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deep_belief_net.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)));
}
array sigmoid_binary(const array in)
{
// Choosing "1" with probability sigmoid(in)
return (sigmoid(in) > randu(in.dims())).as(f32);
}
class rbm {
private:
array weights;
array h_bias;
array v_bias;
public:
rbm(int v_size, int h_size) :
weights(randu(h_size, v_size)/100.f),
h_bias(constant(0, 1, h_size)),
v_bias(constant(0, 1, v_size))
{
}
array get_weights()
{
return transpose(join(1, weights, transpose(h_bias)));
}
void train(const array &in, double lr, int num_epochs, int batch_size, bool verbose)
{
const int num_samples = in.dims(0);
const int num_batches = num_samples / batch_size;
for (int i = 0; i < num_epochs; i++) {
double err = 0;
for (int j = 0; j < num_batches - 1; j++) {
int st = j * batch_size;
int en = std::min(num_samples - 1, st + batch_size - 1);
int num = en - st + 1;
array v_pos = in(seq(st, en), span);
array h_pos = sigmoid_binary(tile(h_bias, num) +
matmulNT(v_pos, weights));
array v_neg = sigmoid_binary(tile(v_bias, num) +
matmul(h_pos, weights));
array h_neg = sigmoid_binary(tile(h_bias, num) +
matmulNT(v_neg, weights));
array c_pos = matmulTN(h_pos, v_pos);
array c_neg = matmulTN(h_neg, v_neg);
array delta_w = lr * (c_pos - c_neg) / num;
array delta_vb = lr * sum(v_pos - v_neg) / num;
array delta_hb = lr * sum(h_pos - h_neg) / num;
weights += delta_w;
v_bias += delta_vb;
h_bias += delta_hb;
if (verbose) {
err += error(v_pos, v_neg);
}
}
if (verbose) {
printf("Epoch %d: Reconstruction error: %0.4f\n", i + 1, err / num_batches);
}
}
}
array prop_up(const array &in)
{
return sigmoid(tile(h_bias, in.dims(0)) +
matmulNT(in, weights));
}
};
class dbn {
private:
const int in_size;
const int out_size;
const int num_hidden;
const int num_total;
std::vector<array> weights;
std::vector<int> hidden;
array 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> forward_propagate(const array& input)
{
// Get activations at each layer
vector<array> signal(num_total);
signal[0] = input;
for (int i = 0; i < num_total - 1; i++) {
array in = add_bias(signal[i]);
array out = matmul(in, weights[i]);
signal[i + 1] = sigmoid(out);
}
return signal;
}
void back_propagate(const vector<array> signal,
const array &target,
const double &alpha)
{
// Get error for output layer
array out = signal[num_total - 1];
array err = (out - target);
int m = target.dims(0);
for (int i = num_total - 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)));
}
}
public:
dbn(const int in_sz, const int out_sz,
const std::vector<int> hidden_layers) :
in_size(in_sz),
out_size(out_sz),
num_hidden(hidden_layers.size()),
num_total(hidden_layers.size() + 2),
weights(hidden_layers.size() + 1),
hidden(hidden_layers)
{
}
void train(const array &input, const array &target,
double lr_rbm = 1.0,
double lr_nn = 1.0,
const int epochs_rbm = 15,
const int epochs_nn = 300,
const int batch_size = 100,
double maxerr = 1.0, bool verbose=false)
{
// Pre-training hidden layers
array X = input;
for (int i = 0; i < num_hidden; i++) {
if (verbose) {
printf("Training Hidden Layer %d\n", i);
}
int visible = (i == 0) ? in_size : hidden[i - 1];
rbm r(visible, hidden[i]);
r.train(X, lr_rbm, epochs_rbm, batch_size, verbose);
X = r.prop_up(X);
weights[i] = r.get_weights();
if (verbose) {
printf("\n");
}
}
weights[num_hidden] = 0.05 * randu(hidden[num_hidden - 1] + 1, out_size) - 0.0025;
const int num_samples = input.dims(0);
const int num_batches = num_samples / batch_size;
// Training the entire network
for (int i = 0; i < epochs_nn; i++) {
for (int j = 0; j < num_batches; j++) {
int st = j * batch_size;
int en = std::min(num_samples - 1, 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_total - 1];
// Propagate the error backward
back_propagate(signals, y, lr_nn);
}
// Validate with last batch
int st = (num_batches - 1) * batch_size;
int en = num_samples - 1;
array out = predict(input(seq(st, en), span));
double 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;
}
if (verbose) {
if ((i + 1) % 10 == 0) printf("Epoch: %4d, Error: %0.4f\n", i+1, err);
}
}
}
array predict(const array &input)
{
vector<array> signal = forward_propagate(input);
array out = signal[num_total - 1];
return out;
}
};
int dbn_demo(bool console, int perc)
{
printf("** ArrayFire DBN 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(100);
layers.push_back(50);
// Create network
dbn network(train_feats.dims(1), num_classes, layers);
// Train network
timer::start();
network.train(train_feats, train_target,
0.2, // rbm learning rate
4.0, // nn learning rate
15, // rbm epochs
250, // nn 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 dbn_demo(console, perc);
} catch (af::exception &ae) {
std::cerr << ae.what() << std::endl;
}
}