Lab: Deep Learning
In this section, we show how to fit the examples discussed in the text. We use the luz package, which interfaces to the torch package which in turn links to efficient C++ code in the LibTorch library.
This version of the lab was produced by Daniel Falbel and Sigrid Keydana, both data scientists at Rstudio where these packages were produced.
An advantage over our original keras implementation is that this version does not require a separate python installation.
Single Layer Network on Hitters Data
We start by fitting the models in Section 10.6. We set up the data, and separate out a training and test set.
library(ISLR2)
Gitters <- na.omit(Hitters)
n <- nrow(Gitters)
set.seed(13)
ntest <- trunc(n / 3)
testid <- sample(1:n, ntest)The linear model should be familiar, but we present it anyway.
lfit <- lm(Salary ~ ., data = Gitters[-testid, ])
lpred <- predict(lfit, Gitters[testid, ])
with(Gitters[testid, ], mean(abs(lpred - Salary)))## [1] 254.6687Notice the use of the with() command: the first argument is a dataframe, and the second an expression that can refer to elements of the dataframe by name. In this instance the dataframe corresponds to the test data and the expression computes the mean absolute prediction error on this data.
Next we fit the lasso using glmnet. Since this package does not use formulas, we create x and y first.
x <- scale(model.matrix(Salary ~ . - 1, data = Gitters))
y <- Gitters$SalaryThe first line makes a call to model.matrix(), which produces the same matrix that was used by lm() (the -1 omits the intercept). This function automatically converts factors to dummy variables. The scale() function standardizes the matrix so each column has mean zero and variance one.
library(glmnet)## Loading required package: Matrix## Loaded glmnet 4.1-2cvfit <- cv.glmnet(x[-testid, ], y[-testid],
type.measure = "mae")
cpred <- predict(cvfit, x[testid, ], s = "lambda.min")
mean(abs(y[testid] - cpred))## [1] 252.2994To fit the neural network, we first set up a model structure that describes the network.
library(torch)
library(luz) # high-level interface for torch
library(torchvision) # for datasets and image transformation## Warning: package 'torchvision' was built under R version 4.1.2library(torchdatasets) # for datasets we are going to use
library(zeallot)
torch_manual_seed(13)modnn <- nn_module(
initialize = function(input_size) {
self$hidden <- nn_linear(input_size, 50)
self$activation <- nn_relu()
self$dropout <- nn_dropout(0.4)
self$output <- nn_linear(50, 1)
},
forward = function(x) {
x %>%
self$hidden() %>%
self$activation() %>%
self$dropout() %>%
self$output()
}
)We have created a model called modnn by defining the initialize() and forward() functions and passing them to the nn_module() function. The initialize() function is responsible for initializing the submodules that are used by the model. In the forward method we implement what happens when the model is called on input data. In this case we use the layers we defined in initialize() in that specific order.
self is a list-like special object that is used to share information between the methods of the nn_module(). When you assign an object to self in initialize(), it can then be accessed by forward().
The pipe operator %>% passes the previous term as the first argument to the next function, and returns the result.
We illustrate the use of the pipe operator on a simple example. Earlier, we created x using the command
x <- scale(model.matrix(Salary ~ . - 1, data = Gitters))We first make a matrix, and then we center and scale each of the variables. Compound expressions like this can be difficult to parse. We could have obtained the same result using the pipe operator:
x <- model.matrix(Salary ~ . - 1, data = Gitters) %>% scale()Using the pipe operator makes it easier to follow the sequence of operations.
We now return to our neural network. The object modnn has a single hidden layer with 50 hidden units, and a ReLU activation function. It then has a dropout layer, in which a random 40% of the 50 activations from the previous layer are set to zero during each iteration of the stochastic gradient descent algorithm. Finally, the output layer has just one unit with no activation function, indicating that the model provides a single quantitative output.
Next we add details to modnn that control the fitting algorithm. We minimize squared-error loss as in (10.22). The algorithm tracks the mean absolute error on the training data, and on validation data if it is supplied.
modnn <- modnn %>%
setup(
loss = nn_mse_loss(),
optimizer = optim_rmsprop,
metrics = list(luz_metric_mae())
) %>%
set_hparams(input_size = ncol(x))In the previous line, the pipe operator passes modnn as the first argument to setup(). The setup() function embeds these specification into a new model object. We also use set_hparam() to specify the arguments that should be passed to the initialize() method of modnn.
Now we fit the model. We supply the training data and the number of epochs. By default, at each step of SGD, the algorithm randomly selects 32 training observations for the computation of the gradient. Recall from Sections 10.4 and 10.7 that an epoch amounts to the number of SGD steps required to process \(n\) observations. Since the training set has \(n=176\), an epoch is \(176/32=5.5\) SGD steps. The fit() function has an argument valid_data; these data are not used in the fitting, but can be used to track the progress of the model (in this case reporting mean absolute error). Here we actually supply the test data so we can see mean absolute error of both the training data and test data as the epochs proceed. To see more options for fitting, use ?fit.luz_module_generator.
fitted <- modnn %>%
fit(
data = list(x[-testid, ], matrix(y[-testid], ncol = 1)),
valid_data = list(x[testid, ], matrix(y[testid], ncol = 1)),
epochs = 20
)(Here and elsewhere we have reduced the number of epochs to make runtimes manageable; users can of course change back)
We can plot the fitted model to display the mean absolute error for the training and test data.
plot(fitted)
Finally, we predict from the final model, and evaluate its performance on the test data. Due to the use of SGD, the results vary slightly with each fit.
npred <- predict(fitted, x[testid, ])
mean(abs(y[testid] - npred))## torch_tensor
## 441.292
## [ CPUFloatType{} ]Multilayer Network on the MNIST Digit Data
The torchvision package comes with a number of example datasets, including the MNIST digit data. Our first step is to load the MNIST data. The mnist_dataset() function is provided for this purpose.
This functions returns a dataset(), a data structure implemented in torch allowing one to represent any dataset without making assumptions on where the data is stored and how the data is organized. Usually, torch datasets also implement the data acquisition process, like downloading and caching some files on disk.
train_ds <- mnist_dataset(root = ".", train = TRUE, download = TRUE)
test_ds <- mnist_dataset(root = ".", train = FALSE, download = TRUE)
str(train_ds[1])## List of 2
## $ x: int [1:28, 1:28] 0 0 0 0 0 0 0 0 0 0 ...
## $ y: int 6str(test_ds[2])## List of 2
## $ x: int [1:28, 1:28] 0 0 0 0 0 0 0 0 0 0 ...
## $ y: int 3length(train_ds)## [1] 60000length(test_ds)## [1] 10000There are 60,000 images in the training data and 10,000 in the test data. The images are \(28\times 28\), and stored as matrix of pixels. We need to transform each one into a vector.
Neural networks are somewhat sensitive to the scale of the inputs. For example, ridge and lasso regularization are affected by scaling. Here the inputs are eight-bit grayscale values between 0 and 255, so we rescale to the unit interval. (Note: eight bits means \(2^8\), which equals 256. Since the convention is to start at \(0\), the possible values range from \(0\) to \(255\).)
To apply these transformations we will re-define train_ds and test_ds, now passing a the transform argument that will apply a transformation to each of the image inputs.
transform <- function(x) {
x %>%
torch_tensor() %>%
torch_flatten() %>%
torch_div(255)
}
train_ds <- mnist_dataset(
root = ".",
train = TRUE,
download = TRUE,
transform = transform
)
test_ds <- mnist_dataset(
root = ".",
train = FALSE,
download = TRUE,
transform = transform
)Now we are ready to fit our neural network.
modelnn <- nn_module(
initialize = function() {
self$linear1 <- nn_linear(in_features = 28*28, out_features = 256)
self$linear2 <- nn_linear(in_features = 256, out_features = 128)
self$linear3 <- nn_linear(in_features = 128, out_features = 10)
self$drop1 <- nn_dropout(p = 0.4)
self$drop2 <- nn_dropout(p = 0.3)
self$activation <- nn_relu()
},
forward = function(x) {
x %>%
self$linear1() %>%
self$activation() %>%
self$drop1() %>%
self$linear2() %>%
self$activation() %>%
self$drop2() %>%
self$linear3()
}
)We define the intialize() and forward() methods of the nn_module().
In initialize we specify all layers that are used in the model. For example, nn_linear(784, 256) defines a dense layer that goes from \(28\times28=784\) input units to a hidden layer of \(256\) units. The model will have 3 of them, each one decreasing the number of output units. The last will have 10 output units, because each unit will be associated to a different class, and we have a 10-class classification problem. We also defined dropout layers using nn_dropout(). These will be used to perform dropout regularization. Finally we define the activation layer using nn_relu().
In forward() we define the order in which these layers are called. We call them in blocks like (linear, activation, dropout), except for the last layer that does not use an activation function or dropout.
Finally, we use print to summarize the model, and to make sure we got it all right.
print(modelnn())## An `nn_module` containing 235,146 parameters.
##
## ── Modules ─────────────────────────────────────────────────────────────────────
## • linear1: <nn_linear> #200,960 parameters
## • linear2: <nn_linear> #32,896 parameters
## • linear3: <nn_linear> #1,290 parameters
## • drop1: <nn_dropout> #0 parameters
## • drop2: <nn_dropout> #0 parameters
## • activation: <nn_relu> #0 parametersThe parameters for each layer include a bias term, which results in a parameter count of 235,146. For example, the first hidden layer involves \((784+1)\times 256=200{,}960\) parameters.
Next, we add details to the model to specify the fitting algorithm. We fit the model by minimizing the cross-entropy function given by (10.13).
Notice that in torch the cross entropy function is defined in terms of the logits, for numerical stability and memory efficiency reasons. It does not require the target to be one-hot encoded.
modelnn <- modelnn %>%
setup(
loss = nn_cross_entropy_loss(),
optimizer = optim_rmsprop,
metrics = list(luz_metric_accuracy())
)Now we are ready to go. The final step is to supply training data, and fit the model.
system.time(
fitted <- modelnn %>%
fit(
data = train_ds,
epochs = 5,
valid_data = 0.2,
dataloader_options = list(batch_size = 256),
verbose = FALSE
)
)## user system elapsed
## 131.098 1.879 130.590plot(fitted)
We have suppressed the output here. The output is a progress report on the fitting of the model, grouped by epoch. This is very useful, since on large datasets fitting can take time. Fitting this model took 215 seconds on a 2.7GHz MacBook Pro with 4 cores and 16 GB of RAM. Here we specified a validation split of 20%, so training is actually performed on 80% of the 60,000 observations in the training set. This is an alternative to actually supplying validation data, like we did in Section 10.9.1. See ?fit.luz_module_generator for all the optional fitting arguments. SGD uses batches of 256 observations in computing the gradient, and doing the arithmetic, we see that an epoch corresponds to 188 gradient steps. The last plot() command produces a figure similar to Figure 10.18.
To obtain the test error in Table 10.1, we first write a simple function accuracy() that compares predicted and true class labels, and then use it to evaluate our predictions.
accuracy <- function(pred, truth) {
mean(pred == truth) }
# gets the true classes from all observations in test_ds.
truth <- sapply(seq_along(test_ds), function(x) test_ds[x][[2]])
fitted %>%
predict(test_ds) %>%
torch_argmax(dim = 2) %>% # the predicted class is the one with higher 'logit'.
as_array() %>% # we convert to an R object
accuracy(truth)## [1] 0.941The table also reports LDA (Chapter 4) and multiclass logistic regression. Although packages such as glmnet can handle multiclass logistic regression, they are quite slow on this large dataset. It is much faster and quite easy to fit such a model using the luz software. We just have an input layer and output layer, and omit the hidden layers!
modellr <- nn_module(
initialize = function() {
self$linear <- nn_linear(784, 10)
},
forward = function(x) {
self$linear(x)
}
)
print(modellr())## An `nn_module` containing 7,850 parameters.
##
## ── Modules ─────────────────────────────────────────────────────────────────────
## • linear: <nn_linear> #7,850 parametersWe fit the model just as before.
fit_modellr <- modellr %>%
setup(
loss = nn_cross_entropy_loss(),
optimizer = optim_rmsprop,
metrics = list(luz_metric_accuracy())
) %>%
fit(
data = train_ds,
epochs = 5,
valid_data = 0.2,
dataloader_options = list(batch_size = 128)
)
fit_modellr %>%
predict(test_ds) %>%
torch_argmax(dim = 2) %>% # the predicted class is the one with higher 'logit'.
as_array() %>% # we convert to an R object
accuracy(truth)## [1] 0.9186# alternatively one can use the `evaluate` function to get the results
# on the test_ds
evaluate(fit_modellr, test_ds)## A `luz_module_evaluation`
## ── Results ─────────────────────────────────────────────────────────────────────
## loss: 0.293
## acc: 0.9186Convolutional Neural Networks
In this section we fit a CNN to the CIFAR data, which is available in the torchvision package. It is arranged in a similar fashion as the MNIST data.
transform <- function(x) {
transform_to_tensor(x)
}
train_ds <- cifar100_dataset(
root = "./",
train = TRUE,
download = TRUE,
transform = transform
)
test_ds <- cifar100_dataset(
root = "./",
train = FALSE,
transform = transform
)
str(train_ds[1])## List of 2
## $ x:Float [1:3, 1:32, 1:32]
## $ y: int 20length(train_ds)## [1] 50000The CIFAR dataset consists of 50,000 training images, each represented by a 3d tensor: each three-color image is represented as a set of three channels, each of which consists of \(32\times 32\) eight-bit pixels. We standardize as we did for the digits, but keep the array structure. This is accomplished with the transform argument.
Before we start, we look at some of the training images; similar code produced Figure 10.5 on page 411.
par(mar = c(0, 0, 0, 0), mfrow = c(5, 5))
index <- sample(seq(50000), 25)
for (i in index) plot(as.raster(as.array(train_ds[i][[1]]$permute(c(2,3,1)))))
The as.raster() function converts the feature map so that it can be plotted as a color image.
Here we specify a moderately-sized CNN for demonstration purposes, similar in structure to Figure 10.8.
conv_block <- nn_module(
initialize = function(in_channels, out_channels) {
self$conv <- nn_conv2d(
in_channels = in_channels,
out_channels = out_channels,
kernel_size = c(3,3),
padding = "same"
)
self$relu <- nn_relu()
self$pool <- nn_max_pool2d(kernel_size = c(2,2))
},
forward = function(x) {
x %>%
self$conv() %>%
self$relu() %>%
self$pool()
}
)
model <- nn_module(
initialize = function() {
self$conv <- nn_sequential(
conv_block(3, 32),
conv_block(32, 64),
conv_block(64, 128),
conv_block(128, 256)
)
self$output <- nn_sequential(
nn_dropout(0.5),
nn_linear(2*2*256, 512),
nn_relu(),
nn_linear(512, 100)
)
},
forward = function(x) {
x %>%
self$conv() %>%
torch_flatten(start_dim = 2) %>%
self$output()
}
)
model()## An `nn_module` containing 964,516 parameters.
##
## ── Modules ─────────────────────────────────────────────────────────────────────
## • conv: <nn_sequential> #388,416 parameters
## • output: <nn_sequential> #576,100 parametersNotice that we used the padding = "same" argument to nn_conv2d(), which ensures that the output channels have the same dimension as the input channels. There are 32 channels in the first hidden layer, in contrast to the three channels in the input layer. We use a \(3\times 3\) convolution filter for each channel in all the layers. Each convolution is followed by a max-pooling layer over \(2\times2\) blocks. By studying the summary, we can see that the channels halve in both dimensions after each of these max-pooling operations. After the last of these we have a layer with 256 channels of dimension \(2\times 2\). These are then flattened to a dense layer of size 1,024: in other words, each of the \(2\times 2\) matrices is turned into a \(4\)-vector, and put side-by-side in one layer. This is followed by a dropout regularization layer, then another dense layer of size 512, and finally, the output layer.
Finally, we specify the fitting algorithm, and fit the model.
fitted <- model %>%
setup(
loss = nn_cross_entropy_loss(),
optimizer = optim_rmsprop,
metrics = list(luz_metric_accuracy())
) %>%
set_opt_hparams(lr = 0.001) %>%
fit(
train_ds,
epochs = 10, #30,
valid_data = 0.2,
dataloader_options = list(batch_size = 128)
)
print(fitted)## A `luz_module_fitted`
## ── Time ────────────────────────────────────────────────────────────────────────
## • Total time: 20m 30.5s
## • Avg time per training batch: 344ms
## • Avg time per validation batch 191ms
##
## ── Results ─────────────────────────────────────────────────────────────────────
## Metrics observed in the last epoch.
##
## ℹ Training:
## loss: 2.4198
## acc: 0.371
## ℹ Validation:
## loss: 2.5427
## acc: 0.352
##
## ── Model ───────────────────────────────────────────────────────────────────────
## An `nn_module` containing 964,516 parameters.
##
## ── Modules ─────────────────────────────────────────────────────────────────────
## • conv: <nn_sequential> #388,416 parameters
## • output: <nn_sequential> #576,100 parametersevaluate(fitted, test_ds)## A `luz_module_evaluation`
## ── Results ─────────────────────────────────────────────────────────────────────
## loss: 2.511
## acc: 0.3583This model takes 10 minutes to run and achieves 36% accuracy on the test data. Although this is not terrible for 100-class data (a random classifier gets 1% accuracy), searching the web we see results around 75%. Typically it takes a lot of architecture carpentry, fiddling with regularization, and time to achieve such results.
Using Pretrained CNN Models
We now show how to use a CNN pretrained on the imagenet database to classify natural images, and demonstrate how we produced Figure 10.10. We copied six jpeg images from a digital photo album into the directory book_images. (These images are available from the data section of www.statlearning.com, the ISLR book website. Download book_images.zip; when clicked it creates the book_images directory.) We first read in the images, and convert them into the array format expected by the torch software to match the specifications in imagenet. Make sure that your working directory in R is set to the folder in which the images are stored.
img_dir <- "book_images"
image_names <- list.files(img_dir)
num_images <- length(image_names)
x <- torch_empty(num_images, 3, 224, 224)
for (i in 1:num_images) {
img_path <- file.path(img_dir, image_names[i])
img <- img_path %>%
base_loader() %>%
transform_to_tensor() %>%
transform_resize(c(224, 224)) %>%
# normalize with imagenet mean and stds.
transform_normalize(
mean = c(0.485, 0.456, 0.406),
std = c(0.229, 0.224, 0.225)
)
x[i,,, ] <- img
}We then load the trained network. The model has 18 layers, with a fair bit of complexity.
model <- torchvision::model_resnet18(pretrained = TRUE)
model$eval() # put the model in evaluation modeFinally, we classify our six images, and return the top three class choices in terms of predicted probability for each.
preds <- model(x)
mapping <- jsonlite::read_json("https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json") %>%
sapply(function(x) x[[2]])
top3 <- torch_topk(preds, dim = 2, k = 3)
top3_prob <- top3[[1]] %>%
nnf_softmax(dim = 2) %>%
torch_unbind() %>%
lapply(as.numeric)
top3_class <- top3[[2]] %>%
torch_unbind() %>%
lapply(function(x) mapping[as.integer(x)])
result <- purrr::map2(top3_prob, top3_class, function(pr, cl) {
names(pr) <- cl
pr
})
names(result) <- image_names
print(result)## $flamingo.jpg
## flamingo spoonbill white_stork
## 0.978212118 0.017045651 0.004742272
##
## $hawk_cropped.jpeg
## kite jay magpie
## 0.6157815 0.2311856 0.1530329
##
## $hawk.jpg
## eel agama common_newt
## 0.5391123 0.2527190 0.2081687
##
## $huey.jpg
## Lhasa Tibetan_terrier Shih-Tzu
## 0.79760426 0.12012992 0.08226581
##
## $kitty.jpg
## Saint_Bernard guinea_pig Bernese_mountain_dog
## 0.3946661 0.3427002 0.2626336
##
## $weaver.jpg
## hummingbird lorikeet bee_eater
## 0.3633291 0.3577290 0.2789420IMDb Document Classification
Now we perform document classification (Section 10.4) on the IMDB dataset, which is available as part of the torchdatasets package. We limit the dictionary size to the 10,000 most frequently-used words and tokens.
max_features <- 10000
imdb_train <- imdb_dataset(
root = ".",
download = TRUE,
num_words = max_features
)
imdb_test <- imdb_dataset(
root = ".",
download = TRUE,
num_words = max_features
)Each element of imdb_train is a vector of numbers between 1 and 10000 (the document), referring to the words found in the dictionary. For example, the first training document is the positive review on page 419. The indices of the first 12 words are given below.
imdb_train[1]$x[1:12]## [1] 2 261 297 14 20 23 4 6253 1307 13 70 65To see the words, we create a function, decode_review(), that provides a simple interface to the dictionary.
word_index <- imdb_train$vocabulary
decode_review <- function(text, word_index) {
word <- names(word_index)
idx <- unlist(word_index, use.names = FALSE)
word <- c("<PAD>", "<START>", "<UNK>", word)
words <- word[text]
paste(words, collapse = " ")
}
decode_review(imdb_train[1]$x[1:12], word_index)## [1] "<START> having watched this movie on the scifi channel i can only"Next we write a function to one-hot encode each document in a list of documents, and return a binary matrix in sparse-matrix format.
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))
}To construct the sparse matrix, one supplies just the entries that are nonzero. In the last line we call the function sparseMatrix() and supply the row indices corresponding to each document and the column indices corresponding to the words in each document, since we omit the values they are taken to be all ones. Words that appear more than once in any given document still get recorded as a one.
# collect all values into a list
train <- seq_along(imdb_train) %>%
lapply(function(i) imdb_train[i]) %>%
purrr::transpose()
test <- seq_along(imdb_test) %>%
lapply(function(i) imdb_test[i]) %>%
purrr::transpose()
# num_words + padding + start + oov token = 10000 + 3
x_train_1h <- one_hot(train$x, 10000 + 3)
x_test_1h <- one_hot(test$x, 10000 + 3)
dim(x_train_1h)## [1] 25000 10003nnzero(x_train_1h) / (25000 * (10000 + 3))## [1] 0.01316756Only 1.3% of the entries are nonzero, so this amounts to considerable savings in memory. We create a validation set of size 2,000, leaving 23,000 for training.
set.seed(3)
ival <- sample(seq(along = train$y), 2000)
itrain <- seq_along(train$y)[-ival]First we fit a lasso logistic regression model using glmnet() on the training data, and evaluate its performance on the validation data. Finally, we plot the accuracy, acclmv, as a function of the shrinkage parameter, \(\lambda\). Similar expressions compute the performance on the test data, and were used to produce the left plot in Figure 10.11.
The code takes advantage of the sparse-matrix format of x_train_1h, and runs in about 5 seconds; in the usual dense format it would take about 5 minutes.
library(glmnet)
y_train <- unlist(train$y)
fitlm <- glmnet(x_train_1h[itrain, ], unlist(y_train[itrain]),
family = "binomial", standardize = FALSE)
classlmv <- predict(fitlm, x_train_1h[ival, ]) > 0
acclmv <- apply(classlmv, 2, accuracy, unlist(y_train[ival]) > 0)We applied the accuracy() function that we wrote in Lab 10.9.2 to every column of the prediction matrix classlmv, and since this is a logical matrix of TRUE/FALSE values, we supply the second argument truth as a logical vector as well.
Before making a plot, we adjust the plotting window.
par(mar = c(4, 4, 4, 4), mfrow = c(1, 1))
plot(-log(fitlm$lambda), acclmv)
Next we fit a fully-connected neural network with two hidden layers, each with 16 units and ReLU activation.
model <- nn_module(
initialize = function(input_size = 10000 + 3) {
self$dense1 <- nn_linear(input_size, 16)
self$relu <- nn_relu()
self$dense2 <- nn_linear(16, 16)
self$output <- nn_linear(16, 1)
},
forward = function(x) {
x %>%
self$dense1() %>%
self$relu() %>%
self$dense2() %>%
self$relu() %>%
self$output() %>%
torch_flatten(start_dim = 1)
}
)
model <- model %>%
setup(
loss = nn_bce_with_logits_loss(),
optimizer = optim_rmsprop,
metrics = list(luz_metric_binary_accuracy_with_logits())
) %>%
set_opt_hparams(lr = 0.001)
fitted <- model %>%
fit(
# we transform the training and validation data into torch tensors
list(
torch_tensor(as.matrix(x_train_1h[itrain,]), dtype = torch_float()),
torch_tensor(unlist(train$y[itrain]))
),
valid_data = list(
torch_tensor(as.matrix(x_train_1h[ival, ]), dtype = torch_float()),
torch_tensor(unlist(train$y[ival]))
),
dataloader_options = list(batch_size = 512),
epochs = 10
)
plot(fitted) 
The fitted object has a get_metrics method that gets both the training and validation accuracy at each epoch. Figure 10.11 includes test accuracy at each epoch as well. To compute the test accuracy, we rerun the entire sequence above, replacing the last line with
fitted <- model %>%
fit(
list(
torch_tensor(as.matrix(x_train_1h[itrain,]), dtype = torch_float()),
torch_tensor(unlist(train$y[itrain]))
),
valid_data = list(
torch_tensor(as.matrix(x_test_1h), dtype = torch_float()),
torch_tensor(unlist(test$y))
),
dataloader_options = list(batch_size = 512),
epochs = 10
)Recurrent Neural Networks
In this lab we fit the models illustrated in Section 10.5.
Sequential Models for Document Classification
Here we fit a simple LSTM RNN for sentiment analysis with the IMDB movie-review data, as discussed in Section 10.5.1. We showed how to input the data in 10.9.5, so we will not repeat that here.
We first calculate the lengths of the documents.
wc <- sapply(seq_along(imdb_train), function(i) length(imdb_train[i]$x))
median(wc)## [1] 178sum(wc <= 500) / length(wc)## [1] 0.916We see that over 91% of the documents have fewer than 500 words. Our RNN requires all the document sequences to have the same length. We hence restrict the document lengths to the last \(L=500\) words, and pad the beginning of the shorter ones with blanks. We will use torchdatasets functionality for this.
maxlen <- 500
num_words <- 10000
imdb_train <- imdb_dataset(root = ".", split = "train", num_words = num_words,
maxlen = maxlen)
imdb_test <- imdb_dataset(root = ".", split = "test", num_words = num_words,
maxlen = maxlen)
vocab <- c(rep(NA, imdb_train$index_from - 1), imdb_train$get_vocabulary())
tail(names(vocab)[imdb_train[1]$x])## [1] "compensate" "you" "the" "rental" ""
## [6] "d"The last expression shows the last few words in the first document. At this stage, each of the 500 words in the document is represented using an integer corresponding to the location of that word in the 10,000-word dictionary. The first layer of the RNN is an embedding layer of size 32, which will be learned during training. This layer one-hot encodes each document as a matrix of dimension \(500 \times 10,000\), and then maps these \(10,000\) dimensions down to \(32\).
model <- nn_module(
initialize = function() {
self$embedding <- nn_embedding(10000 + 3, 32)
self$lstm <- nn_lstm(input_size = 32, hidden_size = 32, batch_first = TRUE)
self$dense <- nn_linear(32, 1)
},
forward = function(x) {
c(output, c(hn, cn)) %<-% (x %>%
self$embedding() %>%
self$lstm())
output[,-1,] %>% # get the last output
self$dense() %>%
torch_flatten(start_dim = 1)
}
)The second layer is an LSTM with 32 units, and the output layer is a single logit for the binary classification task. The rest is now similar to other networks we have fit. We track the test performance as the network is fit, and see that it attains 87% accuracy.
model <- model %>%
setup(
loss = nn_bce_with_logits_loss(),
optimizer = optim_rmsprop,
metrics = list(luz_metric_binary_accuracy_with_logits())
) %>%
set_opt_hparams(lr = 0.001)
fitted <- model %>% fit(
imdb_train,
epochs = 10,
dataloader_options = list(batch_size = 128),
valid_data = imdb_test
)
plot(fitted)
predy <- torch_sigmoid(predict(fitted, imdb_test)) > 0.5
evaluate(fitted, imdb_test, dataloader_options = list(batch_size = 512))## A `luz_module_evaluation`
## ── Results ─────────────────────────────────────────────────────────────────────
## loss: 0.3841
## acc: 0.8529Time Series Prediction
We now show how to fit the models in Section 10.5.2 for time series prediction. We first set up the data, and standardize each of the variables.
library(ISLR2)
xdata <- data.matrix(
NYSE[, c("DJ_return", "log_volume","log_volatility")]
)
istrain <- NYSE[, "train"]
xdata <- scale(xdata)The variable istrain contains a TRUE for each year that is in the training set, and a FALSE for each year in the test set.
We first write functions to create lagged versions of the three time series. We start with a function that takes as input a data matrix and a lag \(L\), and returns a lagged version of the matrix. It simply inserts \(L\) rows of NA at the top, and truncates the bottom.
lagm <- function(x, k = 1) {
n <- nrow(x)
pad <- matrix(NA, k, ncol(x))
rbind(pad, x[1:(n - k), ])
}We now use this function to create a data frame with all the required lags, as well as the response variable.
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)
)If we look at the first five rows of this frame, we will see some missing values in the lagged variables (due to the construction above). We remove these rows, and adjust istrain accordingly.
arframe <- arframe[-(1:5), ]
istrain <- istrain[-(1:5)]We now fit the linear AR model to the training data using lm(), and predict on the test data.
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## [1] 0.413223The last two lines compute the \(R^2\) on the test data, as defined in (3.17).
We refit this model, including the factor variable day_of_week.
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## [1] 0.4598616To fit the RNN, we need to reshape these data, since it expects a sequence of \(L=5\) feature vectors \(X=\{X_\ell\}_1^L\) for each observation, as in (10.20) on page 428. These are lagged versions of the time series going back \(L\) time points.
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)## [1] 6046 5 3We have done this in four steps. The first simply extracts the \(n\times 15\) matrix of lagged versions of the three predictor variables from arframe. The second converts this matrix to a \(n\times 3\times 5\) array. We can do this by simply changing the dimension attribute, since the new array is filled column wise. The third step reverses the order of lagged variables, so that index \(1\) is furthest back in time, and index \(5\) closest. The final step rearranges the coordinates of the array (like a partial transpose) into the format that the RNN module in torch expects.
Now we are ready to proceed with the RNN, which uses 12 hidden units.
model <- nn_module(
initialize = function() {
self$rnn <- nn_rnn(3, 12, batch_first = TRUE)
self$dense <- nn_linear(12, 1)
self$dropout <- nn_dropout(0.2)
},
forward = function(x) {
c(output, ...) %<-% (x %>%
self$rnn())
output[,-1,] %>%
self$dropout() %>%
self$dense() %>%
torch_flatten(start_dim = 1)
}
)
model <- model %>%
setup(
optimizer = optim_rmsprop,
loss = nn_mse_loss()
) %>%
set_opt_hparams(lr = 0.001)The output layer has a single unit for the response.
We fit the model in a similar fashion to previous networks. We supply the fit function with test data as validation data, so that when we monitor its progress and plot the history function we can see the progress on the test data. Of course we should not use this as a basis for early stopping, since then the test performance would be biased.
fitted <- model %>% fit(
list(xrnn[istrain,, ], arframe[istrain, "log_volume"]),
epochs = 75, #epochs = 200,
dataloader_options = list(batch_size = 64),
valid_data =
list(xrnn[!istrain,, ], arframe[!istrain, "log_volume"])
)
kpred <- as.numeric(predict(fitted, xrnn[!istrain,, ]))
1 - mean((kpred - arframe[!istrain, "log_volume"])^2) / V0## [1] 0.4085004This model takes about one minute to train.
We could replace the nn_module() command above with the following command:
model <- nn_module(
initialize = function() {
self$dense <- nn_linear(15, 1)
},
forward = function(x) {
x %>%
torch_flatten(start_dim = 2) %>%
self$dense()
}
)Here, torch_flatten() simply takes the input sequence and turns it into a long vector of predictors. This results in a linear AR model. To fit a nonlinear AR model, we could add in a hidden layer.
However, since we already have the matrix of lagged variables from the AR model that we fit earlier using the lm() command, we can actually fit a nonlinear AR model without needing to perform flattening. We extract the model matrix x from arframed, which includes the day_of_week variable.
x <- model.matrix(log_volume ~ . - 1, data = arframed)
colnames(x)## [1] "dayfri" "daymon" "daythur"
## [4] "daytues" "daywed" "L1.DJ_return"
## [7] "L1.log_volume" "L1.log_volatility" "L2.DJ_return"
## [10] "L2.log_volume" "L2.log_volatility" "L3.DJ_return"
## [13] "L3.log_volume" "L3.log_volatility" "L4.DJ_return"
## [16] "L4.log_volume" "L4.log_volatility" "L5.DJ_return"
## [19] "L5.log_volume" "L5.log_volatility"The -1 in the formula avoids the creation of a column of ones for the intercept. The variable day\_of\_week is a five-level factor (there are five trading days), and the -1 results in five rather than four dummy variables.
The rest of the steps to fit a nonlinear AR model should by now be familiar.
arnnd <- nn_module(
initialize = function() {
self$dense <- nn_linear(15, 32)
self$dropout <- nn_dropout(0.5)
self$activation <- nn_relu()
self$output <- nn_linear(32, 1)
},
forward = function(x) {
x %>%
torch_flatten(start_dim = 2) %>%
self$dense() %>%
self$activation() %>%
self$dropout() %>%
self$output() %>%
torch_flatten(start_dim = 1)
}
)
arnnd <- arnnd %>%
setup(
optimizer = optim_rmsprop,
loss = nn_mse_loss()
) %>%
set_opt_hparams(lr = 0.001)
fitted <- arnnd %>% fit(
list(xrnn[istrain,, ], arframe[istrain, "log_volume"]),
epochs = 30, #epochs = 200,
dataloader_options = list(batch_size = 64),
valid_data =
list(xrnn[!istrain,, ], arframe[!istrain, "log_volume"])
)
plot(fitted)
npred <- as.numeric(predict(fitted, xrnn[!istrain, ,]))
1 - mean((arframe[!istrain, "log_volume"] - npred)^2) / V0## [1] 0.4196072