Machine Learning for OpenCV

Preprocessing the MNIST dataset

To make sure we get the same result every time we run the experiment, we will pick a random seed for NumPy's random number generator. This way, shuffling the training samples from the MNIST dataset will always result in the same order:

In [1]: import numpy as np...     np.random.seed(1337)

Keras provides a loading function similar to train_test_split from scikit-learn's model_selection module. Its syntax might look strangely familiar to you:

In [2]: from keras.datasets import mnist...     (X_train, y_train), (X_test, y_test) = mnist.load_data()

In contrast to other datasets we have encountered so far, MNIST comes with a pre-defined train-test split. This allows the dataset to be used as a benchmark, as the test ...