Tuner image12/18/2023 ![]() ![]() ![]() Start_neurons : number of conv units in the first layer Input_size : tuple (h, w, ch) for the input dimentions Generates U-Net architecture model (refactored version) Let’s look at the documentation to see the variable parameters: Signature: ![]() I already imported the model, and I’m going to initialize an object of the model’s class. Since U-NET was introduced back in 2015, there are multiple implementations already available for us. If you’re interested in U-NET and how it works, I highly recommend reading this research paper. Stamp region is segmented in a single maskīasically, for an input image that contains some objects, our deep neural net, when trained, should segment all objects of our interest and return a set of masks each mask corresponds to an object of a particular class. Real UNET’s output for an input image introduced previously. It’s not a toy problem, which is important to mention because you’ve probably seen other articles that aren’t based on real projects. Why real projects matterĮverything that I’ll be doing is based on a real project. In this article, I’ll tell you how I like to implement Keras Tuner in deep learning projects. Hyperparameter Tuning in Python: a Complete Guide 2021 It’s a great tool that helps with hyperparameter tuning in a smart and convenient way. If, like me, you’re a deep learning engineer working with TensorFlow/Keras, then you should consider using Keras Tuner. Even though tuning might be time- and CPU-consuming, the end result pays off, unlocking the highest potential capacity for your model. To select the right set of hyperparameters, we do hyperparameter tuning. You can think of learning rate value as a good example of parameters in a training configuration. Training algorithm configuration, on the other hand, influences the speed and quality of the training process. ![]() In case of deep learning, these can be things like number of layers, or types of activation functions. Model configuration can be defined as a set of hyperparameters which influences model architecture. Finding an optimal configuration, both for the model and for the training algorithm, is a big challenge for every machine learning engineer. The performance of your machine learning model depends on your configuration. ![]()
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