_u
FW. valid_h4_min_x_min_u
FX. valid_h4_range_x_max_u
FY. valid_h4_range_x_mean_u
FZ. valid_h4_range_x_min_u
GA. valid_h5_col_norms_max
GB. valid_h5_col_norms_mean
GC. valid_h5_col_norms_min
GD. valid_h5_max_x_max_u
GE. valid_h5_max_x_mean_u
GF. valid_h5_max_x_min_u
GG. valid_h5_mean_x_max_u
GH. valid_h5_mean_x_mean_u
GI. valid_h5_mean_x_min_u
GJ. valid_h5_min_x_max_u
GK. valid_h5_min_x_mean_u
GL. valid_h5_min_x_min_u
GM. valid_h5_range_x_max_u
GN. valid_h5_range_x_mean_u
GO. valid_h5_range_x_min_u
GP. valid_h5_row_norms_max
GQ. valid_h5_row_norms_mean
GR. valid_h5_row_norms_min
GS. valid_objective
GT. valid_term_0
GU. valid_term_1_weight_decay
GV. valid_y_col_norms_max
GW. valid_y_col_norms_mean
GX. valid_y_col_norms_min
GY. valid_y_max_max_class
GZ. valid_y_mean_max_class
HA. valid_y_min_max_class
HB. valid_y_misclass
HC. valid_y_nll
HD. valid_y_row_norms_max
HE. valid_y_row_norms_mean
HF. valid_y_row_norms_min

Put e, b, s or h in the list somewhere to plot epochs, batches, seconds, or hours, respectively.
Enter a list of channels to plot (example: A, C,F-G, h, <test_err>) or q to quit or o for options: HB






    












    



set x_axis to example
A. learning_rate
B. momentum
C. total_seconds_last_epoch
D. train_h1_kernel_norms_max
E. train_h1_kernel_norms_mean
F. train_h1_kernel_norms_min
G. train_h1_max_x_max_u
H. train_h1_max_x_mean_u
I. train_h1_max_x_min_u
J. train_h1_mean_x_max_u
K. train_h1_mean_x_mean_u
L. train_h1_mean_x_min_u
M. train_h1_min_x_max_u
N. train_h1_min_x_mean_u
O. train_h1_min_x_min_u
P. train_h1_range_x_max_u
Q. train_h1_range_x_mean_u
R. train_h1_range_x_min_u
S. train_h2_kernel_norms_max
T. train_h2_kernel_norms_mean
U. train_h2_kernel_norms_min
V. train_h2_max_x_max_u
W. train_h2_max_x_mean_u
X. train_h2_max_x_min_u
Y. train_h2_mean_x_max_u
Z. train_h2_mean_x_mean_u
BA. train_h2_mean_x_min_u
BB. train_h2_min_x_max_u
BC. train_h2_min_x_mean_u
BD. train_h2_min_x_min_u
BE. train_h2_range_x_max_u
BF. train_h2_range_x_mean_u
BG. train_h2_range_x_min_u
BH. train_h3_kernel_norms_max
BI. train_h3_kernel_norms_mean
BJ. train_h3_kernel_norms_min
BK. train_h3_max_x_max_u
BL. train_h3_max_x_mean_u
BM. train_h3_max_x_min_u
BN. train_h3_mean_x_max_u
BO. train_h3_mean_x_mean_u
BP. train_h3_mean_x_min_u
BQ. train_h3_min_x_max_u
BR. train_h3_min_x_mean_u
BS. train_h3_min_x_min_u
BT. train_h3_range_x_max_u
BU. train_h3_range_x_mean_u
BV. train_h3_range_x_min_u
BW. train_h4_kernel_norms_max
BX. train_h4_kernel_norms_mean
BY. train_h4_kernel_norms_min
BZ. train_h4_max_x_max_u
CA. train_h4_max_x_mean_u
CB. train_h4_max_x_min_u
CC. train_h4_mean_x_max_u
CD. train_h4_mean_x_mean_u
CE. train_h4_mean_x_min_u
CF. train_h4_min_x_max_u
CG. train_h4_min_x_mean_u
CH. train_h4_min_x_min_u
CI. train_h4_range_x_max_u
CJ. train_h4_range_x_mean_u
CK. train_h4_range_x_min_u
CL. train_h5_col_norms_max
CM. train_h5_col_norms_mean
CN. train_h5_col_norms_min
CO. train_h5_max_x_max_u
CP. train_h5_max_x_mean_u
CQ. train_h5_max_x_min_u
CR. train_h5_mean_x_max_u
CS. train_h5_mean_x_mean_u
CT. train_h5_mean_x_min_u
CU. train_h5_min_x_max_u
CV. train_h5_min_x_mean_u
CW. train_h5_min_x_min_u
CX. train_h5_range_x_max_u
CY. train_h5_range_x_mean_u
CZ. train_h5_range_x_min_u
DA. train_h5_row_norms_max
DB. train_h5_row_norms_mean
DC. train_h5_row_norms_min
DD. train_objective
DE. train_term_0
DF. train_term_1_weight_decay
DG. train_y_col_norms_max
DH. train_y_col_norms_mean
DI. train_y_col_norms_min
DJ. train_y_max_max_class
DK. train_y_mean_max_class
DL. train_y_min_max_class
DM. train_y_misclass
DN. train_y_nll
DO. train_y_row_norms_max
DP. train_y_row_norms_mean
DQ. train_y_row_norms_min
DR. training_seconds_this_epoch
DS. valid_h1_kernel_norms_max
DT. valid_h1_kernel_norms_mean
DU. valid_h1_kernel_norms_min
DV. valid_h1_max_x_max_u
DW. valid_h1_max_x_mean_u
DX. valid_h1_max_x_min_u
DY. valid_h1_mean_x_max_u
DZ. valid_h1_mean_x_mean_u
EA. valid_h1_mean_x_min_u
EB. valid_h1_min_x_max_u
EC. valid_h1_min_x_mean_u
ED. valid_h1_min_x_min_u
EE. valid_h1_range_x_max_u
EF. valid_h1_range_x_mean_u
EG. valid_h1_range_x_min_u
EH. valid_h2_kernel_norms_max
EI. valid_h2_kernel_norms_mean
EJ. valid_h2_kernel_norms_min
EK. valid_h2_max_x_max_u
EL. valid_h2_max_x_mean_u
EM. valid_h2_max_x_min_u
EN. valid_h2_mean_x_max_u
EO. valid_h2_mean_x_mean_u
EP. valid_h2_mean_x_min_u
EQ. valid_h2_min_x_max_u
ER. valid_h2_min_x_mean_u
ES. valid_h2_min_x_min_u
ET. valid_h2_range_x_max_u
EU. valid_h2_range_x_mean_u
EV. valid_h2_range_x_min_u
EW. valid_h3_kernel_norms_max
EX. valid_h3_kernel_norms_mean
EY. valid_h3_kernel_norms_min
EZ. valid_h3_max_x_max_u
FA. valid_h3_max_x_mean_u
FB. valid_h3_max_x_min_u
FC. valid_h3_mean_x_max_u
FD. valid_h3_mean_x_mean_u
FE. valid_h3_mean_x_min_u
FF. valid_h3_min_x_max_u
FG. valid_h3_min_x_mean_u
FH. valid_h3_min_x_min_u
FI. valid_h3_range_x_max_u
FJ. valid_h3_range_x_mean_u
FK. valid_h3_range_x_min_u
FL. valid_h4_kernel_norms_max
FM. valid_h4_kernel_norms_mean
FN. valid_h4_kernel_norms_min
FO. valid_h4_max_x_max_u
FP. valid_h4_max_x_mean_u
FQ. valid_h4_max_x_min_u
FR. valid_h4_mean_x_max_u
FS. valid_h4_mean_x_mean_u
FT. valid_h4_mean_x_min_u
FU. valid_h4_min_x_max_u
FV. valid_h4_min_x_mean_u
FW. valid_h4_min_x_min_u
FX. valid_h4_range_x_max_u
FY. valid_h4_range_x_mean_u
FZ. valid_h4_range_x_min_u
GA. valid_h5_col_norms_max
GB. valid_h5_col_norms_mean
GC. valid_h5_col_norms_min
GD. valid_h5_max_x_max_u
GE. valid_h5_max_x_mean_u
GF. valid_h5_max_x_min_u
GG. valid_h5_mean_x_max_u
GH. valid_h5_mean_x_mean_u
GI. valid_h5_mean_x_min_u
GJ. valid_h5_min_x_max_u
GK. valid_h5_min_x_mean_u
GL. valid_h5_min_x_min_u
GM. valid_h5_range_x_max_u
GN. valid_h5_range_x_mean_u
GO. valid_h5_range_x_min_u
GP. valid_h5_row_norms_max
GQ. valid_h5_row_norms_mean
GR. valid_h5_row_norms_min
GS. valid_objective
GT. valid_term_0
GU. valid_term_1_weight_decay
GV. valid_y_col_norms_max
GW. valid_y_col_norms_mean
GX. valid_y_col_norms_min
GY. valid_y_max_max_class
GZ. valid_y_mean_max_class
HA. valid_y_min_max_class
HB. valid_y_misclass
HC. valid_y_nll
HD. valid_y_row_norms_max
HE. valid_y_row_norms_mean
HF. valid_y_row_norms_min

Put e, b, s or h in the list somewhere to plot epochs, batches, seconds, or hours, respectively.
Enter a list of channels to plot (example: A, C,F-G, h, <test_err>) or q to quit or o for options: q

Unfortunately, it's difficult to view the weights in an IPython notebook (and we get the above problems with user input above) and the devs don't want to fix it because they don't like IPython notebooks. So we need to hack around this problem.

The hack I've made is to define their environment variable PYLEARN2_VIEWER_COMMAND to be a shell script that just uses ImageMagick to rewrite the file (whatever it is) to a file in ~/tmp called pylearnplot.png. Then, we can just use IPython's rich display system to show it in the notebook.



In [12]:

    
%env PYLEARN2_VIEWER_COMMAND=/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/image_hack.sh









    



env: PYLEARN2_VIEWER_COMMAND=/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/image_hack.sh



In [13]:

    
!echo $PYLEARN2_VIEWER_COMMAND









    



/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/image_hack.sh



In [14]:

    
%run ~/repos/pylearn2/pylearn2/scripts/show_weights.py /disk/scratch/neuroglycerin/models/alexnet_based.pkl.backup









    



making weights report
loading model
loading done
smallest enc weight magnitude: 7.74973341322e-06
mean enc weight magnitude: 0.0863215103745
max enc weight magnitude: 1.04721748829



In [15]:

    
from IPython.display import Image



In [16]:

    
def plot_recent_pylearn2():
    pl2plt = Image(filename="/afs/inf.ed.ac.uk/user/s08/s0805516/tmp/pylearnplot.png", width=500)
    return pl2plt
plot_recent_pylearn2()









    Out[16]:

Looking at these, quite a few of the weight matrices seems to be quite noisy. Maybe they've learned something about the texture of the classes? Anyway, this was just to show that we can investigate the weights like this in a notebook. Looking closely at the weights is a job for another time.

Normalisation

Taking a break from how to use Pylearn2, this notebook is also about looking at what Normalisation can do for us. We might as well inspect by eye the difference between images normalised by pixel and images normalised globally.

Loading some images into our Pylearn2 dataset class for two different cases. First, we have to prepare two run settings files:



In [18]:

    
import neukrill_net.utils as utils



In [148]:

    
!cp run_settings/alexnet_based.json run_settings/alexnet_based_norm_pixel.json



In [149]:

    
import os
import copy



In [150]:

    
settings = utils.Settings("settings.json")
bypixel = utils.load_run_settings("run_settings/alexnet_based_norm_pixel.json",settings)
byglobal = copy.deepcopy(bypixel)
byglobal['run_settings_path'] = os.path.abspath("run_settings/alexnet_based_norm_global.json")
bypixel['preprocessing']['normalise'] = {"global_or_pixel":"pixel"}
byglobal['preprocessing']['normalise'] = {"global_or_pixel":"global"}
utils.save_run_settings(byglobal)
utils.save_run_settings(bypixel)



In [151]:

    
import neukrill_net.dense_dataset as dense
reload(dense)









    Out[151]:





<module 'neukrill_net.dense_dataset' from '/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-tools/neukrill_net/dense_dataset.pyc'>



In [145]:

    
# force recompile
import neukrill_net.image_processing
reload(neukrill_net.image_processing)









    Out[145]:





<module 'neukrill_net.image_processing' from '/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-tools/neukrill_net/image_processing.py'>



In [146]:

    
global_data = dense.DensePNGDataset(run_settings=byglobal['run_settings_path'],verbose=True)









    



Applying normalisation: global



In [152]:

    
pixel_data = dense.DensePNGDataset(run_settings=bypixel['run_settings_path'],verbose=True)









    



Applying normalisation: pixel
Applying pixel-wise normalisation

The normalisation statistics have now been saved to run settings json, and we can look at them if we want to:



In [161]:

    
!cat run_settings/alexnet_based_norm_pixel.json









    



{
    "augmentation_factor":1,
    "filename":"alexnet_based_norm_pixel",
    "final_shape":[
        48,
        48
    ],
    "model type":"pylearn2",
    "modeldir":"/disk/scratch/neuroglycerin/models",
    "pickle abspath":"/disk/scratch/neuroglycerin/models/alexnet_based_norm_pixel.pkl",
    "preprocessing":{
        "normalise":{
            "global_or_pixel":"pixel",
            "mu":{
                "(0, 0)":0.9807952059667816,
                "(0, 1)":0.98973486432202984,
                "(0, 10)":0.9874850675900948,
                "(0, 11)":0.98740896702778236,
                "(0, 12)":0.98720651596826325,
                "(0, 13)":0.98717504341953832,
                "(0, 14)":0.98707215070156384,
                "(0, 15)":0.98687027153704809,
                "(0, 16)":0.98679753938061354,
                "(0, 17)":0.98675479686548151,
                "(0, 18)":0.98665363365862691,
                "(0, 19)":0.98681208509863494,
                "(0, 2)":0.98935695303652027,
                "(0, 20)":0.98664668643350062,
                "(0, 21)":0.98629084645121012,
                "(0, 22)":0.98643770858909785,
                "(0, 23)":0.98623263634853964,
                "(0, 24)":0.986242158357301,
                "(0, 25)":0.98603625041885279,
                "(0, 26)":0.98603656799775419,
                "(0, 27)":0.98578672946493751,
                "(0, 28)":0.98590243858306281,
                "(0, 29)":0.98611081998979899,
                "(0, 3)":0.98899703412331552,
                "(0, 30)":0.98622118449938845,
                "(0, 31)":0.98632307834677657,
                "(0, 32)":0.98632064951908072,
                "(0, 33)":0.98633188817909845,
                "(0, 34)":0.98625684530543734,
                "(0, 35)":0.98643573862312262,
                "(0, 36)":0.98616605197994089,
                "(0, 37)":0.98663058646392265,
                "(0, 38)":0.98666889513698475,
                "(0, 39)":0.98663806034614387,
                "(0, 4)":0.98858915840675554,
                "(0, 40)":0.9871629405485729,
                "(0, 41)":0.98727972922765861,
                "(0, 42)":0.98782331816004432,
                "(0, 43)":0.98813636877302724,
                "(0, 44)":0.98864526759068672,
                "(0, 45)":0.98908860223803108,
                "(0, 46)":0.98955899150874693,
                "(0, 47)":0.98065473883531462,
                "(0, 5)":0.98840650189240453,
                "(0, 6)":0.98804075716229511,
                "(0, 7)":0.98776918598690466,
                "(0, 8)":0.98745665579433406,
                "(0, 9)":0.98757201845156906,
                "(1, 0)":0.98980896143353025,
                "(1, 1)":0.99925890692710351,
                "(1, 10)":0.99699334732267264,
                "(1, 11)":0.99694276257455183,
                "(1, 12)":0.99679081126858993,
                "(1, 13)":0.99660417712110339,
                "(1, 14)":0.99642422045783197,
                "(1, 15)":0.99623665781764692,
                "(1, 16)":0.99595331001127485,
                "(1, 17)":0.99594531829115507,
                "(1, 18)":0.99575794459609812,
                "(1, 19)":0.99568637260315762,
                "(1, 2)":0.99889215842490231,
                "(1, 20)":0.99563512193545667,
                "(1, 21)":0.9956032882915975,
                "(1, 22)":0.99563411749577613,
                "(1, 23)":0.99546048546103438,
                "(1, 24)":0.99518468022823203,
                "(1, 25)":0.99492629694845292,
                "(1, 26)":0.99482916239255392,
                "(1, 27)":0.99464521347947121,
                "(1, 28)":0.99488218545515505,
                "(1, 29)":0.99498794739845198,
                "(1, 3)":0.9984812532617674,
                "(1, 30)":0.99523514317657824,
                "(1, 31)":0.99535550233005554,
                "(1, 32)":0.99547252798550878,
                "(1, 33)":0.9957071947779238,
                "(1, 34)":0.99553777382149344,
                "(1, 35)":0.99563950899362363,
                "(1, 36)":0.99566266407415183,
                "(1, 37)":0.99576613401026981,
                "(1, 38)":0.99594197082647584,
                "(1, 39)":0.99620830076414746,
                "(1, 4)":0.99816581919954217,
                "(1, 40)":0.99624292636873801,
                "(1, 41)":0.99666599944001832,
                "(1, 42)":0.99720528989269697,
                "(1, 43)":0.99760905704092462,
                "(1, 44)":0.99803642145925542,
                "(1, 45)":0.99858513318509656,
                "(1, 46)":0.99906999231469251,
                "(1, 47)":0.98969836054489191,
                "(1, 5)":0.99796067317288073,
                "(1, 6)":0.99764752864072936,
                "(1, 7)":0.99730752512378862,
                "(1, 8)":0.99714392067315927,
                "(1, 9)":0.99720660982067233,
                "(10, 0)":0.98749848453885181,
                "(10, 1)":0.99680718609140229,
                "(10, 10)":0.98875367005752179,
                "(10, 11)":0.98741124264390501,
                "(10, 12)":0.98612745541809921,
                "(10, 13)":0.98427687075472714,
                "(10, 14)":0.98260936274729049,
                "(10, 15)":0.98105659097064191,
                "(10, 16)":0.97921088868100603,
                "(10, 17)":0.97668737602248135,
                "(10, 18)":0.97461185553067231,
                "(10, 19)":0.97240362435525285,
                "(10, 2)":0.99619382788355071,
                "(10, 20)":0.96899387218852184,
                "(10, 21)":0.96587967357803794,
                "(10, 22)":0.96350369723399765,
                "(10, 23)":0.95955700549253631,
                "(10, 24)":0.95691065952708954,
                "(10, 25)":0.9546783134720539,
                "(10, 26)":0.95328357982782719,
                "(10, 27)":0.95313396285064511,
                "(10, 28)":0.95473874365802125,
                "(10, 29)":0.95721904965168914,
                "(10, 3)":0.99565606232610104,
                "(10, 30)":0.96006127684987741,
                "(10, 31)":0.96320998638026589,
                "(10, 32)":0.96613320779323586,
                "(10, 33)":0.96846749709721247,
                "(10, 34)":0.97035725612031687,
                "(10, 35)":0.97306621644579072,
                "(10, 36)":0.97621728939776509,
                "(10, 37)":0.97880368030704401,
                "(10, 38)":0.98178329064111569,
                "(10, 39)":0.98412266944696081,
                "(10, 4)":0.99495316431323577,
                "(10, 40)":0.98639756921254529,
                "(10, 41)":0.98917286055676246,
                "(10, 42)":0.99104538593951941,
                "(10, 43)":0.99274381975822512,
                "(10, 44)":0.99432981804954124,
                "(10, 45)":0.99557357705228922,
                "(10, 46)":0.99639590662190725,
                "(10, 47)":0.98723267305396523,
                "(10, 5)":0.99435896846176186,
                "(10, 6)":0.99350740373131341,
                "(10, 7)":0.99237923582406207,
                "(10, 8)":0.99113938358465459,
                "(10, 9)":0.98983727595461168,
                "(11, 0)":0.98745227527150725,
                "(11, 1)":0.99666963488131677,
                "(11, 10)":0.98670977861124864,
                "(11, 11)":0.98523551245494245,
                "(11, 12)":0.98354326108597256,
                "(11, 13)":0.98099748466547698,
                "(11, 14)":0.97864992735526246,
                "(11, 15)":0.97646823524917137,
                "(11, 16)":0.9743618848153226,
                "(11, 17)":0.97203718689661245,
                "(11, 18)":0.96854248905846252,
                "(11, 19)":0.96536124293103831,
                "(11, 2)":0.99604264685180077,
                "(11, 20)":0.96071135905125038,
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            }
        },
        "resize":[
            48,
            48
        ]
    },
    "run_settings_path":"/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/run_settings/alexnet_based_norm_pixel.json",
    "settings_path":"/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/settings.json",
    "submissions abspath":"/disk/scratch/neuroglycerin/submissions/alexnet_based_norm_pixel.csv",
    "train_split":0.8,
    "yaml file":"alexnet_based.yaml"
}

Now that the data is loaded we can have a look at these images with Holoviews (it's from Edinburgh!).



In [153]:

    
import numpy as np



In [154]:

    
np.random.seed(42)
random_indexes = np.random.randint(low=0,high=global_data.X.shape[0],size=20)
global_images = global_data.get_topological_view(
                    global_data.X[random_indexes,:]).reshape(20,48,48)



In [155]:

    
%opts Image style(cmap='gray')
channels = hl.Image(global_images[0])
for i in global_images[1:,:,:]:
    channels = channels + hl.Image(i)
# this is so easy it should be illegal
channels









    Out[155]:

Global images are just scaled by a single value, so we'd expect them to look the same. There's no reason to expect that for images scaled over the pixel values though:



In [156]:

    
np.random.seed(42)
random_indexes = np.random.randint(low=0,high=pixel_data.X.shape[0],size=20)
pixel_images = pixel_data.get_topological_view(
                    pixel_data.X[random_indexes,:]).reshape(20,48,48)



In [157]:

    
channels = hl.Image(pixel_images[0])
for i in pixel_images[1:,:,:]:
    channels = channels + hl.Image(i)
channels









    Out[157]:

It's easy to see the difference. There's a washout in the middle of images compared with the outside because so little happens at the edges that when something does happen it doesn't get squashed.



In [158]:

    
import matplotlib.pyplot as plt



In [159]:

    
data = global_data.X
print("Max",np.max(data))
print("Min",np.min(data))
print("Mean",np.mean(data))
print("Stdev",np.sqrt(np.var(data)))
_ = plt.hist(np.ravel(data),bins=50)
plt.title("Global normalisation results")









    



('Max', 0.31255826933187614)
('Min', -6.6099986906559627)
('Mean', -1.08585851115198e-15)
('Stdev', 1.0)






    Out[159]:





<matplotlib.text.Text at 0x7f880ad51510>



In [160]:

    
data = pixel_data.X
print("Max",np.max(data))
print("Min",np.min(data))
print("Mean",np.mean(data))
print("Stdev",np.sqrt(np.var(data)))
_ = plt.hist(np.ravel(data),bins=50)
plt.title("Pixel normalisation results")









    



('Max', 1.1513339147312567)
('Min', -68.6217779241979)
('Mean', 1.6430570170280674e-16)
('Stdev', 0.99999999999999933)






    Out[160]:





<matplotlib.text.Text at 0x7f87cc642390>

Similar distributions, but getting some massive negative values on some of these pixels. At this point all we can do is try to train a model with them and see if we get better results than without scaling.

Running a model

We're going to be running two models, using a utility called longjob. If you leave a job running on an active ssh connection then it will be deemed inactive after a period of time (12 hours?) and you'll suddenly stop being able to write anything. Using longjob this won't happen. This notebook server is being run through longjob for this reason.

longjob is a wrapper script which simplifies the task of running jobs which require access to valid kerberos credentials for a longer period than the default ticket length (18 hours). The most common reason for this is so that a job can continue to access the AFS file system after the user’s initial ticket has expired.

Another useful utility if you're not in a notebook, running jobs through the shell is screen. Using this it's possible to keep a shell open without being connected to it. You can run something in a shell then disconnect from it and leave it running in the background, then come back to it later. Simple usage in our case:

screen -D -R <session_name>

Where <session_name> is whatever name you want to give your shell session. Then you can use ctrl-a + d to detach and leave it running.

We want to run two nets on different GPUs. One for global and one for pixel-wise normalisation. The two run settings files will be an input to the general-purpose training script train.py. To get the help message for train.py:



In [165]:

    
%run train.py -h









    



usage: train.py [-h] [-f] [-v] [run_settings]

Train a model and store apickled model file.

positional arguments:
  run_settings  Path to run settings json file.

optional arguments:
  -h, --help    show this help message and exit
  -f            Force overwrite of model files/submission csvs/anything else.
  -v            Run verbose.

Can't figure out how to send off longjobs from IPython notebooks yet. So the two commands I'm running are:

longjob -28day -c "python train.py run_settings/alexnet_based_norm_global.json" > /disk/scratch/neuroglycerin/logs/norm_global.log

and

longjob -28day -c "python train.py run_settings/alexnet_based_norm_pixel.json" > /disk/scratch/neuroglycerin/logs/norm_pixel.log

These were started in a screen session we can see with the following command:



In [167]:

    
!screen -ls









    



	2418.ipy	(Detached)
	33993.longjobs	(Detached)
2 Sockets in /var/run/screen/S-s0805516.

Because we're writing to these logs we can check the most recent progress by just tailing the logs:



In [186]:

    
!tail -n 20 /disk/scratch/neuroglycerin/logs/norm_global.log









    



	valid_h5_range_x_mean_u: 0.684866130352
	valid_h5_range_x_min_u: 0.0
	valid_h5_row_norms_max: 0.877702474594
	valid_h5_row_norms_mean: 0.763510167599
	valid_h5_row_norms_min: 0.703453838825
	valid_objective: 3.12544417381
	valid_term_0: 2.94545531273
	valid_term_1_weight_decay: 0.192011252046
	valid_y_col_norms_max: 1.76487243176
	valid_y_col_norms_mean: 1.59867763519
	valid_y_col_norms_min: 1.47974860668
	valid_y_max_max_class: 0.592624187469
	valid_y_mean_max_class: 0.221788734198
	valid_y_min_max_class: 0.0387513153255
	valid_y_misclass: 0.646739006042
	valid_y_nll: 2.67557621002
	valid_y_row_norms_max: 0.710210502148
	valid_y_row_norms_mean: 0.54859995842
	valid_y_row_norms_min: 0.457096040249
Saving to /disk/scratch/neuroglycerin/models/alexnet_based_norm_global.pkl...



In [187]:

    
!tail -n 20 /disk/scratch/neuroglycerin/logs/norm_pixel.log









    



	valid_h5_row_norms_mean
	valid_h5_row_norms_min
	valid_objective
	valid_term_0
	valid_term_1_weight_decay
	valid_y_col_norms_max
	valid_y_col_norms_mean
	valid_y_col_norms_min
	valid_y_max_max_class
	valid_y_mean_max_class
	valid_y_min_max_class
	valid_y_misclass
	valid_y_nll
	valid_y_row_norms_max
	valid_y_row_norms_mean
	valid_y_row_norms_min
Compiling accum...
graph size: 491
graph size: 487
Compiling accum done. Time elapsed: 0:02:30.649570

We can also check how the GPUs are doing with the same command as at the start:



In [188]:

    
!nvidia-smi









    



Thu Feb 26 02:35:40 2015       
+------------------------------------------------------+                       
| NVIDIA-SMI 340.24     Driver Version: 340.24         |                       
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K40c          Off  | 0000:02:00.0     Off |                    0 |
| 30%   64C    P0    97W / 235W |    365MiB / 11519MiB |     91%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Tesla K40c          Off  | 0000:04:00.0     Off |                    0 |
| 26%   53C    P0    62W / 235W |    145MiB / 11519MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   2  Tesla K40c          Off  | 0000:83:00.0     Off |                    0 |
| 27%   57C    P0   104W / 235W |    218MiB / 11519MiB |     80%      Default |
+-------------------------------+----------------------+----------------------+
|   3  Tesla K40c          Off  | 0000:84:00.0     Off |                    0 |
| 23%   27C    P8    18W / 235W |     24MiB / 11519MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Compute processes:                                               GPU Memory |
|  GPU       PID  Process name                                     Usage      |
|=============================================================================|
|    0     31158  ...s08/s0805516/repos/neukrill_venv2/bin/python2.7    88MiB |
|    0     34176  python                                               210MiB |
|    1     81884  python                                               120MiB |
|    2     34273  python                                               192MiB |
+-----------------------------------------------------------------------------+

One is running on GPU 0 and one is running on GPU 2. The above were run shortly after starting the training. After leaving it overnight we get the following logs:



In [189]:

    
!tail -n 20 /disk/scratch/neuroglycerin/logs/norm_global.log









    



	valid_h5_range_x_min_u: 0.0
	valid_h5_row_norms_max: 1.01608288288
	valid_h5_row_norms_mean: 0.740970134735
	valid_h5_row_norms_min: 0.678165793419
	valid_objective: 1.8276052475
	valid_term_0: 1.65191495419
	valid_term_1_weight_decay: 0.203093662858
	valid_y_col_norms_max: 1.93657040596
	valid_y_col_norms_mean: 1.70017051697
	valid_y_col_norms_min: 1.43537795544
	valid_y_max_max_class: 0.996362328529
	valid_y_mean_max_class: 0.687002122402
	valid_y_min_max_class: 0.150911450386
	valid_y_misclass: 0.332201063633
	valid_y_nll: 1.18461406231
	valid_y_row_norms_max: 0.897549271584
	valid_y_row_norms_mean: 0.581896603107
	valid_y_row_norms_min: 0.448730647564
Saving to /disk/scratch/neuroglycerin/models/alexnet_based_norm_global.pkl...
retrying with pickle



In [200]:

    
!tail -n 200 /disk/scratch/neuroglycerin/logs/norm_pixel.log









    



	train_y_max_max_class: 0.995562672615
	train_y_mean_max_class: 0.815425693989
	train_y_min_max_class: 0.294000834227
	train_y_misclass: 0.0736606568098
	train_y_nll: 0.3095087111
	train_y_row_norms_max: 0.855372488499
	train_y_row_norms_mean: 0.586683392525
	train_y_row_norms_min: 0.454553991556
	training_seconds_this_epoch: 39.1973304749
	valid_h1_kernel_norms_max: 1.93649971485
	valid_h1_kernel_norms_mean: 0.927059650421
	valid_h1_kernel_norms_min: 0.327536135912
	valid_h1_max_x_max_u: 64.1715164185
	valid_h1_max_x_mean_u: 4.57075119019
	valid_h1_max_x_min_u: 0.091011762619
	valid_h1_mean_x_max_u: 4.3826880455
	valid_h1_mean_x_mean_u: 0.421219885349
	valid_h1_mean_x_min_u: 0.0108055388555
	valid_h1_min_x_max_u: 0.193720579147
	valid_h1_min_x_mean_u: 0.000374322815333
	valid_h1_min_x_min_u: 0.0
	valid_h1_range_x_max_u: 64.1715164185
	valid_h1_range_x_mean_u: 4.57037687302
	valid_h1_range_x_min_u: 0.0907139778137
	valid_h2_kernel_norms_max: 1.93649935722
	valid_h2_kernel_norms_mean: 1.36715149879
	valid_h2_kernel_norms_min: 0.873390734196
	valid_h2_max_x_max_u: 79.0194625854
	valid_h2_max_x_mean_u: 11.210735321
	valid_h2_max_x_min_u: 0.237421020865
	valid_h2_mean_x_max_u: 7.56675815582
	valid_h2_mean_x_mean_u: 1.10737860203
	valid_h2_mean_x_min_u: 0.0103207370266
	valid_h2_min_x_max_u: 0.271307021379
	valid_h2_min_x_mean_u: 0.00015917645942
	valid_h2_min_x_min_u: 0.0
	valid_h2_range_x_max_u: 79.0194625854
	valid_h2_range_x_mean_u: 11.2105760574
	valid_h2_range_x_min_u: 0.237421020865
	valid_h3_kernel_norms_max: 1.93649971485
	valid_h3_kernel_norms_mean: 1.13978934288
	valid_h3_kernel_norms_min: 0.360583513975
	valid_h3_max_x_max_u: 72.941078186
	valid_h3_max_x_mean_u: 7.05418348312
	valid_h3_max_x_min_u: 0.0
	valid_h3_mean_x_max_u: 10.23960495
	valid_h3_mean_x_mean_u: 0.596179366112
	valid_h3_mean_x_min_u: 0.0
	valid_h3_min_x_max_u: 1.3681640625
	valid_h3_min_x_mean_u: 0.00114450987894
	valid_h3_min_x_min_u: 0.0
	valid_h3_range_x_max_u: 72.941078186
	valid_h3_range_x_mean_u: 7.05303907394
	valid_h3_range_x_min_u: 0.0
	valid_h4_kernel_norms_max: 1.93649971485
	valid_h4_kernel_norms_mean: 1.43103218079
	valid_h4_kernel_norms_min: 0.916529715061
	valid_h4_max_x_max_u: 29.1415691376
	valid_h4_max_x_mean_u: 2.37752890587
	valid_h4_max_x_min_u: 0.0
	valid_h4_mean_x_max_u: 3.83462047577
	valid_h4_mean_x_mean_u: 0.167018145323
	valid_h4_mean_x_min_u: 0.0
	valid_h4_min_x_max_u: 0.012051041238
	valid_h4_min_x_mean_u: 2.84752059088e-06
	valid_h4_min_x_min_u: 0.0
	valid_h4_range_x_max_u: 29.1415691376
	valid_h4_range_x_mean_u: 2.37752604485
	valid_h4_range_x_min_u: 0.0
	valid_h5_col_norms_max: 1.89104878902
	valid_h5_col_norms_mean: 1.83905756474
	valid_h5_col_norms_min: 1.81500041485
	valid_h5_max_x_max_u: 12.0401086807
	valid_h5_max_x_mean_u: 2.84514117241
	valid_h5_max_x_min_u: 0.0
	valid_h5_mean_x_max_u: 3.89247059822
	valid_h5_mean_x_mean_u: 0.294317662716
	valid_h5_mean_x_min_u: 0.0
	valid_h5_min_x_max_u: 0.0908049419522
	valid_h5_min_x_mean_u: 0.000148596896906
	valid_h5_min_x_min_u: 0.0
	valid_h5_range_x_max_u: 11.9507627487
	valid_h5_range_x_mean_u: 2.84499263763
	valid_h5_range_x_min_u: 0.0
	valid_h5_row_norms_max: 1.12359809875
	valid_h5_row_norms_mean: 0.742707014084
	valid_h5_row_norms_min: 0.677861809731
	valid_objective: 1.95369648933
	valid_term_0: 1.73092210293
	valid_term_1_weight_decay: 0.209054395556
	valid_y_col_norms_max: 1.93465721607
	valid_y_col_norms_mean: 1.71490502357
	valid_y_col_norms_min: 1.50027453899
	valid_y_max_max_class: 0.999137997627
	valid_y_mean_max_class: 0.720116913319
	valid_y_min_max_class: 0.161853581667
	valid_y_misclass: 0.330842405558
	valid_y_nll: 1.22162234783
	valid_y_row_norms_max: 0.855371832848
	valid_y_row_norms_mean: 0.586682379246
	valid_y_row_norms_min: 0.45455417037
monitoring channel is valid_y_misclass
Time this epoch: 39.173683 seconds
Monitoring step:
	Epochs seen: 131
	Batches seen: 24759
	Examples seen: 3169152
	learning_rate: 0.00249999854714
	momentum: 0.950000703335
	total_seconds_last_epoch: 60.7451095581
	train_h1_kernel_norms_max: 1.93650114536
	train_h1_kernel_norms_mean: 0.928892612457
	train_h1_kernel_norms_min: 0.328443169594
	train_h1_max_x_max_u: 56.9687805176
	train_h1_max_x_mean_u: 4.05644750595
	train_h1_max_x_min_u: 0.0590690672398
	train_h1_mean_x_max_u: 4.96982002258
	train_h1_mean_x_mean_u: 0.421429365873
	train_h1_mean_x_min_u: 0.00585033558309
	train_h1_min_x_max_u: 0.3436653018
	train_h1_min_x_mean_u: 0.00192947941832
	train_h1_min_x_min_u: 0.0
	train_h1_range_x_max_u: 56.9687576294
	train_h1_range_x_mean_u: 4.05451583862
	train_h1_range_x_min_u: 0.058736462146
	train_h2_kernel_norms_max: 1.93650114536
	train_h2_kernel_norms_mean: 1.37118768692
	train_h2_kernel_norms_min: 0.873928904533
	train_h2_max_x_max_u: 73.6567687988
	train_h2_max_x_mean_u: 9.87738132477
	train_h2_max_x_min_u: 0.118375577033
	train_h2_mean_x_max_u: 9.38936233521
	train_h2_mean_x_mean_u: 1.1014097929
	train_h2_mean_x_min_u: 0.00311834085733
	train_h2_min_x_max_u: 0.561589598656
	train_h2_min_x_mean_u: 0.000850826385431
	train_h2_min_x_min_u: 0.0
	train_h2_range_x_max_u: 73.6567687988
	train_h2_range_x_mean_u: 9.8765335083
	train_h2_range_x_min_u: 0.118375577033
	train_h3_kernel_norms_max: 1.93650114536
	train_h3_kernel_norms_mean: 1.1431876421
	train_h3_kernel_norms_min: 0.360394984484
	train_h3_max_x_max_u: 61.781452179
	train_h3_max_x_mean_u: 6.14226007462
	train_h3_max_x_min_u: 0.0
	train_h3_mean_x_max_u: 11.4197006226
	train_h3_mean_x_mean_u: 0.583802700043
	train_h3_mean_x_min_u: 0.0
	train_h3_min_x_max_u: 1.605651021
	train_h3_min_x_mean_u: 0.00165921065491
	train_h3_min_x_min_u: 0.0
	train_h3_range_x_max_u: 61.7693595886
	train_h3_range_x_mean_u: 6.14059972763
	train_h3_range_x_min_u: 0.0
	train_h4_kernel_norms_max: 1.93650114536
	train_h4_kernel_norms_mean: 1.4344984293
	train_h4_kernel_norms_min: 0.920431852341
	train_h4_max_x_max_u: 25.4164829254
	train_h4_max_x_mean_u: 2.16171121597
	train_h4_max_x_min_u: 0.0
	train_h4_mean_x_max_u: 4.52112007141
	train_h4_mean_x_mean_u: 0.171170488
	train_h4_mean_x_min_u: 0.0
	train_h4_min_x_max_u: 0.0957624837756
	train_h4_min_x_mean_u: 4.92021172249e-05
	train_h4_min_x_min_u: 0.0
	train_h4_range_x_max_u: 25.4164829254
	train_h4_range_x_mean_u: 2.16166162491
	train_h4_range_x_min_u: 0.0
	train_h5_col_norms_max: 1.89087700844
	train_h5_col_norms_mean: 1.83874583244
	train_h5_col_norms_min: 1.81461060047
	train_h5_max_x_max_u: 10.9953956604
	train_h5_max_x_mean_u: 2.29204964638
	train_h5_max_x_min_u: 0.0
	train_h5_mean_x_max_u: 4.5512008667
	train_h5_mean_x_mean_u: 0.291231125593
	train_h5_mean_x_min_u: 0.0
	train_h5_min_x_max_u: 0.361003488302
	train_h5_min_x_mean_u: 0.000777485547587
	train_h5_min_x_min_u: 0.0
	train_h5_range_x_max_u: 10.9214706421
	train_h5_range_x_mean_u: 2.29127216339
	train_h5_range_x_min_u: 0.0
	train_h5_row_norms_max: 1.12332606316
	train_h5_row_norms_mean: 0.742581665516
	train_h5_row_norms_min: 0.67771178484
	train_objective: 0.8281442523
	train_term_0: 0.625607132912
	train_term_1_weight_decay: 0.209171548486
	train_y_col_norms_max: 1.93222010136
	train_y_col_norms_mean: 1.71577477455
	train_y_col_norms_min: 1.49978578091
	train_y_max_max_class: 0.995287895203
	train_y_mean_max_class: 0.81663531065
	train_y_min_max_class: 0.291170716286
	train_y_misclass: 0.0707671493292
	train_y_nll: 0.304521560669
	train_y_ro

So that file just stops halfway through writing? Has someone filled up scratch again?



In [193]:

    
!df -h









    



Filesystem      Size  Used Avail Use% Mounted on
/dev/sda1        39G   24G   14G  64% /
tmpfs            32G  288K   32G   1% /dev/shm
/dev/sda3       7.7G  6.2G  1.2G  85% /var/cache/afs
/dev/sda4       855G  805G  6.9G 100% /disk/scratch
AFS             8.6G     0  8.6G   0% /afs
/dev/gpfsdev    6.4T  3.6T  2.9T  56% /gpfs

Looks like it was probably filled during the night and has now been cleaned up a bit.

Results logging

Pending section on how these results are logged back into the run settings. (Pending code to do that).

Analysing these networks

Looking into the pickle files, seeing which is better and why.



In [ ]:

Prediction

So we've trained these models and now we want to make a submission to the Kaggle leaderboard. For this, we use our general purpose test.py script (I would've preferred to call it predict.py at this point). It takes a stored pickle file, loads the test data and runs forward passes of the network to generate predictions for every test point. It then writes these results to gzipped csv file. All we have to do is specify which run settings file to use.

Doing this for both of these:



In [202]:

    
%run test.py run_settings/alexnet_based_norm_global.json
%run test.py run_settings/alexnet_based_norm_pixel.json









    



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Waiting 128.0 seconds and trying again






    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/test.py in <module>()
    131             help="Path to run settings json file.")
    132     args = parser.parse_args()
--> 133     main(args.run_settings)

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/test.py in main(run_settings_path)
     27     elif run_settings['model type'] == 'pylearn2':
     28         #train_pylearn2(run_settings)
---> 29         test_pylearn2(run_settings)
     30     else:
     31         raise NotImplementedError("Unsupported model type.")

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/test.py in test_pylearn2(run_settings, batch_size, verbose)
     69 
     70     # first load the model
---> 71     model = pylearn2.utils.serial.load(run_settings['pickle abspath'])
     72 
     73     # then load the dataset

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    188         if not retry:
    189             reraise_as(ValueError('Failed to open {0}'.format(filepath)))
--> 190         obj =  exponential_backoff()
    191     except Exception:
    192         #assert False

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    147             nsec = 0.5 * (2.0 ** float(recurse_depth))
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
--> 149             time.sleep(nsec)
    150             return load(filepath, recurse_depth + 1, retry)
    151 

KeyboardInterrupt: 





    



Waiting 0.5 seconds and trying again
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Waiting 16.0 seconds and trying again
Waiting 32.0 seconds and trying again
Waiting 64.0 seconds and trying again






    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/test.py in <module>()
    131             help="Path to run settings json file.")
    132     args = parser.parse_args()
--> 133     main(args.run_settings)

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/test.py in main(run_settings_path)
     27     elif run_settings['model type'] == 'pylearn2':
     28         #train_pylearn2(run_settings)
---> 29         test_pylearn2(run_settings)
     30     else:
     31         raise NotImplementedError("Unsupported model type.")

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/neukrill-net-work/test.py in test_pylearn2(run_settings, batch_size, verbose)
     69 
     70     # first load the model
---> 71     model = pylearn2.utils.serial.load(run_settings['pickle abspath'])
     72 
     73     # then load the dataset

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    182         if not retry:
    183             reraise_as(e.__class__('Failed to open {0}'.format(filepath)))
--> 184         obj =  exponential_backoff()
    185     except ValueError:
    186         logger.exception

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    182         if not retry:
    183             reraise_as(e.__class__('Failed to open {0}'.format(filepath)))
--> 184         obj =  exponential_backoff()
    185     except ValueError:
    186         logger.exception

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    182         if not retry:
    183             reraise_as(e.__class__('Failed to open {0}'.format(filepath)))
--> 184         obj =  exponential_backoff()
    185     except ValueError:
    186         logger.exception

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    182         if not retry:
    183             reraise_as(e.__class__('Failed to open {0}'.format(filepath)))
--> 184         obj =  exponential_backoff()
    185     except ValueError:
    186         logger.exception

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    182         if not retry:
    183             reraise_as(e.__class__('Failed to open {0}'.format(filepath)))
--> 184         obj =  exponential_backoff()
    185     except ValueError:
    186         logger.exception

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    182         if not retry:
    183             reraise_as(e.__class__('Failed to open {0}'.format(filepath)))
--> 184         obj =  exponential_backoff()
    185     except ValueError:
    186         logger.exception

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    182         if not retry:
    183             reraise_as(e.__class__('Failed to open {0}'.format(filepath)))
--> 184         obj =  exponential_backoff()
    185     except ValueError:
    186         logger.exception

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
    149             time.sleep(nsec)
--> 150             return load(filepath, recurse_depth + 1, retry)
    151 
    152     try:

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in load(filepath, recurse_depth, retry)
    182         if not retry:
    183             reraise_as(e.__class__('Failed to open {0}'.format(filepath)))
--> 184         obj =  exponential_backoff()
    185     except ValueError:
    186         logger.exception

/afs/inf.ed.ac.uk/user/s08/s0805516/repos/pylearn2/pylearn2/utils/serial.pyc in exponential_backoff()
    147             nsec = 0.5 * (2.0 ** float(recurse_depth))
    148             logger.info("Waiting {0} seconds and trying again".format(nsec))
--> 149             time.sleep(nsec)
    150             return load(filepath, recurse_depth + 1, retry)
    151 

KeyboardInterrupt:

Looks like those pickle files are corrupted then. Normally that would have run ok.

Submission

Anyway, if that had worked you could then copy that file back to your computer with scp or rsync and submitted it to the Kaggle web form to see what score you'd get on the public leaderboard.