A well-designed API, making complicated tasks feel easy, will probably prevent a lot more pain in this world than a brilliant new design for a bedside lamp ever would. User experience (UX) should be central in application programming interface (API) design.

Our tools have great potential to cause us pain, especially in a field as complex as software engineering. Developers who unfortunately are often being let down by their tools, and left cursing at obscure error messages, wondering why that stupid library doesn't do what they thought it would. Developers who produce better code, in greater quantity, when they are kept happy and productive, working with tools they love. It is meant to be read by people, used by other developers, maintained and built upon. Code is not just meant for machines it has human users. Writing code is rarely just a private affair between you and your computer. Keras is a Deep Learning library for Python, that is simple, modular, and extensible. I'll leave that as an exercise to the reader.The Keras Blog - Francois Chollet The Keras Blog The next step would be to retrain the model with all 60,000 training examples (remember that in the model above we trained on 50,000 examples and validated on the remaining 10,000). We can also find the class with the highest prediction score with a numpy function: np.

Here you can see that the network is 99.9% certain it is a seven. Since the output of the network was a layer with 10 units and a softmax activation, we will get an array of length 10 with a prediction for each potential number. !(images/mnist-keras/test-prediction.png) imshow (train_images, cmap =plt.cm.binary) They are each 28x28 grayscale images with one color value between 0 and 255. Lets look at the first ten training images. The training set consists of 60,000 28x28 pixel images, and the test set 10,000. Let's load the data: (train_images, train_labels), (test_images, test_labels) = mnist. You can see a full list of datasets Keras has packaged up. Since working with the MNIST digits is so common, Keras provides a function to load the data. If you would like to follow along, the code is also available in a jupyter notebook here. I highly recommend reading the book if you would like to dig deeper or learn more. Much of this is inspired by the book Deep Learning with Python by François Chollet. In this post we'll use Keras to build the hello world of machine learning, classify a number in an image from the MNIST database of handwritten digits, and achieve ~99% classification accuracy using a convolutional neural network.

Francois chollet deep learning