machine_learning/neural_network.cpp
/*******************************************************
* Copyright (c) 2014, ArrayFire
* All rights reserved.
*
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
* http://arrayfire.com/licenses/BSD-3-Clause
********************************************************/
#include <arrayfire.h>
#include <stdio.h>
#include <vector>
#include <string>
#include <af/util.h>
#include <math.h>
#include "mnist_common.h"
using namespace af;
using std::vector;
float accuracy(const array& predicted, const array& target)
{
array val, plabels, tlabels;
max(val, tlabels, target, 1);
max(val, plabels, predicted, 1);
return 100 * count<float>(plabels == tlabels) / tlabels. elements();
}
// Derivative of the activation function
array deriv(const array &out)
{
return out * (1 - out);
}
// Cost function
double error(const array &out,
const array &pred)
{
array dif = (out - pred);
return sqrt((double)(sum<float>(dif * dif)));
}
class ann {
private:
int num_layers;
vector<array> weights;
// Add bias input to the output from previous layer
array add_bias(const array &in);
vector<array> forward_propagate(const array& input);
void back_propagate(const vector<array> signal,
const array &pred,
const double &alpha);
public:
// Create a network with given parameters
ann(vector<int> layers, double range=0.05);
// Output after single pass of forward propagation
array predict(const array &input);
// Method to trian the neural net
double train(const array &input, const array &target,
double alpha = 1.0,
int max_epochs = 300,
int batch_size = 100,
double maxerr = 1.0,
bool verbose = false);
};
array ann::add_bias(const array &in)
{
// Bias input is added on top of given input
return join(1, constant(1, in. dims(0), 1), in);
}
vector<array> ann::forward_propagate(const array& input)
{
// Get activations at each layer
vector<array> signal(num_layers);
signal[0] = input;
for (int i = 0; i < num_layers - 1; i++) {
array in = add_bias(signal[i]);
array out = matmul(in, weights[i]);
signal[i + 1] = sigmoid(out);
}
return signal;
}
void ann::back_propagate(const vector<array> signal,
const array &target,
const double &alpha)
{
// Get error for output layer
array out = signal[num_layers - 1];
array err = (out - target);
int m = target.dims(0);
for (int i = num_layers - 2; i >= 0; i--) {
array in = add_bias(signal[i]);
array delta = (deriv(out) * err).T();
// Adjust weights
array grad = -(alpha * matmul(delta, in)) / m;
weights[i] += grad. T();
// Input to current layer is output of previous
out = signal[i];
err = matmulTT(delta, weights[i]);
// Remove the error of bias and propagate backward
err = err(span, seq(1, out.dims(1)));
}
}
ann::ann(vector<int> layers, double range) :
num_layers(layers.size()),
weights(layers.size() - 1)
{
// Generate uniformly distributed random numbers between [-range/2,range/2]
for (int i = 0; i < num_layers - 1; i++) {
weights[i] = range * randu(layers[i] + 1, layers[i + 1]) - range/2;
}
}
array ann::predict(const array &input)
{
vector<array> signal = forward_propagate(input);
array out = signal[num_layers - 1];
return out;
}
double ann::train(const array &input, const array &target,
double alpha, int max_epochs, int batch_size,
double maxerr, bool verbose)
{
const int num_samples = input.dims(0);
const int num_batches = num_samples / batch_size;
double err = 0;
// Training the entire network
for (int i = 0; i < max_epochs; i++) {
for (int j = 0; j < num_batches - 1; j++) {
int st = j * batch_size;
int en = st + batch_size - 1;
array x = input(seq(st, en), span);
array y = target(seq(st, en), span);
// Propagate the inputs forward
vector<array> signals = forward_propagate(x);
array out = signals[num_layers - 1];
// Propagate the error backward
back_propagate(signals, y, alpha);
}
// Validate with last batch
int st = (num_batches - 1) * batch_size;
int en = num_samples - 1;
array out = predict(input(seq(st, en), span));
err = error(out, target(seq(st, en), span));
// Check if convergence criteria has been met
if (err < maxerr) {
printf("Converged on Epoch: %4d\n", i + 1);
return err;
}
if (verbose) {
if ((i + 1) % 10 == 0) printf("Epoch: %4d, Error: %0.4f\n", i+1, err);
}
}
return err;
}
int ann_demo(bool console, int perc)
{
printf("** ArrayFire ANN Demo **\n\n");
array train_images, test_images;
array train_target, test_target;
int num_classes, num_train, num_test;
// Load mnist data
float frac = (float)(perc) / 100.0;
setup_mnist<true>(&num_classes, &num_train, &num_test,
train_images, test_images, train_target, test_target, frac);
int feature_size = train_images.elements() / num_train;
// Reshape images into feature vectors
array train_feats = moddims(train_images, feature_size, num_train).T();
array test_feats = moddims(test_images , feature_size, num_test ).T();
train_target = train_target.T();
test_target = test_target.T();
// Network parameters
vector<int> layers;
layers.push_back(train_feats.dims(1));
layers.push_back(100);
layers.push_back(50);
layers.push_back(num_classes);
// Create network
ann network(layers);
// Train network
timer::start();
network.train(train_feats, train_target,
2.0, // learning rate / alpha
250, // max epochs
100, // batch size
0.5, // max error
true); // verbose
af::sync();
double train_time = timer::stop();
// Run the trained network and test accuracy.
array train_output = network.predict(train_feats);
array test_output = network.predict(test_feats );
// Benchmark prediction
af::sync();
timer::start();
for (int i = 0; i < 100; i++) {
network.predict(test_feats);
}
af::sync();
double test_time = timer::stop() / 100;
printf("\nTraining set:\n");
printf("Accuracy on training data: %2.2f\n",
accuracy(train_output, train_target));
printf("\nTest set:\n");
printf("Accuracy on testing data: %2.2f\n",
accuracy(test_output , test_target ));
printf("\nTraining time: %4.4lf s\n", train_time);
printf("Prediction time: %4.4lf s\n\n", test_time);
if (!console) {
// Get 20 random test images.
test_output = test_output.T();
display_results<true>(test_images, test_output, test_target.T(), 20);
}
return 0;
}
int main(int argc, char** argv)
{
int device = argc > 1 ? atoi(argv[1]) : 0;
bool console = argc > 2 ? argv[2][0] == '-' : false;
int perc = argc > 3 ? atoi(argv[3]) : 60;
try {
af::setDevice(device);
af::info();
return ann_demo(console, perc);
} catch (af::exception &ae) {
std::cerr << ae. what() << std::endl;
}
return 0;
}