Line 32 returns our sequential model (we will use it in our training script). Once your model has all of the components you desire, you can return the object so that it can be be compiled later. Order matters - you must call model.add in the order in which you want to insert layers, normalization methods, softmax classifiers, etc. Notice on each of these lines of code that we call model.add to assemble our CNN with the appropriate building blocks. ShallowNet contains one CONV => RELU layer followed by a softmax classifier ( Lines 22-29). We’ll then add each layer to to the Sequential class, one at a time. Notice how on Line 18, we initialize the model as an instance of the Sequential class. Line 15 defines the shallownet_sequential model builder method. # return the constructed network architecture # define the first (and only) CONV => RELU layer # initialize the model along with the input shape to be Let’s go ahead and create the sequential model now: def shallownet_sequential(width, height, depth, classes): To implement our sequential model, we need the Sequential import on Line 3. Notice how all of our Keras imports on Lines 2-13 come from tensorflow.keras (also known as tf.keras ). Open up the models.py file in your project structure and insert the following code: # import the necessary packagesįrom import Modelįrom import Sequentialįrom import BatchNormalizationįrom import AveragePooling2Dįrom import MaxPooling2Dįrom import Conv2Dįrom import Activationįrom import Dropoutįrom import Flattenįrom import Inputįrom import Denseįrom import concatenate Let’s go ahead and implement a basic Convolutional Neural Network using TensorFlow 2.0 and Keras’ Sequential API. Keras Sequential API is by far the easiest way to get up and running with Keras, but it’s also the most limited - you cannot create models that:Įxamples of seminal sequential architectures that you may have already used or implemented include: Implementing a Sequential model with Keras and TensorFlow 2.0 Figure 1: The “Sequential API” is one of the 3 ways to create a Keras model with TensorFlow 2.0.Ī sequential model, as the name suggests, allows you to create models layer-by-layer in a step-by-step fashion. An accuracy/loss curve plot will be output to a. The model will be trained on the CIFAR-10 dataset. The training script, train.py, will load a model depending on the provided command line arguments. Our models.py contains three functions to build Keras/TensorFlow 2.0 models using the Sequential, Functional and Model subclassing APIs, respectively. Then extract the files and inspect the directory contents with the tree command: $ tree -dirsfirst Go ahead and grab the source code to this post by using the “Downloads” section of this tutorial. Once our training script is implemented we’ll then train each of the sequential, functional, and subclassing models, and review the results.įurthermore, all code examples covered here will be compatible with Keras and TensorFlow 2.0. I’ll then show you how to train each of these model architectures. In the first half of this tutorial, you will learn how to implement sequential, functional, and model subclassing architectures using Keras and TensorFlow 2.0. Looking for the source code to this post? Jump Right To The Downloads Section 3 ways to create a Keras model with TensorFlow 2.0 (Sequential, Functional, and Model subclassing) To learn more about Sequential, Functional, and Model subclassing with Keras and TensorFlow 2.0, just keep reading! You can start by choosing your own datasets or using our PyimageSearch’s assorted library of useful datasets.īring data in any of 40+ formats to Roboflow, train using any state-of-the-art model architectures, deploy across multiple platforms (API, NVIDIA, browser, iOS, etc), and connect to applications or 3rd party tools. Sign up or Log in to your Roboflow account to access state of the art dataset libaries and revolutionize your computer vision pipeline. Roboflow has free tools for each stage of the computer vision pipeline that will streamline your workflows and supercharge your productivity. It allows us to observe how each method impacts the model’s performance. Inside of this tutorial you’ll learn how to utilize each of these methods, including how to choose the right API for the job.Ī dataset is crucial for implementing and understanding the difference between Sequential, Functional, and Model Subclassing in TensorFlow 2.0. Keras and TensorFlow 2.0 provide you with three methods to implement your own neural network architectures: Click here to download the source code to this post
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