- Solve captcha using TensorFlow.
- Learn CNN and TensorFlow by a practical project.
Follow the steps, run the code, and it works!
the easiest model requires only 2 convolutional layers. accuracy is 90%+
There are several more steps to put this prototype on production.
Ping me for paid technical supports.
this is an introduction to Tensorflow and TensorBoard.
skip this section if you are a experienced user.
The dataset is similar to MNIST, The model is also similar to it.
- Using only 1 softmax layer.
- Accuracy: 80%+.
- Training data: 20000 images.
- Time cost: <3min. using GTX-1080.
# generating test data
$ python datasets/gen_captcha.py -d --npi 1 -n 2000
# run the model
$ python simple_softmax.py images/char-1-epoch-2000/Output While Training:
$ time python simple_softmax.py images/char-1-epoch-2000/
data loaded
train images: 20000. test images: 4000
label_size: 10, image_size: 6000
...
step = 9100, accuracy = 91.10%
step = 9200, accuracy = 91.40%
step = 9300, accuracy = 92.00%
step = 9400, accuracy = 91.40%
step = 9500, accuracy = 91.35%
step = 9600, accuracy = 90.80%
step = 9700, accuracy = 91.60%
step = 9800, accuracy = 91.65%
step = 9900, accuracy = 90.50%
testing accuracy = 91.05%
real2m46.478s
user2m29.704s
sys0m17.828sTensorboard is a suite of visualization tools.
Tensorboard can visualize TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.
It is really cool and helpful.
some examples:
How doest it work?
- TensorFlow will write necessary info, defined by
tf.summary, to log files. - TensorBoard will parse the logs and visualize the data.
Adding tf.summary to simple_softmax.py, we got softmax_with_log.py.
Run the model using the same training dataset
$ python softmax_with_log.py images/char-1-epoch-2000/Launching TensorBoard in a new terminal
$ tensorboard --logdir=logNavigate your web browser to http://127.0.0.1:6006/ to view the TensorBoard.
the same 2-layer CNN model as talked in MNIST tutorial
- 2 Convolutional layers.
- Accuracy: 97%
- Training data: 20000 images.
- Time cost: <4min. using GTX-1080.
# generating test data
$ python datasets/gen_captcha.py -d --npi 1 -n 2000
# run the model
$ python cnn_1_char.py --data_dir images/char-1-epoch-2000/Output While Training:
$ $ time python cnn_1_char.py --data_dir images/char-1-epoch-2000/
data loaded
train images: 20000. test images: 4000
label_size: 10, image_size: 6000
...
step 9100, training accuracy = 100.00%, testing accuracy = 96.60%
step 9200, training accuracy = 100.00%, testing accuracy = 97.80%
step 9300, training accuracy = 100.00%, testing accuracy = 97.65%
step 9400, training accuracy = 100.00%, testing accuracy = 97.95%
step 9500, training accuracy = 100.00%, testing accuracy = 97.25%
step 9600, training accuracy = 98.00%, testing accuracy = 96.80%
step 9700, training accuracy = 100.00%, testing accuracy = 97.70%
step 9800, training accuracy = 100.00%, testing accuracy = 97.25%
step 9900, training accuracy = 100.00%, testing accuracy = 95.40%
testing accuracy = 97.85%
real 3m35.217s
user 3m4.168s
sys 0m31.036sAdd 1 more layer, reshape the output before calculating Loss.
- Accuracy: 97%
- Training data: 45000 images.
- Time cost: <5min. using GTX-1080.
# generating test data
$ python datasets/gen_captcha.py -d --npi=2 -n 500
# run the model
$ python cnn_n_char.py --data_dir=images/char-2-epoch-500/Output While Training:
$ time python cnn_n_char.py --data_dir=images/char-2-epoch-500/
data loaded
train images: 45000. test images: 9000
label_size: 10, image_size: 8000
...
step 9100, training accuracy = 99.00%, testing accuracy = 96.45%
step 9200, training accuracy = 100.00%, testing accuracy = 97.28%
step 9300, training accuracy = 100.00%, testing accuracy = 96.75%
step 9400, training accuracy = 100.00%, testing accuracy = 96.68%
step 9500, training accuracy = 100.00%, testing accuracy = 96.55%
step 9600, training accuracy = 100.00%, testing accuracy = 96.40%
step 9700, training accuracy = 100.00%, testing accuracy = 95.58%
step 9800, training accuracy = 99.00%, testing accuracy = 96.70%
step 9900, training accuracy = 99.00%, testing accuracy = 96.68%
testing accuracy = 97.33%
real 4m49.509s
user 4m10.980s
sys 0m34.164sthe same model as 2-char captcha.
only change the training dataset to 4-char ones.
# generating test data
$ python datasets/gen_captcha.py -d --npi=4 -n 6
# run the model
$ python cnn_n_char.py --data_dir images/char-4-epoch-6/Output While Training:
$ python cnn_n_char.py --data_dir images/char-4-epoch-6/
data loaded
train images: 30240. test images: 5040
label_size: 10, image_size: 12000
...
step 9100, training accuracy = 100.00%, testing accuracy = 92.73%
step 9200, training accuracy = 98.00%, testing accuracy = 92.86%
step 9300, training accuracy = 97.50%, testing accuracy = 91.74%
step 9400, training accuracy = 99.00%, testing accuracy = 92.84%
step 9500, training accuracy = 99.00%, testing accuracy = 92.99%
step 9600, training accuracy = 99.00%, testing accuracy = 93.78%
step 9700, training accuracy = 100.00%, testing accuracy = 92.56%
step 9800, training accuracy = 99.50%, testing accuracy = 93.01%
step 9900, training accuracy = 99.00%, testing accuracy = 92.84%
testing accuracy = 93.24%$ python datasets/gen_captcha.py -h
usage: gen_captcha.py [-h] [-n N] [-t T] [-d] [-l] [-u] [--npi NPI]
[--data_dir DATA_DIR]
optional arguments:
-h, --help show this help message and exit
-n N epoch number of character permutations.
-t T ratio of test dataset.
-d, --digit use digits in dataset.
-l, --lower use lowercase in dataset.
-u, --upper use uppercase in dataset.
--npi NPI number of characters per image.
--data_dir DATA_DIR where data will be saved.examples:
1 epoch will have 10 images, generate 2000 epoches for training.
generating the dataset:
$ python datasets/gen_captcha.py -d --npi 1 -n 2000
10 choices: 0123456789
generating 2000 epoches of captchas in ./images/char-1-epoch-2000/train
generating 400 epoches of captchas in ./images/char-1-epoch-2000/test
write meta info in ./images/char-1-epoch-2000/meta.jsonpreview the dataset:
$ python datasets/base.py images/char-1-epoch-2000/
========== Meta Info ==========
num_per_image: 1
label_choices: 0123456789
height: 100
width: 60
n_epoch: 2000
label_size: 10
==============================
train images: (20000, 100, 60), labels: (20000, 10)
test images: (4000, 100, 60), labels: (4000, 10)1 epoch will have 62*61=3782 images, generate 10 epoches for training.
generating the dataset:
$ python datasets/gen_captcha.py -dlu --npi 2 -n 10
62 choices: 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ
generating 10 epoches of captchas in ./images/char-2-epoch-10/train
generating 2 epoches of captchas in ./images/char-2-epoch-10/test
write meta info in ./images/char-2-epoch-10/meta.jsonpreview the dataset:
========== Meta Info ==========
num_per_image: 2
label_choices: 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ
height: 100
width: 80
n_epoch: 10
label_size: 62
==============================
train images: (37820, 100, 80), labels: (37820, 124)
test images: (7564, 100, 80), labels: (7564, 124)1 epoch has 10*9*8*7=5040 images, generate 10 epoches for training.
generating the dataset:
$ python datasets/gen_captcha.py -d --npi=4 -n 6
10 choices: 0123456789
generating 6 epoches of captchas in ./images/char-4-epoch-6/train
generating 1 epoches of captchas in ./images/char-4-epoch-6/test
write meta info in ./images/char-4-epoch-6/meta.jsonpreview the dataset:
$ python datasets/base.py images/char-4-epoch-6/
========== Meta Info ==========
num_per_image: 4
label_choices: 0123456789
height: 100
width: 120
n_epoch: 6
label_size: 10
==============================
train images: (30240, 100, 120), labels: (30240, 40)
test images: (5040, 100, 120), labels: (5040, 40)