Example: Handwritten Digit Classification¶

As an example for the classification task, we perform a classification of hand-written digits using the MNIST database. There, images of single hand-written digits are stored, and a training and test set is provided, which we can access with our bob.db.mnist database interface.

Note

In fact, to minimize the dependencies to other packages, the bob.db.mnist database interface is replaced by a local interface.

In our experiments, we simply use the pixel gray values as features. Since the gray values are discrete in range $[0, 255]$ , we can employ both the stump decision classifiers and the look-up-table’s. Nevertheless, other discrete features, like Local Binary Patterns (LBP) could be used as well.

Running the example script¶

The script boosting_example.py is provided to execute digit classification tasks. This script has several command line parameters, which vary the behavior of the training and/or testing procedure. All parameters have a long value (starting with --) and a shortcut (starting with a single -). These parameters are (see also boosting_example.py --help):

To control the type of training, you can select:

--trainer-type: Select the type of weak classifier. Possible values are stump and lut
--loss-type: Select the loss function. Possible values are tan, log and exp. By default, a loss function suitable to the trainer type is selected.
--number-of-boosting-rounds: The number of weak classifiers to select.
--multi-variate (only valid for LUT trainer): Perform multi-variate classification, or binary (one-to-one) classification.
--feature-selection-style (only valid for multi-variate training): Select the feature for each output independent or shared?

To control the experimentation, you can choose:

--digits: The digits to classify. For multi-variate training, one classifier is trained for all given digits, while for uni-variate training all possible one-to-one classifiers are trained.
--all: Select all 10 digits.
--classifier-file: Save the trained classifier(s) into the given file and/or read the classifier(s) from this file.
--force: Overwrite the given classifier file if it already exists.

For information and debugging purposes, it might be interesting to use:

--verbose (can be used several times): Increases the verbosity level from 0 (error) over 1 (warning) and 2 (info) to 3 (debug). Verbosity level 2 (-vv) is recommended.
--number-of-elements: Reduce the number of elements per class (digit) to the given value.

Four different kinds of experiments can be performed:

Uni-variate classification using the stump classifier bob.learn.boosting.StumpMachine, classifying digits 5 and 6:
```
$ boosting_example.py -vv --trainer-type stump --digits 5 6
```
Uni-variate classification using the LUT classifier bob.learn.boosting.LUTMachine, classifying digits 5 and 6:
```
$ boosting_example.py -vv --trainer-type lut --digits 5 6
```
Multi-variate classification using LUT classifier bob.learn.boosting.LUTMachine and shared features, classifying all 10 digits:
```
$ boosting_example.py -vv --trainer-type lut --all-digits --multi-variate --feature-selection-style shared
```
Multi-variate classification using LUT classifier bob.learn.boosting.LUTMachine and independent features, classifying all 10 digits:
```
$ boosting_example.py -vv --trainer-type lut --all-digits --multi-variate --feature-selection-style independent
```

All experiments should be able to run using several minutes of execution time. The results of the above experiments should be the following (split in the remaining classification error on the training set, and the error on the test set)

Experiment	Training	Test
1	91.04 %	92.05 %
2	100.0 %	95.35 %
3	97.59 %	83.47 %
4	99.04 %	86.25 %

Of course, you can try out different combinations of digits for experiments 1 and 2.

One exemplary test case in details¶

Having a closer look into the example script, there are several steps that are performed. The first step is generating the training examples from the MNIST database interface. Here, we describe the more complex way, i.e., the multi-variate case.

>>> # open the database interface (will download the digits from the webpage)
>>> db = bob.learn.boosting.utils.MNIST()
>>> # get the training data for digits 0, 1
>>> training_samples, training_labels = db.data("train", labels = [0, 1])
>>> # limit the training samples (for test purposes only)
>>> training_samples = training_samples[:100]
>>> training_labels = training_labels[:100]

>>> # create the correct entries for the training targets from the classes; pre-fill with negative class
>>> training_targets = -numpy.ones((training_labels.shape[0], 2))
>>> # set positive class
>>> for i in [0,1]:
...   training_targets[training_labels == i, i] = 1
>>> training_labels[:10]
array([0, 1, 1, 1, 1, 0, 1, 1, 0, 0], dtype=uint8)
>>> training_targets[:10]
array([[ 1., -1.],
       [-1.,  1.],
       [-1.,  1.],
       [-1.,  1.],
       [-1.,  1.],
       [ 1., -1.],
       [-1.,  1.],
       [-1.,  1.],
       [ 1., -1.],
       [ 1., -1.]])

Now, we can train the classifier using the bob.learn.boosting.Boosting boosting trainer. Here, we use the multi-variate LUT trainer bob.learn.boosting.LUTTrainer with logit loss bob.learn.boosting.LogitLoss:

>>> weak_trainer = bob.learn.boosting.LUTTrainer(
...       maximum_feature_value = 256,
...       number_of_outputs = 2,
...       selection_style = 'independent'
... )
>>> loss_function = bob.learn.boosting.LogitLoss()
>>> strong_trainer = bob.learn.boosting.Boosting(weak_trainer, loss_function)

>>> # perform training for 100 rounds (i.e., select 100 weak machines)
>>> strong_classifier = strong_trainer.train(training_samples.astype(numpy.uint16), training_targets, 10)

Having the strong classifier (which is of type bob.learn.boosting.BoostedMachine), we can classify the test samples:

 >>> # get the test data for digits 0, 1
 >>> test_samples, test_labels = db.data("test", labels = [0, 1])

 >>> # create the correct entries for the test targets from the classes; pre-fill with negative class
 >>> test_targets = -numpy.ones((test_labels.shape[0], 2))
 >>> # set positive class
 >>> for i in [0,1]:
 ...   test_targets[test_labels == i, i] = 1

>>> # classify the test samples
>>> scores = numpy.zeros(test_targets.shape)
>>> classification = numpy.zeros(test_targets.shape)
>>> strong_classifier(test_samples.astype(numpy.uint16), scores, classification)

>>> # evaluate the results
>>> row_sum = numpy.sum(test_targets == classification, 1)
>>> # the example is correctly classified, when all test labels correspond to all target labels
>>> correctly_classified = numpy.sum(row_sum == 2)
>>> correctly_classified
2004
>>> classification.shape[0]
2115