https://github.com/ksachdeva/tfjs-data-mnist
API for MNIST dataset built using tfjs-data
https://github.com/ksachdeva/tfjs-data-mnist
dataset mnist tensorflow tfjs tfjs-data
Last synced: 10 months ago
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API for MNIST dataset built using tfjs-data
- Host: GitHub
- URL: https://github.com/ksachdeva/tfjs-data-mnist
- Owner: ksachdeva
- License: apache-2.0
- Created: 2018-12-02T06:02:37.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-10T23:52:32.000Z (about 7 years ago)
- Last Synced: 2025-04-19T07:59:35.577Z (10 months ago)
- Topics: dataset, mnist, tensorflow, tfjs, tfjs-data
- Language: TypeScript
- Size: 228 KB
- Stars: 3
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Dataset API (tfjs-data) for MNIST
This package provides the Dataset API for MNIST dataset. It is built using [@tensorflow/tfjs-data](https://github.com/tensorflow/tfjs-data) package (which is now
included in [@tensorflow/tfjs](https://github.com/tensorflow/tfjs) union package) that provides a uniform and consistent way to access various datasets.
## Installation
npm install tfjs-data-mnist
## Usage
```ts
// get the dataset
const ds = await MNISTDataset.create();
// there are 2 properties in ds (testDataset and trainDataset)
// get the iterator for testDataset
const it = await ds.testDataset.iterator();
// iterate by invoking next
const dataElement = await it.next();
// dataElement.done === true => there are no more elements
// dataElement.value is **TensorContainer** of type [feature, label]
// where feature and label are of type Tensor1D
//
// feature is Tensor1D with shape [784]
// label is Tensor1D with shape [10]
//
//
// label is actually a one-hot encoded vector
// how to get the feature and label
const feature = dataElement.value[0] as tfjs.Tensor;
const label = dataElement.value[1] as tfjs.Tensor;
// The nice thing about dataset API is that you get
// lot of operations such as suffle, repeat, take etc
// for free
// Here is an example to first shuffle the dataset
// and then take only first 5 samples
const shuffled5 = await ds.testDataset.shuffle(10).take(5).iterator();
// You can also pass dataset to train the model
await model.fitDataset(ds.trainDataset.batch(32), {
epochs: 1,
callbacks: {
onBatchEnd: async (batch: number, logs?: tf.Logs) => {
batchProgressEl.innerText =
`${batch} - ${logs['loss']} - ${logs['acc']}`;
},
onEpochEnd: async (epoch: number, logs?: tf.Logs) => {
epochEndResultEl.innerText =
`${epoch} - ${logs['loss']} - ${logs['acc']}`;
}
}
});
```
## Examples
### Running the samples
```bash
# do npm install at the root of this directory
npm install
# install peer dependnencies
npm install @tensorflow/tfjs-core @tensorflow/tfjs-data --no-save
# change directory into example
cd examples
# do npm install in example
npm install
# Run a basic example that shows
# how to use the api of Dataset
npm run basic
# Another example is to train a model
# where I use fitDataset api that takes Dataset
# as an input
npm run train
```