machine_learning/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:
* https://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 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
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
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
for (int i = 0; i < 100; i++) { network.predict(test_feats); }
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);
return dbn_demo(console, perc);
} catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
return 0;
}
af::matmul
AFAPI array matmul(const array &lhs, const array &rhs, const matProp optLhs=AF_MAT_NONE, const matProp optRhs=AF_MAT_NONE)
Matrix multiply of two arrays.
af::seq
seq is used to create sequences for indexing af::array
Definition: seq.h:46
util.h
af::info
AFAPI void info()
af::array::as
const array as(dtype type) const
Converts the array into another type.
af::grad
AFAPI void grad(array &dx, array &dy, const array &in)
C++ Interface for calculating the gradients.
af::constant
array constant(T val, const dim4 &dims, const dtype ty=(af_dtype) dtype_traits< T >::ctype)
af::join
AFAPI array join(const int dim, const array &first, const array &second)
Join 2 arrays along dim.
af::moddims
AFAPI array moddims(const array &in, const unsigned ndims, const dim_t *const dims)
af::setDevice
AFAPI void setDevice(const int device)
Sets the current device.
af::timer::start
static AFAPI timer start()
af::array
A multi dimensional data container.
Definition: array.h:35
af
Definition: algorithm.h:15
af::matmulTN
AFAPI array matmulTN(const array &lhs, const array &rhs)
Matrix multiply of two arrays.
af::max
AFAPI array max(const array &in, const int dim=-1)
C++ Interface for maximum values in an array.
af::array::elements
dim_t elements() const
Get the total number of elements across all dimensions of the array.
af::matmulNT
AFAPI array matmulNT(const array &lhs, const array &rhs)
Matrix multiply of two arrays.
af::matmulTT
AFAPI array matmulTT(const array &lhs, const array &rhs)
Matrix multiply of two arrays.
af::sqrt
AFAPI array sqrt(const array &in)
C++ Interface for square root of input.
af::randu
AFAPI array randu(const dim4 &dims, const dtype ty, randomEngine &r)
af::exception
An ArrayFire exception class.
Definition: exception.h:29
af::tile
AFAPI array tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)
af::span
AFAPI seq span
A special value representing the entire axis of an af::array.
af::array::dims
dim4 dims() const
Get dimensions of the array.
af::sigmoid
AFAPI array sigmoid(const array &in)
C++ Interface for calculating sigmoid function of an array.
af::timer::stop
static AFAPI double stop()
af::sum
AFAPI array sum(const array &in, const int dim=-1)
C++ Interface for sum of elements in an array.
arrayfire.h
af::sync
AFAPI void sync(const int device=-1)
Blocks until the device is finished processing.
af::exception::what
virtual const char * what() const
Returns an error message for the exception in a string format.
Definition: exception.h:60
af::array::T
array T() const
Get the transposed the array.
af::min
AFAPI array min(const array &in, const int dim=-1)
C++ Interface for minimum values in an array.
af::transpose
AFAPI array transpose(const array &in, const bool conjugate=false)
Transposes a matrix.
f32
@ f32
32-bit floating point values
Definition: defines.h:211