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| library(keras) | |
| library(densenet) | |
| input_img <- layer_input(shape = c(28, 28, 1)) | |
| model <- application_densenet(input_tensor = input_img, classes = 10L) | |
| model %>% compile( | |
| optimizer = "adam", | |
| loss = "categorical_crossentropy", | |
| metrics = "accuracy" |
| Rcpp::sourceCpp('t.cpp') | |
| x <- runif(1000000) | |
| m <- microbenchmark::microbenchmark( | |
| psort(x), | |
| csort(x), | |
| sort(x) | |
| ) | |
| x <- 1000000:1 |
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