jjallaire

env/
__pycache__/
*-app.py
*_files/
*.html

jjallaire

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## R Markdown

This is an R Markdown presentation. Markdown is a simple formatting syntax for authoring HTML, PDF,

jjallaire

library(shiny)

# Define server logic required to generate and plot a random distribution
shinyServer(function(input, output) {
   
  # Expression that generates a plot of the distribution. The expression
  # is wrapped in a call to renderPlot to indicate that:
  #
  #  1) It is "reactive" and therefore should be automatically 
  #     re-executed when inputs change

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'''Train MNIST with tfrecords yielded from a TF Dataset

In order to run this example you should first run 'mnist_to_tfrecord.py'
which will download MNIST data and serialize it into 3 tfrecords files
(train.tfrecords, validation.tfrecords, and test.tfrecords).

This example demonstrates the use of TF Datasets wrapped by a generator
function. The example currently only works with a fork of keras that accepts
`workers=0` as an argument to fit_generator, etc. Passing `workers=0` results
in the generator function being run on the main thread (without this various

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library(shiny)

# Define server logic for random distribution application
shinyServer(function(input, output) {
  
  # Reactive expression to generate the requested distribution. This is 
  # called whenever the inputs change. The output functions defined 
  # below then all use the value computed from this expression
  data <- reactive({
    dist <- switch(input$dist,

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#include <Rcpp.h>
using namespace Rcpp;



 
class BinFixed {
    double width_;
    double origin_;
  public:

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library(keras)
library(reticulate)

layer_multiplicative_lstm <-function(
  object, units, activation = "tanh", recurrent_activation = "hard_sigmoid", use_bias = TRUE, 
  return_sequences = FALSE, return_state = FALSE, go_backwards = FALSE, stateful = FALSE, unroll = FALSE,
  kernel_initializer = "glorot_uniform", recurrent_initializer = "orthogonal", bias_initializer = "zeros", 
  unit_forget_bias = TRUE, kernel_regularizer = NULL, recurrent_regularizer = NULL, bias_regularizer = NULL, 

jjallaire

from keras.models import Model
from keras import layers
from keras import Input
text_vocabulary_size = 10000
question_vocabulary_size = 10000
answer_vocabulary_size = 500


text_input = Input(shape=(None,), dtype='int32', name='text')
embedded_text = layers.Embedding(

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#include <Rcpp.h>
using namespace Rcpp;

void finalizeInt(int* ptr) {
    // do nothing
}

typedef XPtr<int,PreserveStorage,finalizeInt> XPtrInt;

// [[Rcpp::export]]

jjallaire

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title: "Untitled"
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## R Markdown

This is an R Markdown presentation. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.

When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.