arrayfire/examples/machine_learning/rbm.cpp at master · AMD-Ecosystem/arrayfire · GitHub

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/*******************************************************

* 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 <math.h>

#include <stdio.h>

#include <af/util.h>

#include <string>

#include <vector>

#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;

}

arrayfire/examples/machine_learning/rbm.cpp at master · AMD-Ecosystem/arrayfire · GitHub

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