We are going to verify our deep learning environment installation by building and training a simple fully-connected neural network to perform classification on images of handwritten digits from the MNIST dataset. MNIST is an introductory dataset that contains 70,000 images, thus enabling us to quickly train a small model on a CPU and extremely fast on the GPU. In this simple example, we are only interested in testing our deep learning setup.
We start by using the keras built-in function to download and load the train and test datasets associated with MNIST:
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.utils import np_utils
from keras.optimizers import SGD
from keras.layers.core import Dense,Activation
The training set has 60,000 samples and the test set has 10,000 samples. The dataset is balanced...
