#KERAS SEQUENTIAL HOW TO#
#KERAS SEQUENTIAL CODE#

# 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

#KERAS SEQUENTIAL CODE#

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.

#KERAS SEQUENTIAL HOW TO#

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)

Keras sequential