This repository contains my capstone project for Udacity's Machine Learning Engineering Nanodegree. Below is a brief description of the dataset and instructions to reproduce my results.
The purpose of the capstone project was to examine street view images of house numbers and predict all the numbers present in the image. The evaluation metric for the project is per digit accuracy. In other words, the number of correct digits predicted divided by the total number of digits in the dataset. Our best model achieves 95% per digit accuracy after ten epochs of training (going through the dataset ten times).
This project works on Anaconda's distribution of Python 3.5
Once you have Anaconda installed:
$ conda env create capstone_kvn219 -f environment.yml
$ source activate capstone_kvn219
You'll have two options. The recommended option is to download the TFRecords, I've preprocessed. The second, is to download and process the data on your own.
- Go to records/README.md
- Download train, test, and validation data and place them in the records/ directory.
cd svhn
python download.py
python wrangle.py
cd ..
$ python convert_to_records.py
$ python main.py --name=demo --mode=train --log=run1 --num_epochs=10
Next you'll see the following output. This is to help you keep track of parameters.
Note: Some directories will not apply if you downloaded the data from dropbox.
$ python main.py --name=demo --mode=train --log=run1 --num_epochs=10
MULTI-SVHN CLASSIFICATION
Running project: demo
Mode: train
Is training: True
Log name: run1
Checkpoint dir: /Users/kvn219/PycharmProjects/multi-number-recognition/checkpoints/demo/
Loading data from train.tfrecords
Num epochs: 10
Batch size: 36
Learning rate: 0.0001
Random crop: False
Grayscale: False
Channels: 3
Starting new train session
Training...
Training takes awhile, so you should see something like this after 15 mins.
...
Starting new train session
Training...
[20:12:03] Step: 1000, Loss: 005.53591, Seq Acc: 0.08, Dig Acc: 0.65
[20:14:19] Step: 2000, Loss: 003.99407, Seq Acc: 0.19, Dig Acc: 0.76
[20:16:40] Step: 3000, Loss: 004.14222, Seq Acc: 0.22, Dig Acc: 0.76
[20:18:58] Step: 4000, Loss: 002.56051, Seq Acc: 0.53, Dig Acc: 0.87
We've set up the program like this so that you can stop the program [KeyboardInterrupt]. And pick up where you left off later by using the same command.
$ python main.py --name=demo --mode=train --log=run1 --num_epochs=10
...
Restored model from: /Users/kvn219/PycharmProjects/multi-number-recognition/checkpoints/demo/demo-4080
Training...
[20:23:12] Step: 5000, Loss: 002.25958, Seq Acc: 0.47, Dig Acc: 0.85
[20:25:40] Step: 6000, Loss: 001.39092, Seq Acc: 0.64, Dig Acc: 0.91
...
To get validation results, keep the same name, change the mode to "valid" and reduce the num_epochs to 1.
$ python main.py --name=demo --mode=valid --num_epochs=1
0it [00:00, ?it/s]
Restoring...
/Users/kvn219/PycharmProjects/multi-number-recognition/checkpoints/demo/demo-1000
Stopping evaluation at 1 epochs, 1 steps.
Total time to run: 0:00:16.118897
1it [00:16, 16.76s/it]
Restoring...
/Users/kvn219/PycharmProjects/multi-number-recognition/checkpoints/demo/demo-2000
Stopping evaluation at 1 epochs, 1 steps.
Total time to run: 0:00:15.927491
2it [00:33, 16.70s/it]
Restoring...
/Users/kvn219/PycharmProjects/multi-number-recognition/checkpoints/demo/demo-3000
Stopping evaluation at 1 epochs, 1 steps.
Total time to run: 0:00:17.427519
3it [00:51, 17.16s/it]
Restoring...
/Users/kvn219/PycharmProjects/multi-number-recognition/checkpoints/demo/demo-4000
Stopping evaluation at 1 epochs, 1 steps.
Total time to run: 0:00:16.658947
4it [01:08, 17.21s/it]
Restoring...
/Users/kvn219/PycharmProjects/multi-number-recognition/checkpoints/demo/demo-5000
Stopping evaluation at 1 epochs, 1 steps.
Total time to run: 0:00:17.136208
5it [01:26, 17.41s/it]
Results saved to /Users/kvn219/PycharmProjects/multi-number-recognition/results/demo/valid-demo.csv
checkpoint digit_accuracy sequence_accuracy
0 1000 0.657715 0.075794
1 2000 0.740248 0.202088
2 3000 0.810117 0.373621
3 4000 0.846223 0.482516
4 5000 0.869377 0.551180
(capstone)
Your results will be saved to /results/valid-name_of_task.csv
The same applies for testing. You'll just need to change the mode.
$ python main.py --name=test --mode=test --num_epochs=1
To view the model in tensorboard, you can enter the following:
$ tensorboard --logdir=./logs --port 6006
Starting TensorBoard b'39' on port 6006
(You can navigate to http://192.168.1.9:6006)
WARNING:tensorflow
...
Go to your browser and go to the url: http://192.168.1.9:6006/
We also provide a supplemental metric: sequential accuracy, which measures the number of correct predicted sequences divided by the number of total sequences in the dataset.
Two good papers on the subject are from Goodfellow et al. (2014) and Netzer et al. (2011). And for more recent articles: Jaderberg et al. (2015) and Ba et al. (2014) are also very useful resources. Jaderberg presents the use of Spatial Transformer Networks and Ba attention-based models. Two great examples of SVHN projects are from fellow Udacity students from camigord and hangyao.
Much of the work in this repo is directly inspired by the work of the papers and projects mentioned above.
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