CIFAR: classification with nonlinearity

[anhhuyalex]

for k in 1 2 3 4 5 6 7 8 9 11 17; do python -u cifar_classification_randomfeatures.py ./data  --coarsegrain_blocksize  $k --num_hidden_features $i --num_train_samples $j  --fileprefix linelr0.001_wd1em5_cifarAUG15 --nonlinearity line --train_method gradient_descent --epochs 150 --lr 0.001; done #    --wd 0.0000000000000000000000001

for k in 1 2 3 4 5 6 7 8 9 11 17; do python -u cifar_classification_randomfeatures.py ./data  --coarsegrain_blocksize  $k --num_hidden_features $i --num_train_samples $j  --fileprefix tanhlr0.1_wd1em5_cifarAUG15 --nonlinearity tanh --train_method gradient_descent --epochs 150 --lr 0.1; done #    --wd 0.0000000000000000000000001

Whole network

for k in 1 2 3 4 5 6 7 8 9 11 17; do python -u cifar_classification_randomfeatures_wholenetwork.py ./data  --coarsegrain_blocksize  $k --num_hidden_features $i --num_train_samples $j  --fileprefix linelr0.1_wd1em5_cifar_fullnetAUG16 --nonlinearity line--train_method gradient_descent --epochs 150 --lr 0.1; done #    --wd 0.0000000000000000000000001

for k in 1 2 3 4 5 6 7 8 9 11 17; do python -u cifar_classification_randomfeatures_wholenetwork.py ./data  --coarsegrain_blocksize  $k --num_hidden_features $i --num_train_samples $j  --fileprefix tanhlr0.1_wd1em5_cifar_fullnetAUG16 --nonlinearity tanh --train_method gradient_descent --epochs 150 --lr 0.1; done #    --wd 0.0000000000000000000000001

for k in 1 2 3 4 5 6 7 8 9 11 17; do python -u cifar_classification_randomfeatures_wholenetwork.py ./data  --coarsegrain_blocksize  $k --num_hidden_features $i --num_train_samples $j  --fileprefix relulr0.001_wd1em5_cifar_fullnetAUG16 --nonlinearity relu --train_method gradient_descent --epochs 150 --lr 0.001; done #    --wd 0.0000000000000000000000001

[anhhuyalex]

try training whole network

[anhhuyalex]

try making the stride not all of block-size