Mastering Machine Learning Algorithms

Examples of deep convolutional networks with Keras

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) ...