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forked from anonymous's block: fresh block
Rcpp::sourceCpp('t.cpp') | |
x <- runif(1000000) | |
m <- microbenchmark::microbenchmark( | |
psort(x), | |
csort(x), | |
sort(x) | |
) | |
x <- 1000000:1 |
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" |
download.file("https://snap.stanford.edu/data/finefoods.txt.gz", "finefoods.txt.gz") | |
library(readr) | |
library(stringr) | |
library(purrr) | |
reviews <- read_lines("finefoods.txt.gz") | |
reviews <- reviews[str_sub(reviews, 1, 12) == "review/text:"] | |
reviews <- str_sub(reviews, start = 14) |
library(readr) | |
library(keras) | |
library(purrr) | |
FLAGS <- flags( | |
flag_integer("vocab_size", 50000), | |
flag_integer("max_len_padding", 20), | |
flag_integer("embedding_size", 256), | |
flag_numeric("regularization", 0.0001), | |
flag_integer("seq_embedding_size", 512) |
--- | |
title: "Quora Question Pairs" | |
output: | |
flexdashboard::flex_dashboard: | |
orientation: columns | |
runtime: shiny | |
--- | |
```{r global, include=FALSE} | |
library(keras) |
todoist_token <- config::get("TODOIST_TOKEN") | |
get_tasks_week <- function(week = 0, offset = 0) { | |
res <- httr::POST( | |
url = "https://api.todoist.com/sync/v8/activity/get", | |
body = list( | |
token = todoist_token, | |
limit = 100, | |
page = week, |
library(torch) | |
library(ggplot2) | |
# we want to find the minimum of this function | |
# using the gradient descent. | |
f <- function(x) { | |
x^2 - x | |
} |
library(keras) | |
library(tensorflow) | |
input <- layer_input(shape = list(365, 10)) | |
representation <- input %>% | |
layer_lstm(units = 32, input_shape = list(365, 10)) %>% | |
layer_dropout(rate = 0.2) | |
output1 <- representation %>% | |
layer_dense(units = 2, name = "out1") |