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Showing content from http://www.arrayfire.org/docs/machine_learning_2perceptron_8cpp-example.htm below:

ArrayFire: machine_learning/perceptron.cpp

#include <math.h>

#include <stdio.h>

#include <string>

#include <vector>

#include "mnist_common.h"

accuracy(

&predicted,

&target) {

val, plabels, tlabels;

max(val, tlabels, target, 1);

max(val, plabels, predicted, 1);

100 * count<float>(plabels == tlabels) / tlabels.

();

}

return sigmoid(matmul(X, Weights));

}

double maxerr = 0.05, int maxiter = 1000, bool verbose = false) {

for (int i = 0; i < maxiter; i++) {

P = predict(X, Weights);

mean_abs_err = mean<float>(

(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 *

(X, err);

}

return Weights;

}

benchmark_perceptron(

&train_feats,

&train_targets,

test_feats) {

timer::start();

Weights = train(train_feats, train_targets, 0.1, 0.01, 1000);

printf("Training time: %4.4lf s\n", timer::stop());

timer::start();

const int iter = 100;

for (int i = 0; i < iter; i++) {

test_outputs = predict(test_feats, Weights);

}

printf("Prediction time: %4.4lf s\n", timer::stop() / iter);

}

int perceptron_demo(bool console, int perc) {

train_images, train_targets;

test_images, test_targets;

int num_train, num_test, num_classes;

float frac = (float)(perc) / 100.0;

setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,

test_images, train_targets, test_targets, frac);

feature_length = train_images.

() / num_train;

train_feats =

(train_images, feature_length, num_train).

();

test_feats =

(test_images, feature_length, num_test).

();

train_targets = train_targets.

();

test_targets = test_targets.

();

train_feats =

(1,

(1, num_train, 1), train_feats);

test_feats =

(1,

(1, num_test, 1), test_feats);

Weights = train(train_feats, train_targets, 0.1, 0.01, 1000,

);

train_outputs = predict(train_feats, Weights);

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.

();

test_targets = test_targets.

();

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 {

return perceptron_demo(console, perc);

return 0;

}

A multi dimensional data container.

dim4 dims() const

Get dimensions of the array.

void eval() const

Evaluate any JIT expressions to generate data for the array.

array T() const

Get the transposed the array.

dim_t elements() const

Get the total number of elements across all dimensions of the array.

An ArrayFire exception class.

virtual const char * what() const

Returns an error message for the exception in a string format.

AFAPI array abs(const array &in)

C++ Interface to calculate the absolute value.

AFAPI array matmulTN(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.

AFAPI void setDevice(const int device)

Sets the current device.

AFAPI void sync(const int device=-1)

Blocks until the device is finished processing.

AFAPI array join(const int dim, const array &first, const array &second)

C++ Interface to join 2 arrays along a dimension.

AFAPI array moddims(const array &in, const dim4 &dims)

C++ Interface to modify the dimensions of an input array to a specified shape.

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