unit test
unit test
use unittest.TestCase or pytest or nose.
For the tensorflow based test, there is a `tf.test`.
--> for tensorflow:
Data
Ensure that our data has the right format (yes I put it again here for completion)
Ensure that the training labels are correct
Test our complex processing steps such as image manipulation
Assert data completion, quality, and errors
Test the distribution of the features
Training
Run a training step and compare the weight before and after to ensure that they are updated
Check that our loss function can be actually used on our data
Evaluation:
Having tests to ensure that your metrics ( e.g accuracy, precision, and recall ) are above a threshold when iterating over different architectures
You can run speed/benchmark tests on training to catch possible overfitting
Of course, cross-validation can be in the form of a unit test
Model Architecture:
The model’s layers are actually stacking
The model’s output has the correct shape
2. mocking
Mocking makes it very easy to replace complex logic or heavy dependencies when testing code using dummy objects.
3. coverage
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