virtual void forwardPropagate(TensorBuf *inp, vector<TensorBuf *>& outp)
{
switch(engine)
{
case XSMM:
forwardPropagate(inp, outp, 0);
break;
}
}
float
SentimentTraining::computeDerivatives(int startIndex, int endIndex) {
CudaInterface::fillParamMem(mModel.derivatives_d, 0);
for (int i = startIndex; i < endIndex; i++) {
forwardPropagate(mTrainTrees[i]);
}
for (int i = startIndex; i < endIndex; i++) {
backPropagate(mTrainTrees[i]);
}
return 1.0;
}
static void forwardPropagate_test() {
float hiddenWeights[2][3] = {{-1, 1, -1}, {1, -1, 1}};
float outputWeights[2][3] = {{0.5, 0.25, 0.25}, {0.25, -0.5, 0.75}};
Node inputNodes[2];
Node hiddenNodes[2] = {{hiddenWeights[0]}, {hiddenWeights[1]}};
Node outputNode[2] = {{outputWeights[0]}, {outputWeights[1]}};
Layer inputLayer = {inputNodes, 2};
Layer hiddenLayer = {hiddenNodes, 2};
Layer outputLayer = {outputNode, 2};
inputLayer.nodes[0].output = 0.5; inputLayer.nodes[1].output = -0.5;
forwardPropagate( &inputLayer, &hiddenLayer, &outputLayer );
assert( fabs( 0.0089929 - outputLayer.nodes[0].output ) < 0.001 && "Output is not as expected" );
assert( fabs( 0.0269728 - outputLayer.nodes[1].output ) < 0.001 && "Output is not as expected" );
}
static void backPropagate_test( void ) {
float hiddenWeights[2][3] = {{-1, 1, 0}, {1, -1, 0}};
float outputWeights[2][3] = {{0.5, 0.25, 0}, {0.25, -0.5, 0}};
Node inputNodes[2];
Node hiddenNodes[2] = {{hiddenWeights[0]}, {hiddenWeights[1]}};
Node outputNode[2] = {{outputWeights[0]}, {outputWeights[1]}};
Node desired[2] = {{NULL, -0.5}, {NULL, -0.5}};
Layer inputLayer = {inputNodes, 2};
Layer hiddenLayer = {hiddenNodes, 2};
Layer outputLayer = {outputNode, 2};
Layer desiredLayer = {desired, 2};
inputLayer.nodes[0].output = 0.5; inputLayer.nodes[1].output = -0.5;
forwardPropagate( &inputLayer, &hiddenLayer, &outputLayer );
backPropagate( &hiddenLayer, &outputLayer, &desiredLayer );
assert( fabs( -0.3118693 - outputLayer.nodes[0].error ) < 0.001 && "First output node error is not as expected" );
assert( fabs( 0.0162368 - outputLayer.nodes[1].error ) < 0.001 && "Second output node error is not as expected" );
assert( fabs( -0.1518755 - hiddenLayer.nodes[0].error ) < 0.001 && "First node of hidden layer's output is not as expected" );
assert( fabs( -0.0860857 - hiddenLayer.nodes[1].error ) < 0.001 && "Second node of hidden layer's output is not as expected" );
}
int main( int argc, char *argv[] ) {
int numInputs;
Layer *hiddenLayer, *outputLayer;
TestCase testCase;
int i;
initRand();
if( !processArguments( argc, argv ) ) {
fprintf( stderr, "Usage: main [-r, -t] [node definition file] [input file, training file]\n" );
exit( EXIT_FAILURE );
}
numInputs = buildLayers( &hiddenLayer, &outputLayer );
if( !numInputs ) {
exit( EXIT_FAILURE );
}
getDefaultTestCase( numInputs, outputLayer->numNodes, &testCase );
if( trainingFlag ) {
for( i = 0; i < 1000; ++i ) {
populateNextTestCase( &testCase );
train( &testCase, hiddenLayer, outputLayer );
}
if( !persistWeights( numInputs, hiddenLayer, outputLayer ) ) {
exit( EXIT_FAILURE );
}
} else {
while( populateNextTestCase( &testCase ) == NEW_INPUT ) {
forwardPropagate( testCase.inputs, hiddenLayer, outputLayer );
printTestResults( testCase.inputs, outputLayer, testCase.desiredOutputs );
}
}
exit( EXIT_SUCCESS );
}
