#include#include int main() { boost::variance<> v; v(1.0); v(2.0); v(3.0); std::cout << "Variance: " << v.value() << std::endl; boost::z_score<> z(v); std::cout << "Z-score for 2.5: " << z(2.5) << std::endl; return 0; }
#includeint main() { cv::Mat img1 = cv::imread("image1.jpg"); cv::Mat img2 = cv::imread("image2.jpg"); double ssim = cv::quality::qualitySSIM(img1, img2); double psnr = cv::quality::qualityPSNR(img1, img2); return 0; }
#includeThis code creates a tensor for logits and labels, applies the softmax operation to the logits, and calculates the loss, accuracy, and gradients using the cross-entropy score. The TensorFlow Score library is included using the `tensorflow/core/framework/tensor.h` and `tensorflow/core/kernels/softmax_op.h` headers.#include int main() { tensorflow::Tensor logits(tensorflow::DT_FLOAT, {2, 3}); auto logits_t = logits.tensor (); logits_t(0, 0) = 1.0; logits_t(0, 1) = 2.0; logits_t(0, 2) = 3.0; logits_t(1, 0) = 3.0; logits_t(1, 1) = 2.0; logits_t(1, 2) = 1.0; tensorflow::Tensor labels(tensorflow::DT_INT32, {2}); auto labels_t = labels.tensor (); labels_t(0) = 0; labels_t(1) = 1; tensorflow::Tensor pred; tensorflow::softmax(&logits, &pred); double loss, accuracy; tensorflow::Tensor* gradients = nullptr; tensorflow::OpRegistry::Global()->LookUp () .function->Compute(logits.dtype(), tensorflow::OpAttrs(), &logits, &pred, nullptr, &labels, nullptr, &loss, nullptr, &accuracy, gradients); return 0; }