### Lab: Deep Learning
### Note: this lab is slightly different from that in the August 2021 printing of ISLRII
### It is based on keras 2.6.0, while the original was based on keras 2.4.0
### The predict_classes() has been deprecated

## A Single Layer Network on the Hitters Data

###
library(ISLR2)
Gitters <- na.omit(Hitters)
n <- nrow(Gitters)
set.seed(13)
ntest <- trunc(n / 3)
testid <- sample(1:n, ntest)
###
lfit <- lm(Salary ~ ., data = Gitters[-testid, ])
lpred <- predict(lfit, Gitters[testid, ])
with(Gitters[testid, ], mean(abs(lpred - Salary)))
###
x <- scale(model.matrix(Salary ~ . - 1, data = Gitters))
y <- Gitters$Salary
###
library(glmnet)
cvfit <- cv.glmnet(x[-testid, ], y[-testid],
    type.measure = "mae")
cpred <- predict(cvfit, x[testid, ], s = "lambda.min")
mean(abs(y[testid] - cpred))
###
library(keras)
reticulate::use_condaenv(condaenv = "r-tensorflow")
modnn <- keras_model_sequential() %>%
  layer_dense(units = 50, activation = "relu",
        input_shape = ncol(x)) %>%
   layer_dropout(rate = 0.4) %>%
   layer_dense(units = 1)
###
x <- scale(model.matrix(Salary ~ . - 1, data = Gitters))
###
x <- model.matrix(Salary ~ . - 1, data = Gitters) %>% scale()
###
modnn %>% compile(loss = "mse",
    optimizer = optimizer_rmsprop(),
    metrics = list("mean_absolute_error")
   )
###
history <- modnn %>% fit(
#    x[-testid, ], y[-testid], epochs = 1500, batch_size = 32,
    x[-testid, ], y[-testid], epochs = 15, batch_size = 32,
    validation_data = list(x[testid, ], y[testid])
  )
###
plot(history)
###
npred <- predict(modnn, x[testid, ])
mean(abs(y[testid] - npred))

## A Multilayer Network on the MNIST Digit Data

###
mnist <- dataset_mnist()
x_train <- mnist$train$x
g_train <- mnist$train$y
x_test <- mnist$test$x
g_test <- mnist$test$y
dim(x_train)
dim(x_test)
###
x_train <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_reshape(x_test, c(nrow(x_test), 784))
y_train <- to_categorical(g_train, 10)
y_test <- to_categorical(g_test, 10)
###
x_train <- x_train / 255
x_test <- x_test / 255
###
modelnn <- keras_model_sequential()
modelnn %>%
  layer_dense(units = 256, activation = "relu",
       input_shape = c(784)) %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 128, activation = "relu") %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 10, activation = "softmax")
###
summary(modelnn)
###
modelnn %>% compile(loss = "categorical_crossentropy",
    optimizer = optimizer_rmsprop(), metrics = c("accuracy")
  )
###
system.time(
  history <- modelnn %>%
    fit(x_train, y_train, epochs = 30, batch_size = 128,
        validation_split = 0.2)
)
plot(history, smooth = FALSE)
###
accuracy <- function(pred, truth)
  mean(drop(as.numeric(pred)) == drop(truth))
modelnn %>% predict(x_test) %>% k_argmax() %>% accuracy(g_test)
###
modellr <- keras_model_sequential() %>%
  layer_dense(input_shape = 784, units = 10,
       activation = "softmax")
summary(modellr)
###
modellr %>% compile(loss = "categorical_crossentropy",
     optimizer = optimizer_rmsprop(), metrics = c("accuracy"))
modellr %>% fit(x_train, y_train, epochs = 30,
      batch_size = 128, validation_split = 0.2)
modellr %>% predict(x_test) %>% k_argmax() %>% accuracy(g_test)

## Convolutional Neural Networks

###
cifar100 <- dataset_cifar100()
names(cifar100)
x_train <- cifar100$train$x
g_train <- cifar100$train$y
x_test <- cifar100$test$x
g_test <- cifar100$test$y
dim(x_train)
range(x_train[1,,, 1])
###
x_train <- x_train / 255
x_test <- x_test / 255
y_train <- to_categorical(g_train, 100)
dim(y_train)
###
library(jpeg)
par(mar = c(0, 0, 0, 0), mfrow = c(5, 5))
index <- sample(seq(50000), 25)
for (i in index) plot(as.raster(x_train[i,,, ]))
###
model <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3),
      padding = "same", activation = "relu",
      input_shape = c(32, 32, 3)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3),
      padding = "same", activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3),
      padding = "same", activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 256, kernel_size = c(3, 3),
      padding = "same", activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = 512, activation = "relu") %>%
  layer_dense(units = 100, activation = "softmax")
summary(model)
###
model %>% compile(loss = "categorical_crossentropy",
    optimizer = optimizer_rmsprop(), metrics = c("accuracy"))
history <- model %>% fit(x_train, y_train, epochs = 30,
    batch_size = 128, validation_split = 0.2)
model %>% predict(x_test) %>% k_argmax() %>% accuracy(g_test)

## Using Pretrained CNN Models

###
img_dir <- "book_images"
image_names <- list.files(img_dir)
num_images <- length(image_names)
x <- array(dim = c(num_images, 224, 224, 3))
for (i in 1:num_images) {
  img_path <- paste(img_dir, image_names[i], sep = "/")
  img <- image_load(img_path, target_size = c(224, 224))
  x[i,,, ] <- image_to_array(img)
}
x <- imagenet_preprocess_input(x)
###
model <- application_resnet50(weights = "imagenet")
summary(model)
###
pred6 <- model %>% predict(x) %>%
  imagenet_decode_predictions(top = 3)
names(pred6) <- image_names
print(pred6)

## IMDb Document Classification

###
max_features <- 10000
imdb <- dataset_imdb(num_words = max_features)
c(c(x_train, y_train), c(x_test, y_test)) %<-% imdb
###
x_train[[1]][1:12]
###
word_index <- dataset_imdb_word_index()
decode_review <- function(text, word_index) {
  word <- names(word_index)
  idx <- unlist(word_index, use.names = FALSE)
  word <- c("<PAD>", "<START>", "<UNK>", "<UNUSED>", word)
  idx <- c(0:3, idx + 3)
  words <- word[match(text, idx, 2)]
  paste(words, collapse = " ")
}
decode_review(x_train[[1]][1:12], word_index)
###
library(Matrix)
one_hot <- function(sequences, dimension) {
  seqlen <- sapply(sequences, length)
  n <- length(seqlen)
  rowind <- rep(1:n, seqlen)
  colind <- unlist(sequences)
  sparseMatrix(i = rowind, j = colind,
      dims = c(n, dimension))
}
###
x_train_1h <- one_hot(x_train, 10000)
x_test_1h <- one_hot(x_test, 10000)
dim(x_train_1h)
nnzero(x_train_1h) / (25000 * 10000)
###
set.seed(3)
ival <- sample(seq(along = y_train), 2000)
###
library(glmnet)
fitlm <- glmnet(x_train_1h[-ival, ], y_train[-ival],
    family = "binomial", standardize = FALSE)
classlmv <- predict(fitlm, x_train_1h[ival, ]) > 0
acclmv <- apply(classlmv, 2, accuracy,  y_train[ival] > 0)
###
par(mar = c(4, 4, 4, 4), mfrow = c(1, 1))
plot(-log(fitlm$lambda), acclmv)
###
model <- keras_model_sequential() %>%
  layer_dense(units = 16, activation = "relu",
      input_shape = c(10000)) %>%
  layer_dense(units = 16, activation = "relu") %>%
  layer_dense(units = 1, activation = "sigmoid")
model %>% compile(optimizer = "rmsprop",
    loss = "binary_crossentropy", metrics = c("accuracy"))
history <- model %>% fit(x_train_1h[-ival, ], y_train[-ival],
    epochs = 20, batch_size = 512,
    validation_data = list(x_train_1h[ival, ], y_train[ival]))
###
history <- model %>% fit(
    x_train_1h[-ival, ], y_train[-ival], epochs = 20,
    batch_size = 512, validation_data = list(x_test_1h, y_test)
  )

## Recurrent Neural Networks


### Sequential Models for Document Classification

###
wc <- sapply(x_train, length)
median(wc)
sum(wc <= 500) / length(wc)
###
maxlen <- 500
x_train <- pad_sequences(x_train, maxlen = maxlen)
x_test <- pad_sequences(x_test, maxlen = maxlen)
dim(x_train)
dim(x_test)
x_train[1, 490:500]
###
model <- keras_model_sequential() %>%
  layer_embedding(input_dim = 10000, output_dim = 32) %>%
  layer_lstm(units = 32) %>%
  layer_dense(units = 1, activation = "sigmoid")
###
model %>% compile(optimizer = "rmsprop",
    loss = "binary_crossentropy", metrics = c("acc"))
history <- model %>% fit(x_train, y_train, epochs = 10,
    batch_size = 128, validation_data = list(x_test, y_test))
plot(history)
predy <- predict(model, x_test) > 0.5
mean(abs(y_test == as.numeric(predy)))

### Time Series Prediction

###
library(ISLR2)
xdata <- data.matrix(
    NYSE[, c("DJ_return", "log_volume","log_volatility")]
  )
istrain <- NYSE[, "train"]
xdata <- scale(xdata)
###
lagm <- function(x, k = 1) {
  n <- nrow(x)
  pad <- matrix(NA, k, ncol(x))
  rbind(pad, x[1:(n - k), ])
}
###
arframe <- data.frame(log_volume = xdata[, "log_volume"],
   L1 = lagm(xdata, 1), L2 = lagm(xdata, 2),
   L3 = lagm(xdata, 3), L4 = lagm(xdata, 4),
   L5 = lagm(xdata, 5)
 )
###
arframe <- arframe[-(1:5), ]
istrain <- istrain[-(1:5)]
###
arfit <- lm(log_volume ~ ., data = arframe[istrain, ])
arpred <- predict(arfit, arframe[!istrain, ])
V0 <- var(arframe[!istrain, "log_volume"])
1 - mean((arpred - arframe[!istrain, "log_volume"])^2) / V0
###
arframed <- data.frame(day = NYSE[-(1:5), "day_of_week"], arframe)
arfitd <- lm(log_volume ~ ., data = arframed[istrain, ])
arpredd <- predict(arfitd, arframed[!istrain, ])
1 - mean((arpredd - arframe[!istrain, "log_volume"])^2) / V0
###
n <- nrow(arframe)
xrnn <- data.matrix(arframe[, -1])
xrnn <- array(xrnn, c(n, 3, 5))
xrnn <- xrnn[,, 5:1]
xrnn <- aperm(xrnn, c(1, 3, 2))
dim(xrnn)
###
model <- keras_model_sequential() %>%
  layer_simple_rnn(units = 12,
      input_shape = list(5, 3),
      dropout = 0.1, recurrent_dropout = 0.1) %>%
  layer_dense(units = 1)
model %>% compile(optimizer = optimizer_rmsprop(),
    loss = "mse")
###
history <- model %>% fit(
    xrnn[istrain,, ], arframe[istrain, "log_volume"],
    batch_size = 64, epochs = 200,
    validation_data =
      list(xrnn[!istrain,, ], arframe[!istrain, "log_volume"])
  )
kpred <- predict(model, xrnn[!istrain,, ])
1 - mean((kpred - arframe[!istrain, "log_volume"])^2) / V0
###
model <- keras_model_sequential() %>%
  layer_flatten(input_shape = c(5, 3)) %>%
  layer_dense(units = 1)
###
x <- model.matrix(log_volume ~ . - 1, data = arframed)
colnames(x)
###
arnnd <- keras_model_sequential() %>%
  layer_dense(units = 32, activation = 'relu',
      input_shape = ncol(x)) %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = 1)
arnnd %>% compile(loss = "mse",
    optimizer = optimizer_rmsprop())
history <- arnnd %>% fit(
    x[istrain, ], arframe[istrain, "log_volume"], epochs = 100,
    batch_size = 32, validation_data =
      list(x[!istrain, ], arframe[!istrain, "log_volume"])
  )
plot(history)
npred <- predict(arnnd, x[!istrain, ])
1 - mean((arframe[!istrain, "log_volume"] - npred)^2) / V0
###