TF IMAGE RESIZE HOW TO
TF IMAGE RESIZE CODE

Let’s look at an example using once again our UNet. Mocking also helps us control the code’s behavior and simulate expensive calls.

TF IMAGE RESIZE CODE

In our case a dummy object might be a 2d tensor with all ones which mimics an actual image (just like the “input_image in the first code snippet). By dummy objects, we refer to simple, easy to code objects that have the same structure with our real objects but contain fake or useless data. Mocking makes it very easy to replace complex logic or heavy dependencies when testing code using dummy objects. Mocking classes and functions are super common when writing java for example but in Python is very underutilized. Pretty cool hah? MockingĪnother super important topic you should be aware of is Mocking and mock objects. It has exactly the same baselines with the caveat that we need to call the “super()” function inside the “setUp”, which enables “tf.test” to do its magic. In that case, our code morphed into this: Since we use Tensorflow to program our model we can take advantage of “ tf.test”, which is an extension of unittest but it contains assertions tailored to Tensorflow code (yup I was shocked when I found that out too). Tests in Tensorflow: tf.testīut here I’m going to discuss another one, a less known one. But honestly it doesn’t have that big of a difference so you should be fine with either of them. I personally use pytest most of the times because it feels a bit simpler and it supports a few nice to have things like fixtures and test parameterization( which I’m not gonna go into details here, you can check the official docs for more). I suggest playing with them a little before you decide what suits you best. Well, now they can.Īnother two powerful frameworks are pytest and nose, which are pretty much governed by the same principles. This is helpful because all tests should run independently and generally they can’t share information. In setUp() we can define our inputs that can be accessed by all tests and in tearDown() we can dissolve them( see snippet in the next chapter). This class helps us set unique test cases with different inputs because it comes with “ setUp()” and “tearDown()” methods. Our UnetTest class inherits the “ unittest.TestCase” class. To run unit tests, we call the “unittest.main()” function which discovers all tests within the module, runs them and prints their output. (This is a convention but it also enables unittest’s autodiscovery functionality, which is the ability of the library to automatically detect all unit tests within a project or a module so you don’t have to run them one by one) In general, test functions are named with “test” as a prefix followed by the name of the function they test.

We have our test class which includes a “test normalize” function as a method. Sure testing can take up a lot of your precious time but it’s 100% worth it. Unit tests are tremendously useful because they:Įnsure that the code does what is supposed to doĭon’t tell me that you don’t want at least some of the above. To prevent all these things before they even occur. We might even discover that our model was in fact corrupted all this time. That’s all good but what happens when the model will be deployed into a server and used in an actual public faced application? Most likely it will crash because some users may be sending wrong data or because of some silent bug that messes up our data preprocessing pipeline. And then we just need to increase its accuracy until it reaches an acceptable point. When developing a neural network, most of us don’t care about catching all possible exceptions, finding all corner cases, or debugging every single function. We will start on why we need them in our code, then we will do a quick catch up on the basics of testing in python, and then we will go over a number of practical real-life scenarios.

TF IMAGE RESIZE HOW TO

In this article, we are going to focus on how to properly test machine learning code, analyze some best practices when writing unit tests and present a number of example cases where testing is kind of a necessity. Note that this post is the third part of the Deep Learning in Production course where we discover how to convert a notebook into production-ready code that can be served to millions of users. After all, machine learning is not different from any other software. But when your code is going to live in a production environment, making sure that it actually does what is intended should be a priority. That’s why most of the TensorFlow and PyTorch code out there does not include unit testing.

Programming a deep learning model is not easy (I’m not going to lie) but testing one is even harder.