machine_learning/rbm.cpp

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machine_learning/rbm.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 binary(const array in)
{
// Choosing "1" with probability sigmoid(in)
return (in > randu(in. dims())).as(f32);
}
class rbm {
private:
array weights;
array h_bias;
array v_bias;
// Add bias input to the output from previous layer
array vtoh(const array &v)
{
return binary(prop_up(v));
}
array htov(const array &h)
{
return binary(prop_down(h));
}
public:
rbm() {}
rbm(int v_size, int h_size) :
weights(randu(h_size, v_size)/100 - 0.05),
h_bias(constant(0, 1, h_size)),
v_bias(constant(0, 1, v_size))
{
}
array prop_up(const array &v)
{
array h_bias_tile = tile(h_bias, v.dims(0));
return sigmoid(h_bias_tile + matmulNT(v, weights));
}
array prop_down(const array &h)
{
array v_bias_tile = tile(v_bias, h.dims(0));
return sigmoid(v_bias_tile + matmul(h, weights));
}
void gibbs_vhv(array &vt, array &ht, const array &v, int k = 1)
{
vt = v;
for (int i = 0; i < k; i++) {
ht = vtoh(vt);
vt = htov(ht);
}
}
void gibbs_hvh(array &vt, array &ht, const array &h, int k = 1)
{
ht = h;
for (int i = 0; i < k; i++) {
vt = htov(ht);
ht = vtoh(vt);
}
}
void train(const array &in,
double lr = 0.1,
int num_epochs = 15,
int batch_size = 100,
int k = 1, bool verbose = false)
{
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 = vtoh(v_pos);
array v_neg, h_neg;
gibbs_hvh(v_neg, h_neg, h_pos, k);
// Update 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);
}
}
if (verbose) printf("\n");
}
};
int rbm_demo(bool console, int perc)
{
printf("** ArrayFire RBM 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);
dim4 dims = train_images. dims();
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();
rbm network(train_feats.dims(1), 2000);
network.train(train_feats,
0.1, // learning rate
15, // num epochs
100, // batch size
1, // k
true);
// Test reconstructed images
for (int ii = 0; ii < 5; ii++) {
array in = test_feats(ii, span);
array res, tmp;
network.gibbs_vhv(res, tmp, in);
in = moddims(in , dims[0], dims[1]);
res = moddims(res, dims[0], dims[1]);
in = round(in);
res = round(res);
printf("Reconstructed Error for image %2d: %.4f\n", ii,
sum<float>(abs(in - res)) / feature_size);
}
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 rbm_demo(console, perc);
} catch (af::exception &ae) {
std::cerr << ae. what() << std::endl;
}
return 0;
}