mnist.py

from neuralnetwork.neural_network import NeuralNetwork

from layers.fc_layer import FCLayer
from layers.softmax_layer import SoftmaxLayer
from layers.dropout_layer import DropOut

from activations.tanh import TanH
from activations.softmax import Softmax

from optimizers.adam import Adam

from regularizers.l2_regularizer import L2Regularizer

from losses.multi_class_cross_entropy import MultiClassCrossEntropy

from utils.mnist_reader import load_mnist
from utils.one_hot_encoding import one_hot
from utils.normalize_images import normalize_images

import numpy as np

# load MNIST from gzip files
(x_train, y_train) = load_mnist('./datas/mnist/', 'train')
(x_test, y_test) = load_mnist('./datas/mnist/', 'test')

# reshape and normalize input data
x_train = normalize_images(x_train)

# One hot encoding number 3 will become [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
y_train_enc = one_hot(y_train)

# number of classes / pixels per image
num_classes = y_train_enc.shape[0]
num_pixels = x_train.shape[0]

# Create our NN structure
net = NeuralNetwork()
net.add(FCLayer(num_pixels, 100, activation=TanH(), optimizer=Adam()))
net.add(DropOut(rate=0.0))
net.add(FCLayer(100, 50, activation=TanH(), optimizer=Adam()))
net.add(DropOut(rate=0.0))
net.add(FCLayer(50, 25, activation=TanH(), optimizer=Adam()))
net.add(DropOut(rate=0.0))
net.add(SoftmaxLayer(25, num_classes, activation=Softmax(), optimizer=Adam()))

# train
net.use(loss=MultiClassCrossEntropy(), regularizer=L2Regularizer(lambd=0.01))
net.train(x_train, y_train_enc, epochs=50, learning_rate=0.001, batch_size=256)

# check training accuracy
train_results = net.predict(x_train)
train_results = np.argmax(train_results, axis=0)
print("Accuracy on training set:", np.mean(train_results == y_train) * 100, "%")

# Check our model on the test set
x_test = normalize_images(x_test)

test_results = net.predict(x_test)
test_results = np.argmax(test_results, axis=0)

print("Accuracy on testing set:", np.mean(test_results == y_test) * 100, "%")