Scidata currently supports the following training and test datasets:

• Caltech101
• CIFAR10
• CIFAR100
• FashionMNIST
• IMDb Reviews
• Kuzushiji-MNIST (KMNIST)
• MNIST
• SQuAD
• Yelp Reviews (Full and Polarity)
• Iris
• Wine

Download or fetch datasets locally:

{train_images, train_labels} = Scidata.MNIST.download()
{test_images, test_labels} = Scidata.MNIST.download_test()

# Unpack train_images like...
{images_binary, tensor_type, shape} = train_images

Most often you will convert those results to Nx tensors:

{train_images, train_labels} = Scidata.MNIST.download()

# Normalize and batch images
{images_binary, images_type, images_shape} = train_images

batched_images =
  images_binary
  |> Nx.from_binary(images_type)
  |> Nx.reshape(images_shape)
  |> Nx.divide(255)
  |> Nx.to_batched(32)

# One-hot-encode and batch labels
{labels_binary, labels_type, _shape} = train_labels

batchd_labels =
  labels_binary
  |> Nx.from_binary(labels_type)
  |> Nx.new_axis(-1)
  |> Nx.equal(Nx.tensor(Enum.to_list(0..9)))
  |> Nx.to_batched(32)

def deps do
  [
    {:scidata, "~> 0.1.11"}
  ]
end

PRs are encouraged! Consider using utils to add your favorite dataset or one from this list.

Copyright (c) 2022 Tom Rutten

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.