SemeionHandwrittenDigit

Semeion Handwritten Digit

Dataset

• Link: Semeion Handwritten Digit Data Set
• Data
  • Image size: 16x16
  • Number of images: 1593

Usage

# Training
python3 backward.py
# Testing
python3 test.py
# Predict actual image (bug)
python3 app.py path/to/images ...

Class ImageDataSet

(with subclass ImageData)

The purpose is to act like Tensorflow official mnist dataset object. I havn't see the source code of how Tensorflow design the class. I just imitate the functionality.

Note: Current only support one-hot label. (transfer to one-hot label before creating the instance/object)

DataSetObject = ImageDataSet(data, label, test_set_ratio=0.3, random_seed=87)

# Get a batch of training data
images, labels = data.train.next_batch(BATCH_SIZE)
# Get total training number
training_number = DataSetObject.train.num_examples

# Get all the testing data and label
testData = DataSetObject.test.images
testLabel = DataSetObject.test.labels

Result

Using 80% training data and 20% testing data in 1593 instances.

After 50000 training step(s), loss on training batch is 0.075765
After 50000 training step(s), test accuracy = 0.909091

There are strange things here. (Haven't found the answer yet)

1. Training loss did change but very small
2. Test accuracy barely change <- very weird
3. My prediction of app.py not even close the accuracy (90% vs less than 10%)

possible answer

• maybe is shuffle problem, but I did shuffle the dataset twice
• maybe is test set problem, but training loss is acting weird too. (far away from I playing around with MNIST)
• or maybe is the parameters problem, but I don't think it will affect that much...
• or maybe the training set is too small that the weights keep stay (overfit) in the final state...

Links

Others Dataset Class implementation

• tentone/SemeionNet
  • Dataset Class
  • Load dataset

Other Semeion code

• SemeionNet-CNN (R)