machine_learning/perceptron.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;
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();
}
// Predict based on given parameters
array predict(const array &X, const array &Weights) {
return sigmoid(matmul(X, Weights));
}
array train(const array &X, const array &Y, double alpha = 0.1,
double maxerr = 0.05, int maxiter = 1000, bool verbose = false) {
// Initialize parameters to 0
array Weights = constant(0, X.dims(1), Y.dims(1));
for (int i = 0; i < maxiter; i++) {
array P = predict(X, Weights);
array err = Y - P;
float mean_abs_err = mean<float>(abs(err));
if (mean_abs_err < maxerr) break;
if (verbose && (i + 1) % 25 == 0) {
printf("Iter: %d, Err: %.4f\n", i + 1, mean_abs_err);
}
Weights = Weights + alpha * matmulTN(X, err);
}
return Weights;
}
void benchmark_perceptron(const array &train_feats, const array &train_targets,
const array test_feats) {
array Weights = train(train_feats, train_targets, 0.1, 0.01, 1000);
printf("Training time: %4.4lf s\n", timer::stop());
const int iter = 100;
for (int i = 0; i < iter; i++) {
array test_outputs = predict(test_feats, Weights);
test_outputs.eval();
}
printf("Prediction time: %4.4lf s\n", timer::stop() / iter);
}
// Demo of one vs all logistic regression
int perceptron_demo(bool console, int perc) {
array train_images, train_targets;
array test_images, test_targets;
int num_train, num_test, num_classes;
// Load mnist data
float frac = (float)(perc) / 100.0;
setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
test_images, train_targets, test_targets, frac);
// Reshape images into feature vectors
int feature_length = train_images.elements() / num_train;
array train_feats = moddims(train_images, feature_length, num_train).T();
array test_feats = moddims(test_images, feature_length, num_test).T();
train_targets = train_targets.T();
test_targets = test_targets.T();
// Add a bias that is always 1
train_feats = join(1, constant(1, num_train, 1), train_feats);
test_feats = join(1, constant(1, num_test, 1), test_feats);
// Train logistic regression parameters
array Weights = train(train_feats, train_targets, 0.1, 0.01, 1000, true);
// Predict the results
array train_outputs = predict(train_feats, Weights);
array test_outputs = predict(test_feats, Weights);
printf("Accuracy on training data: %2.2f\n",
accuracy(train_outputs, train_targets));
printf("Accuracy on testing data: %2.2f\n",
accuracy(test_outputs, test_targets));
benchmark_perceptron(train_feats, train_targets, test_feats);
if (!console) {
test_outputs = test_outputs.T();
test_targets = test_targets.T();
// Get 20 random test images.
display_results<true>(test_images, test_outputs, test_targets, 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 perceptron_demo(console, perc);
} catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
return 0;
}