(".", buflen + 1, buffer, NULL); std::string srcPath = buffer; delete[] buffer; // get the destination path std::string correctPath(getDrive() + getPath()); buflen = GetFullPathName(correctPath.c_str(), 0, buffer, NULL); buffer = new char[buflen + 1]; buflen = ...
C++ (Cpp) cairo_image_surface_create_for_data - 30 examples found. These are the top rated real world C++ (Cpp) examples of cairo_image_surface_create_for_data extracted from open source projects. You can rate examples to help us improve the quality of e
When combined together, $D$ and $G$ as sort of playing a minmax game (the Generator is trying to fool the Discriminator making it increase the probability for on fake examples, i.e. minimize $\mathbb{E}{z \sim p{z}(z)} [\log (1 - D(G(z)))]$. The Discriminator wants to se...
When combined together, $D$ and $G$ as sort of playing a minmax game (the Generator is trying to fool the Discriminator making it increase the probability for on fake examples, i.e. minimize $\mathbb{E}{z \sim p{z}(z)} [\log (1 - D(G(z)))]$. The Discriminator wants to se...
Old Cygwin versions create fake 32-bit inode numbers from a hash algorithm and thus create non-unique numbers. If mkisofs would cache inodes on old Cygwin versions, it would believe that some files are identical although they are not. The result in this case are files that contain the wrong...
When combined together, D and G as sort of playing a minmax game (the Generator is trying to fool the Discriminator making it increase the probability for on fake examples, i.e. minimize $\mathbb{E}{z \sim p{z}(z)} [\log (1 - D(G(z)))]$. The Discriminator wants to separate...
test dynamo_skips TestProxyTensorOpInfoCPU.test_make_fx_exhaustive_T_cpu_float32 TestProxyTensorOpInfoCPU.test_make_fx_exhaustive_t_cpu_float32 TestProxyTensorOpInfoCPU.test_make_fx_fake_exhaustive_T_cpu_float32 TestProxyTensorOpInfoCPU.test_make_fx_fake_exhaustive_t_cpu_float32 Test...
When combined together, D and G as sort of playing a minmax game (the Generator is trying to fool the Discriminator making it increase the probability for on fake examples, i.e. minimize $\mathbb{E}{z \sim p{z}(z)} [\log (1 - D(G(z)))]$. The Discriminator wants to separate...
When combined together, D and G as sort of playing a minmax game (the Generator is trying to fool the Discriminator making it increase the probability for on fake examples, i.e. minimize $\mathbb{E}{z \sim p{z}(z)} [\log (1 - D(G(z)))]$. The Discriminator wants to separate...
When combined together, D and G as sort of playing a minmax game (the Generator is trying to fool the Discriminator making it increase the probability for on fake examples, i.e. minimize $\mathbb{E}{z \sim p{z}(z)} [\log (1 - D(G(z)))]$. The Discriminator wants to separate...