- c -

• calculateGradient() : mic::neural_nets::loss::CrossEntropyLoss< dtype > , mic::neural_nets::loss::LogLikelihoodLoss< dtype > , mic::neural_nets::loss::Loss< dtype > , mic::neural_nets::loss::SquaredErrorLoss< dtype > , mic::neural_nets::optimization::artificial_landscapes::Beale2DFunction< eT > , mic::neural_nets::optimization::artificial_landscapes::DifferentiableFunction< eT > , mic::neural_nets::optimization::artificial_landscapes::Rosenbrock2DFunction< eT > , mic::neural_nets::optimization::artificial_landscapes::SphereFunction< eT >
• calculateLoss() : mic::neural_nets::loss::CrossEntropyLoss< dtype > , mic::neural_nets::loss::LogLikelihoodLoss< dtype > , mic::neural_nets::loss::Loss< dtype > , mic::neural_nets::loss::SquaredErrorLoss< dtype >
• calculateMeanLoss() : mic::mlnn::BackpropagationNeuralNetwork< eT > , mic::neural_nets::loss::Loss< dtype >
• calculateMeanReconstructionError() : mic::mlnn::fully_connected::Linear< eT >
• calculateNumericalGradient() : mic::mlnn::Layer< eT >
• calculateUpdate() : mic::neural_nets::learning::BinaryCorrelatorLearningRule< eT > , mic::neural_nets::learning::HebbianRule< eT > , mic::neural_nets::learning::NormalizedHebbianRule< eT > , mic::neural_nets::learning::NormalizedZerosumHebbianRule< eT > , mic::neural_nets::optimization::AdaDelta< eT > , mic::neural_nets::optimization::AdaGrad< eT > , mic::neural_nets::optimization::AdaGradPID< eT > , mic::neural_nets::optimization::Adam< eT > , mic::neural_nets::optimization::AdamID< eT > , mic::neural_nets::optimization::GradientDescent< eT > , mic::neural_nets::optimization::GradPID< eT > , mic::neural_nets::optimization::Momentum< eT > , mic::neural_nets::optimization::OptimizationFunction< eT > , mic::neural_nets::optimization::RMSProp< eT >
• calculateValue() : mic::neural_nets::optimization::artificial_landscapes::Beale2DFunction< eT > , mic::neural_nets::optimization::artificial_landscapes::DifferentiableFunction< eT > , mic::neural_nets::optimization::artificial_landscapes::Rosenbrock2DFunction< eT > , mic::neural_nets::optimization::artificial_landscapes::SphereFunction< eT >
• clear() : mic::neural_nets::optimization::OptimizationArray< T >
• collectTestStatistics() : mic::applications::MNISTPatchReconstructionApplication , mic::applications::MNISTPatchSoftmaxApplication
• Conv28x28x1Filter2x28x28s1Double() : mic::neural_nets::unit_tests::Conv28x28x1Filter2x28x28s1Double
• Conv2x2x2Filter2x1x1s1Double() : mic::neural_nets::unit_tests::Conv2x2x2Filter2x1x1s1Double
• Conv3x3x2Filter3x2x2s1Float() : mic::neural_nets::unit_tests::Conv3x3x2Filter3x2x2s1Float
• Conv4x4x1Filter1x2x2s2Float() : mic::neural_nets::unit_tests::Conv4x4x1Filter1x2x2s2Float
• Conv4x4x1Filter3x1x1s3Double() : mic::neural_nets::unit_tests::Conv4x4x1Filter3x1x1s3Double
• Conv5x5x1Filter1x2x2s3Float() : mic::neural_nets::unit_tests::Conv5x5x1Filter1x2x2s3Float
• Conv5x5x1Filter1x3x3s1Float() : mic::neural_nets::unit_tests::Conv5x5x1Filter1x3x3s1Float
• Conv5x6x1Filter1x4x4s1Float() : mic::neural_nets::unit_tests::Conv5x6x1Filter1x4x4s1Float
• Conv7x7x3Filter3x3x3s2Float() : mic::neural_nets::unit_tests::Conv7x7x3Filter3x3x3s2Float
• Conv8x8x1Filter2x4x4s4Double() : mic::neural_nets::unit_tests::Conv8x8x1Filter2x4x4s4Double
• ConvHebbian() : mic::mlnn::experimental::ConvHebbian< eT >
• Convolution() : mic::mlnn::convolution::Convolution< eT >
• cosineSimilarity() : mic::mlnn::convolution::Convolution< eT >
• countCorrectPredictions() : mic::mlnn::MultiLayerNeuralNetwork< eT >
• Cropping() : mic::mlnn::convolution::Cropping< eT >