Programming Machine Learning

Hands On: Keras Playground

Now that you have some Keras under your belt, here is an exercise for you: take the MNIST network we built in Part II of this book and rewrite it from scratch using Keras.

Keras already comes with a few common datasets, MNIST included—so you don’t have to use our mnist.py library. Instead, you can load MNIST and one-hot encode its labels with this piece of code:

from keras.datasets import mnist
from keras.utils import to_categorical

(X_train_raw, Y_train_raw), (X_test_raw, Y_test_raw) = mnist.load_data()
X_train = X_train_raw.reshape(X_train_raw.shape[0], -1) / 255
X_test = X_test_raw.reshape(X_test_raw.shape[0], -1) / 255
Y_train = to_categorical(Y_train_raw)
Y_test = to_categorical(Y_test_raw) ...