需先训练后
python science_rcn/run.py --train_size 100 --test_size 20 --parallel --test_only
# 可以指定模型名称
python science_rcn/run.py --train_size 100 --test_size 20 --parallel --test_only --model_file='model.pkl'
参数 --data_dir=data/MNIST1
输出当前训练的字符
python science_rcn/run.py --test_only
INFO:__main__:Testing on 20 images...
20:28:54
fwd_infer use 0.134
[ 0 17 1 3 11 12 23 18 21 4 15 2 16 24 14 6 19 10 5 7]
count 20
forward_pass use 0.854
try !! char: 0 win: 0 score: 24.4962846709
try !! char: 0 win: 3 score: 25.5222142712
try !! char: 0 win: 12 score: 26.7935296271
try !! char: 0 win: 23 score: 27.0901419072
try !! char: 0 win: 21 score: 27.0918954155
try !! char: 0 win: 4 score: 28.0573342511
try !! char: 0 win: 15 score: 28.205319167
try !! char: 0 win: 24 score: 29.0594048523
try !! char: 0 win: 19 score: 29.7853003534
try !! char: 0 win: 5 score: 30.2620724526
try !! char: 0 win: 7 score: 31.479678884
bwd_pass use 5.833
fwd_infer use 0.112
[27 26 17 8 12 11 3 21 18 15 23 28 14 24 10 6 19 16 7 5]
count 20
forward_pass use 0.871
try !! char: 0 win: 27 score: 33.8280306876
try !! char: 0 win: 26 score: 34.2232459437
...
捕捉错误，返回空，在外层将出错样本结果映射到第0个字符
Reference implementation of a two-level RCN model on MNIST classification. See the Science article "A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs" and Vicarious Blog for details.
Note: this is an unoptimized reference implementation and is not intended for production.
Note: Python 2.7 is supported. The code was tested on OSX 10.11. It may work on other system platforms but not guaranteed.
Before starting please make sure gcc is installed (brew install gcc) and up to date in order to compile the various dependencies (particularly numpy).
Clone the repository:
git clone https://github.com/sloanyyc/science_rcn.git
Simple Install:
cd science_rcn
make
Manual Install (setting up a virtual environment beforehand is recommended):
cd science_rcn
python setup.py install
If you installed via make you need to activate the virtual environment:
source venv/bin/activate
To run a small unit test that trains and tests on 20 MNIST images using one CPU (takes ~2 minutes, accuracy is ~60%):
python science_rcn/run.py
To run a slightly more interesting experiment that trains on 100 images and tests on 20 MNIST images using multiple CPUs (takes <1 min using 7 CPUs, accuracy is ~90%):
python science_rcn/run.py --train_size 100 --test_size 20 --parallel
To test on the full 10k MNIST test set, training on 1000 examples (could take hours depending on the number of available CPUs, average accuracy is ~97.7+%):
python science_rcn/run.py --full_test_set --train_size 1000 --parallel --pool_shape 25 --perturb_factor 2.0
Check out our related blog post.
Yann LeCun (Courant Institute, NYU) and Corinna Cortes (Google Labs, New York) hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.