dfalbel

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dfalbel

This is a Guild AI runs repository. To access runs, install Guild AI and run guild pull gist:dfalbel/denoising-diffusion-runs. For more information about Guild AI Gist based repositories, see Guild AI - Gists.

dfalbel

library(torch)

dat <- dataset(
  "mydataset",
  initialize = function(time, size, len = 100 * 32) {
    self$time <- time
    self$len <- len
    self$size <- size
  },
  .getitem = function(i) {

dfalbel

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")

dfalbel

library(torch)
library(ggplot2)

# we want to find the minimum of this function
# using the gradient descent.

f <- function(x) {
  x^2 - x
}

dfalbel

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,

dfalbel

dataset

Daniel Falbel 4/13/2019

Context

In tf.data in python the api for iterating over the elements of a dataset is the following:

dfalbel

---
title: "Quora Question Pairs"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
runtime: shiny    
---

```{r global, include=FALSE}
library(keras)

dfalbel

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)

dfalbel

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)