In the first example, we want to consider again the complete MNIST handwritten digit dataset, but instead of using an MLP, we are going to employ a small deep convolutional network. The first step consists of loading and normalizing the dataset:
import numpy as npfrom keras.datasets import mnistfrom keras.utils import to_categorical(X_train, Y_train), (X_test, Y_test) = mnist.load_data()width = height = X_train.shape[1]X_train = X_train.reshape((X_train.shape[0], width, height, 1)).astype(np.float32) / 255.0 X_test = X_test.reshape((X_test.shape[0], width, height, 1)).astype(np.float32) / 255.0Y_train = to_categorical(Y_train, num_classes=10)Y_test = to_categorical(Y_test, num_classes=10) ...
