I am working with the MNIST dataset and performing different classification methods on it, but my runtimes are ridiculous, so I am looking for a way to maybe use an a portion of the training part of the set, but keep the test portion at 10K. I have tried a number of different options but nothing is working.
I need to take a sample either from the entire set, or lower the training x and y from 60000 to maybe 20000.
My current code:
library(keras)
mnist <- dataset_mnist()
train_images <- mnist$train$x
train_labels <- mnist$train$y
test_images <- mnist$test$x
test_labels <- mnist$test$y
I have tried to use the sample()
function and other types of splits to no avail.
In the following example I'm downloading MNIST myself and loading it through reticulate
/ numpy
. Shouldn't make much difference. When you want to get a sample with sample()
, you usually take a sample of indices you'll use for subsetting. To get a balanced sample, you might want to draw a specific number or proportion from each label group:
library(reticulate)
library(dplyr)
# Download MNIST dataset as numpy npz,
# load through reticulate, build something along the lines of keras::dataset_mnist() output
np <- import("numpy")
mnist_npz <- curl::curl_download("https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz", "mnist.npz")
mnist_np <- np$load(mnist_npz)
mnist_lst <- list(
train = list(
x = mnist_np[["x_train"]],
y = mnist_np[["y_train"]]
),
test = list(
x = mnist_np[["x_test"]],
y = mnist_np[["y_test"]]
)
)
train_images <- mnist_lst$train$x
train_labels <- mnist_lst$train$y
test_images <- mnist_lst$test$x
test_labels <- mnist_lst$test$y
# sample row indices,
# 100 per class to keep the dataset balanced
sample_idx <-
train_labels |>
tibble(y = _) |>
tibble::rowid_to_column("idx") |>
slice_sample(n = 100, by = y ) |>
arrange(idx) |>
pull(idx)
# use sample_idx for subsetting
train_images_sample <- train_images[sample_idx,,]
train_labels_sample <- train_labels[sample_idx]
str(train_images_sample)
#> int [1:1000, 1:28, 1:28] 0 0 0 0 0 0 0 0 0 0 ...
str(train_labels_sample)
#> int [1:1000(1d)] 9 7 5 6 8 7 7 5 2 9 ...
# original label distribution
table(train_labels)
#> train_labels
#> 0 1 2 3 4 5 6 7 8 9
#> 5923 6742 5958 6131 5842 5421 5918 6265 5851 5949
# sample distribution
table(train_labels_sample)
#> train_labels_sample
#> 0 1 2 3 4 5 6 7 8 9
#> 100 100 100 100 100 100 100 100 100 100
Created on 2024-03-29 with reprex v2.1.0