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Data USPS Digits.Rmd
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Data USPS Digits.Rmd
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---
title: "ML-AI"
author: "Eveline Surbakti"
date: "March 28, 2020"
output:
html_document:
df_print: paged
---
```{r, include=FALSE}
# load packages and data
load("data_usps_digits.RData")
library(keras)
```
```{r}
plot_digit <- function(index, data) {
tmp = (-data + 1) / 2
z = matrix( data = as.numeric(data[index, 256:1]), 16, 16 )
image(z[16:1,1:16], col = gray((1:100)/100),
xaxt = "n", yaxt = "n")}
```
```{r}
# plot few example digits
par(mfrow=c(1,3),mar=rep(1.5,4))
plot_digit(111,x_train)
plot_digit(222,x_train)
plot_digit(333,x_train)
```
```{r}
#checking the data
range(x_train)
```
```{r}
#checking the data
range(x_test)
```
```{r}
# normalize
range_norm <- function(x, a = 0, b = 1) {
( (x - min(x)) / (max(x) - min(x)) )*(b - a) + a }
tensorflow::tf$random$set_seed(1)
x_train= apply(x_train, 2, range_norm)
x_test=apply(x_test,2,range_norm)
range(x_train)
```
```{r}
range(x_test)
```
```{r}
tensorflow::tf$random$set_seed(1)
y_train<-to_categorical(y_train,num_classes = NULL)
y_test<-to_categorical(y_test, num_classes = NULL)
```
```{r}
V <- ncol(x_train)
model <- keras_model_sequential() %>%
layer_dense(units = 256, activation = "relu", input_shape = V) %>%
layer_dense(units = 128, activation = "relu") %>%
layer_dense(units= 10,activation = "softmax") %>%
compile(loss = "categorical_crossentropy", metrics = "accuracy",
optimizer = optimizer_sgd())
count_params(model)
fit<-model%>%fit(
x=x_train, y=y_train,
validation_data=list(x_test, y_test),
epochs=100)
```
```{r}
plot(fit)
smooth_line<-function(y){
x=1:length(y)
out=predict(loess(y~x))
return(out)}
```
```{r}
cols<-c("black","dodgerblue3","gray50","deepskyblue2")
tensorflow::tf$random$set_seed(1)
model_reg <- keras_model_sequential() %>%
layer_dense(units = 256, activation = "relu", input_shape = V,
kernel_regularizer = regularizer_l2(l = 0.0005)) %>%
layer_dense(units = 128, activation = "relu",
kernel_regularizer = regularizer_l2(l = 0.0005)) %>%
layer_dense(units = 10, activation = "softmax") %>%
compile(loss = "categorical_crossentropy",
optimizer = optimizer_sgd(),metrics = "accuracy")
count_params(model_reg)
fit_extra <- model_reg %>% fit(x = x_train, y = y_train,validation_data = list(x_test, y_test), epochs = 100)
out1<-1-cbind(fit$metrics$accuracy,fit$metrics$val_accuracy,
fit_extra$metrics$accuracy,fit_extra$metrics$val_accuracy)
```
```{r}
matplot(out1,pch=19,ylab="Error",xlab ="Epochs",col=adjustcolor(cols,0.3),log="y")
matlines(apply(out1,2,smooth_line),lty=1,col=cols,lwd=2)
legend("bottomleft",legend=c("Training","Test","Training_extra","Test_extra"),fill=cols,bty="n")
```
```{r}
apply(out1,2,min)
```
3 LAYERS AND REGULARIZATION
```{r}
plot_digit <- function(index, data) {
tmp = (-data + 1) / 2
z = matrix( data = as.numeric(data[index, 256:1]), 16, 16 )
image(z[16:1,1:16], col = gray((1:100)/100),
xaxt = "n", yaxt = "n")
}
model2 <- keras_model_sequential() %>%
layer_dense(units = 256, activation = "relu", input_shape = V) %>%
layer_dense(units = 128, activation = "relu") %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units= 10,activation = "softmax") %>%
compile(loss = "categorical_crossentropy", metrics = "accuracy",
optimizer = optimizer_sgd())
count_params(model2)
fit2<-model2%>%fit(
x=x_train, y=y_train,
validation_data=list(x_test, y_test),
epochs=100)
```
```{r}
plot(fit2)
smooth_line2<-function(y){
x=1:length(y)
out=predict(loess(y~x))
return(out)
}
```
```{r}
model_reg2 <- keras_model_sequential() %>%
layer_dense(units = 256, activation = "relu", input_shape = V,
kernel_regularizer = regularizer_l2(l = 0.0005)) %>%
layer_dense(units = 128, activation = "relu",
kernel_regularizer = regularizer_l2(l = 0.0005))%>%
layer_dense(units = 64, activation = "relu",
kernel_regularizer = regularizer_l2(l = 0.0005)) %>%
layer_dense(units = 10, activation = "softmax") %>%
compile(loss = "categorical_crossentropy",
optimizer = optimizer_sgd(),metrics = "accuracy")
count_params(model_reg2)
fit_extra2 <- model_reg2 %>% fit(x = x_train, y = y_train,validation_data = list(x_test, y_test), epochs = 100)
out2<-1-cbind(fit2$metrics$accuracy,fit2$metrics$val_accuracy,
fit_extra2$metrics$accuracy,fit_extra2$metrics$val_accuracy)
matplot(out2,pch=19,ylab="Error",xlab = "Epochs",col=adjustcolor(cols,0.3),log="y")
matlines(apply(out2,2,smooth_line),lty=1,col=cols,lwd=2)
legend("bottomleft",legend=c("Training","Test","Training_extra","Test_extra"),fill=cols,bty="n")
apply(out2,2,min)
```