#include <math.h>
#include <stdio.h>
#include <string>
#include <vector>
#include "mnist_common.h"
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);
}
array deriv(
const array &out) {
return out * (1 - out); }
double error(
const array &out,
const array &pred) {
array dif = (out - pred);
}
}
class rbm {
private:
array weights;
array h_bias;
array v_bias;
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)
array prop_up(const array &v) {
array h_bias_tile =
tile(h_bias, v.
dims(0));
}
array prop_down(const array &h) {
array v_bias_tile =
tile(v_bias, h.
dims(0));
}
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);
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 , 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;
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;
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,
15,
100,
1,
true);
for (int ii = 0; ii < 5; ii++) {
network.gibbs_vhv(res, tmp, in);
in =
moddims(in, dims[0], dims[1]);
res =
moddims(res, dims[0], dims[1]);
printf("Reconstructed Error for image %2d: %.4f\n", ii,
}
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 {
return rbm_demo(console, perc);
return 0;
}
A multi dimensional data container.
dim4 dims() const
Get dimensions of the array.
const array as(dtype type) const
Casts the array into another data type.
array T() const
Get the transposed the array.
dim_t elements() const
Get the total number of elements across all dimensions of the array.
Generic object that represents size and shape.
An ArrayFire exception class.
virtual const char * what() const
Returns an error message for the exception in a string format.
@ f32
32-bit floating point values
array abs(const array &in)
C++ Interface to calculate the absolute value.
array round(const array &in)
C++ Interface to round numbers.
array sigmoid(const array &in)
C++ Interface to evaluate the logistical sigmoid function.
array sqrt(const array &in)
C++ Interface to evaluate the square root.
array matmulTN(const array &lhs, const array &rhs)
C++ Interface to multiply two matrices.
array matmul(const array &lhs, const array &rhs, const matProp optLhs=AF_MAT_NONE, const matProp optRhs=AF_MAT_NONE)
C++ Interface to multiply two matrices.
array matmulNT(const array &lhs, const array &rhs)
C++ Interface to multiply two matrices.
array constant(T val, const dim4 &dims, const dtype ty=(af_dtype) dtype_traits< T >::ctype)
C++ Interface to generate an array with elements set to a specified value.
void setDevice(const int device)
Sets the current device.
array moddims(const array &in, const dim4 &dims)
C++ Interface to modify the dimensions of an input array to a specified shape.
array tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)
C++ Interface to generate a tiled array.
array randu(const dim4 &dims, const dtype ty, randomEngine &r)
C++ Interface to create an array of random numbers uniformly distributed.
array count(const array &in, const int dim=-1)
C++ Interface to count non-zero values in an array along a given dimension.
array max(const array &in, const int dim=-1)
C++ Interface to return the maximum along a given dimension.
array sum(const array &in, const int dim=-1)
C++ Interface to sum array elements over a given dimension.
seq span
A special value representing the entire axis of an af::array.