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Showing content from http://www.arrayfire.org/docs/machine_learning_2softmax_regression_8cpp-example.htm below:

ArrayFire: machine_learning/softmax_regression.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.

();

}

abserr(

&predicted,

&target) {

100 * sum<float>(abs(predicted - target)) / predicted.

();

}

}

&Y,

lambda = 1.0) {

lambdat(0, span) = 0;

H = predict(X, Weights);

Jreg = 0.5 *

(lambdat * Weights * Weights);

J = (Jerr + Jreg) / m;

dJ = (

(X, D) + lambdat * Weights) / m;

}

double lambda = 1.0, double maxerr = 0.01, int maxiter = 1000,

bool verbose = false) {

float err = 0;

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

cost(J, dJ, Weights, X, Y, lambda);

err = max<float>(

(J));

if (err < maxerr) {

printf("Iteration %4d Err: %.4f\n", i + 1, err);

printf("Training converged\n");

return Weights;

}

if (verbose && ((i + 1) % 10 == 0)) {

printf("Iteration %4d Err: %.4f\n", i + 1, err);

}

Weights = Weights - alpha * dJ;

}

printf("Training stopped after %d iterations\n", maxiter);

return Weights;

}

benchmark_softmax_regression(

&train_feats,

&train_targets,

test_feats) {

timer::start();

Weights = train(train_feats, train_targets, 0.1, 1.0, 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 logit_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);

train(train_feats, train_targets,

0.1,

1.0,

0.01,

1000,

true);

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));

printf("Maximum error on testing data: %2.2f\n",

abserr(test_outputs, test_targets));

benchmark_softmax_regression(train_feats, train_targets, test_feats);

if (!console) {

test_outputs = test_outputs.

();

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 logit_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 exp(const array &in)

C++ Interface to evaluate the exponential.

AFAPI array log(const array &in)

C++ Interface to evaluate the natural logarithm.

AFAPI array matmulTN(const array &lhs, const array &rhs)

C++ Interface to multiply two matrices.

AFAPI 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 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.

AFAPI array sum(const array &in, const int dim=-1)

C++ Interface to sum array elements over a given dimension.

AFAPI array batchFunc(const array &lhs, const array &rhs, batchFunc_t func)

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