Here, we use Keras to define a network that recognizes MNIST handwritten digits. We start with a very simple neural network and then progressively improve it.
Keras provides suitable libraries to load the dataset and split it into training sets X_train, used for fine-tuning our net, and tests set X_test, used for assessing the performance. Data is converted into float32 for supporting GPU computation and normalized to [0, 1]. In addition, we load the true labels into Y_train and Y_test respectively and perform a one-hot encoding on them. Let's see the code:
from __future__ import print_functionimport numpy as npfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers.core ...
