kerasinR.Rmd

---
title: "KerasinR"
author: "Amit"
date: "7/18/2018"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
install.packages("rmarkdown")
```

## R Markdown
```{r}
# Install packages
install.packages("keras")
library(keras)
#install_keras()
use_condaenv("r-tensorflow")
install.packages("keras")
keras::install_keras()
install.packages("reticulate")
library(reticulate)
conda_install('r-tensorflow','absl-py')
```
```{r}
#importing data from desktop I find this technique to be really helpful for example when you are trying to locate file from your computer no there are many techniques file.choose lets you browse the files from your computer but I recently discovered a new technique which will come handy for a lot of people when start ~ (tilda) is your home directory after tilda press ~/ and tab lets you select folder so you can select folder's and files 

data<-read.csv(file = "~/Desktop/GIT/project/card.csv",header = T)
View(data)
#Description of the dataset https://archive.ics.uci.edu/ml/datasets/cardiotocography#

data<-data[-1,]
#we have 21 independent variables which and the 22nd variable is NSP which is dependent variable 
#Change to matrix
data<-as.matrix(data)
dimnames(data)<-NULL

str(data)
```

```{r}

#Normalize 
data(,1:21)<-normalize(data[,1:21])
data(,1:21)<-normalize(data[,1:21])
data[,22]<-as.numeric(data[,22])-1
summary(data)
```

```{r}
#data partition
set.seed(1234)
ind<-sample(2,nrow(data),replace = T,prob = c(0.7,0.3))
training<-data[ind=1,1:21]
test<-data[ind==2,1:21]
trainingtarget<-data[ind==1,22]
testtarget<-data[ind==2,22]
```

```{r}
dataset_mnist()
#one hot encoding
trainlabels<-to_categorical(trainingtarget)
testlabels<-to_categorical(testtarget)
```
```{r}
# create sequential model
model<-keras_model_sequential()
#pipe takes information from left 
model %>%
   layer_dense(units=8,activation='relu',input_shape=c(21))%>%
   layer_dense(units=3,activation='softmax')
summary(model)   
```

```{r}
#compile
model %>%
   compile(loss='categorical_crossentropy',
           optimizer='adam',
           metrics='accuracy')
```
```{r}
#fit model
history<-model%>%
   fit(training,
       trainlabels,
       epoch=200,
       batch_size=32,
       validation_split=0.2)
```

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