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---
dataset_info:
features:
- name: x
sequence: float64
- name: 'y'
dtype: int64
splits:
- name: train
num_bytes: 1328000
num_examples: 4000
- name: test
num_bytes: 332000
num_examples: 1000
download_size: 2009200
dataset_size: 1660000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
pretty_name: The MNIST-1D Dataset
size_categories:
- 1K<n<10K
---
> [!NOTE]
> This dataset card is based on the README file of the authors' GitHub repository: https://github.com/greydanus/mnist1d
>
# The MNIST-1D Dataset
Most machine learning models get around the same ~99% test accuracy on MNIST. Our dataset, MNIST-1D, is 100x smaller (default sample size: 4000+1000; dimensionality: 40) and does a better job of separating between models with/without nonlinearity and models with/without spatial inductive biases.
MNIST-1D is a core teaching dataset in Simon Prince's [Understanding Deep Learning](https://udlbook.github.io/udlbook/) textbook.
## Comparing MNIST and MNIST-1D
| Dataset | Logistic Regression | MLP | CNN | GRU* | Human Expert |
|:----------------------|:---------------------|:------|:------|:------|:--------------|
| MNIST | 92% | 99+% | 99+% | 99+% | 99+% |
| MNIST-1D | 32% | 68% | 94% | 91% | 96% |
| MNIST-1D (shuffle**) | 32% | 68% | 56% | 57% | ~30% |
*Training the GRU takes at least 10x the walltime of the CNN.
**The term "shuffle" refers to shuffling the spatial dimension of the dataset, as in [Zhang et al. (2017)](https://arxiv.org/abs/1611.03530).
## Dataset Creation
This version of the dataset was created by using the pickle file provided by the dataset authors in the original repository: [mnist1d_data.pkl](https://github.com/greydanus/mnist1d/blob/master/mnist1d_data.pkl) and was generated like follows:
```python
import sys ; sys.path.append('..') # useful if you're running locally
import mnist1d
from datasets import Dataset, DatasetDict
# Load the data using the mnist1d library
args = mnist1d.get_dataset_args()
data = mnist1d.get_dataset(args, path='./mnist1d_data.pkl', download=True) # This is the default setting
# Load the data into a Hugging Face dataset and push it to the hub
train = Dataset.from_dict({"x": data["x"], "y":data["y"]})
test = Dataset.from_dict({"x": data["x_test"], "y":data["y_test"]})
DatasetDict({"train":train, "test":test}).push_to_hub("christopher/mnist1d")
```
The origina
## Dataset Usage
Using the `datasets` library:
```python
from datasets import load_dataset
train = load_dataset("christopher/mnist1d", split="train")
test = load_dataset("christopher/mnist1d", split="test")
train_test = load_dataset("christopher/mnist1d", split="train+test")
```
Then to get the data as numpy arrays:
```python
train.set_format("numpy")
x = train["x"]
y = train["y"]
```
## Citation
```json
@inproceedings{greydanus2024scaling,
title={Scaling down deep learning with {MNIST}-{1D}},
author={Greydanus, Sam and Kobak, Dmitry},
booktitle={Proceedings of the 41st International Conference on Machine Learning},
year={2024}
}
``` |