#Keras sequential model install#

Keras distinguishes between binary_crossentropy (2 classes) and categorical_crossentropy (>2 classes), so we’ll use the latter. Since we’re using a Softmax output layer, we’ll use the Cross-Entropy loss. Keras has many other optimizers you can look into as well.

We’ll stick with a pretty good default: the Adam gradient-based optimizer. We decide 3 key factors during the compilation step: Compiling the Modelīefore we can begin training, we need to configure the training process. Test_images = (test_images / 255 ) - 0.5 # Flatten the images. Train_images = (train_images / 255 ) - 0.5

#Keras sequential model install#

Let’s first install some packages we’ll need:įrom tensorflow. I’m assuming you already have a basic Python installation ready (you probably do). Our output will be one of 10 possible classes: one for each digit. We’ll flatten each 28x28 into a 784 dimensional vector, which we’ll use as input to our neural network. It’s simple: given an image, classify it as a digit.Įach image in the MNIST dataset is 28x28 and contains a centered, grayscale digit. We’re going to tackle a classic machine learning problem: MNIST handwritten digit classification. Just want the code? The full source code is at the end.

My introduction to Neural Networks covers everything you need to know (and more) for this post - read that first if necessary. This post is intended for complete beginners to Keras but does assume a basic background knowledge of neural networks. In this post, we’ll see how easy it is to build a feedforward neural network and train it to solve a real problem with Keras. Keras is a simple-to-use but powerful deep learning library for Python.