TEST( testlogicaloperators, Convolve_1layerbiased_Or ) {
cout << "Or, convolve" << endl;
LogicalDataCreator ldc;
ldc.applyOrGate();
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
NeuralNet *net = NeuralNet::maker(cl)->planes(2)->imageSize(1)->instance();
net->addLayer( ConvolutionalMaker::instance()->numFilters(2)->filterSize(1)->biased(1) );
net->addLayer( SquareLossMaker::instance() );;
SGD *sgd = SGD::instance( cl, 0.1f, 0 );
for( int epoch = 0; epoch < 20; epoch++ ) {
net->epochMaker(sgd)->batchSize(4)->numExamples(4)->inputData(ldc.data)
->expectedOutputs(ldc.expectedOutput)->run( epoch );
if( epoch % 5 == 0 ) cout << "Loss L " << net->calcLoss(ldc.expectedOutput) << endl;
// AccuracyHelper::printAccuracy( ldc.N, 2, ldc.labels, net->getOutput() );
// net->printWeights();
}
// net->print();
AccuracyHelper::printAccuracy( ldc.N, 2, ldc.labels, net->getOutput() );
float loss = net->calcLoss(ldc.expectedOutput);
cout << "loss, E, " << loss << endl;
EXPECT_GE( 0.4f, loss );
delete sgd;
delete net;
delete cl;
}
TEST( testforward, softmax_byplane ) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
NeuralNet *net = NeuralNet::maker(cl)->imageSize(2)->planes(1)->instance();
net->addLayer( SoftMaxMaker::instance()->perPlane() );
net->setBatchSize( 1 );
int imageSizeSquared = net->getLayer(0)->getOutputSize() * net->getLayer(0)->getOutputSize();
float *input = new float[imageSizeSquared];
input[0] = 0;
input[1] = 1;
input[2] = 3;
input[3] = 2;
net->forward( input );
float const*output = net->getOutput();
float sum = 0;
for( int i = 0; i < imageSizeSquared; i++ ) {
cout << "output[" << i << "]=" << output[i] << endl;
sum += output[i];
EXPECT_LE( 0, output[i] );
EXPECT_GE( 1, output[i] );
}
EXPECT_FLOAT_NEAR( 1.0f, sum );
EXPECT_FLOAT_NEAR( (float)( exp(0.0f)/(exp(0.0f)+exp(1.0f)+exp(3.0f)+exp(2.0f)) ), output[0] );
EXPECT_FLOAT_NEAR( (float)( exp(1.0f)/(exp(0.0f)+exp(1.0f)+exp(3.0f)+exp(2.0f)) ), output[1] );
EXPECT_FLOAT_NEAR( (float)( exp(3.0f)/(exp(0.0f)+exp(1.0f)+exp(3.0f)+exp(2.0f)) ), output[2] );
EXPECT_FLOAT_NEAR( (float)( exp(2.0f)/(exp(0.0f)+exp(1.0f)+exp(3.0f)+exp(2.0f)) ), output[3] );
float *expected = new float[imageSizeSquared];
memset( expected, 0, sizeof(float) * imageSizeSquared );
expected[2] = 1;
float loss = net->calcLoss( expected );
cout << "loss " << loss << endl;
EXPECT_LT( 0, loss );
EXPECT_FLOAT_NEAR( - log(output[2]), loss );
memset( expected, 0, sizeof(float) * imageSizeSquared );
expected[0] = 1;
loss = net->calcLoss( expected );
cout << "loss " << loss << endl;
EXPECT_LT( 0, loss );
EXPECT_FLOAT_NEAR( - log(output[0]), loss );
memset( expected, 0, sizeof(float) * imageSizeSquared );
expected[1] = 1;
loss = net->calcLoss( expected );
cout << "loss " << loss << endl;
EXPECT_LT( 0, loss );
EXPECT_FLOAT_NEAR( - log(output[1]), loss );
memset( expected, 0, sizeof(float) * imageSizeSquared );
expected[3] = 1;
loss = net->calcLoss( expected );
cout << "loss " << loss << endl;
EXPECT_LT( 0, loss );
EXPECT_FLOAT_NEAR( - log(output[3]), loss );
delete[] input;
delete[] expected;
delete net;
delete cl;
}
TEST( testlogicaloperators, DISABLED_Convolve_1layer_And_Nobias ) {
cout << "And" << endl;
LogicalDataCreator ldc;
ldc.applyAndGate();
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
NeuralNet *net = NeuralNet::maker(cl)->planes(2)->imageSize(1)->instance();
net->addLayer( ConvolutionalMaker::instance()->numFilters(2)->filterSize(1)->biased(0) );
SGD *sgd = SGD::instance( cl, 4.0f, 0 );
for( int epoch = 0; epoch < 20; epoch++ ) {
net->epochMaker(sgd)->batchSize(4)->numExamples(4)->inputData(ldc.data)
->expectedOutputs(ldc.expectedOutput)->run(epoch);
cout << "Loss L " << net->calcLoss(ldc.expectedOutput) << endl;
// net->printWeights();
}
// net->print();
int numCorrect = AccuracyHelper::calcNumRight( ldc.N, 2, ldc.labels, net->getOutput() );
cout << "accuracy: " << numCorrect << "/" << ldc.N << endl;
EXPECT_EQ( numCorrect, ldc.N );
delete sgd;
delete net;
delete cl;
}
//layer2 plane0=0 "planes not both -1 and planes not both 1"
// weights = plane0*(-1) + plane1*(-1)
// plane1=1 "planes both -1 or planes both 1"
// weights = plane0*(1) + plane1*(1)
TEST( testlogicaloperators, Convolve_2layers_relu_Xor ) {
cout << "Xor, convolve" << endl;
// LogicalDataCreator ldc(new TanhActivation());
// ldc.applyXorGate();
// int imageSize = 1;
// int inPlanes = 2;
int numExamples = 4;
// int filterSize = 1;
float data[] = { -1, -1,
-1, 1,
1, -1,
1, 1 };
float layer1weights[] = { // going to preset these, to near an optimal solution,
// and at least show the network is stable, and gives the correct
-0.4f,-0.55f, // result...
0.52f, 0.53f,
};
float layer1bias[] = {
0.1f,
-0.1f
};
float layer2weights[] = {
1.1f, 0.9f,
-0.8f, -1.2f
};
float layer2bias[] = {
0.1f,
1.1
};
float expectedOutput[] = {
1, 0,
0, 1,
0, 1,
1, 0
};
int labels[] = {
0,
1,
1,
0
};
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
NeuralNet *net = NeuralNet::maker(cl)->planes(2)->imageSize(1)->instance();
net->addLayer( ConvolutionalMaker::instance()->numFilters(2)->filterSize(1)->biased(1) );
net->addLayer( ActivationMaker::instance()->relu() );
net->addLayer( ConvolutionalMaker::instance()->numFilters(2)->filterSize(1)->biased(1) );
net->addLayer( ActivationMaker::instance()->relu() );
net->addLayer( SquareLossMaker::instance() );;
cout << "hand-setting weights..." << endl;
net->initWeights( 1, layer1weights, layer1bias );
net->initWeights( 3, layer2weights, layer2bias );
// net->printWeights();
// net->setBatchSize(4);
// net->forward( data );
// net->print();
SGD *sgd = SGD::instance( cl, 0.1f, 0 );
for( int epoch = 0; epoch < 200; epoch++ ) {
net->epochMaker(sgd)->batchSize(numExamples)->numExamples(numExamples)->inputData(data)
->expectedOutputs(expectedOutput)->run( epoch );
if( epoch % 5 == 0 ) cout << "Loss L " << net->calcLoss(expectedOutput) << endl;
}
net->print();
AccuracyHelper::printAccuracy( numExamples, 2, labels, net->getOutput() );
float loss = net->calcLoss(expectedOutput);
cout << "loss, E, " << loss << endl;
EXPECT_GE( 0.0000001f, loss );
delete sgd;
delete net;
delete cl;
}
