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cpunks10k 0.1.0

CPUNKS-10K are subsets of the 10,000 labeled images in the CryptoPunks collection by Larva Labs. They have been collected, organized & modified for use in Machine Learning research by tnn1t1s.eth. The source images files and meta-data were designed and created by John Watkinson & Matt Hall.
### The CPUNKS-10K dataset
The CPUNKS-10k dataset consists of 10000 24x24 colour images labeled with 92 classes, with a mixed number of images per class. There are 8000 training images and 2000 test images.
The dataset is divided into four training batches and one test batch, each with 2000 images. The test batch contains randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another.
Five classes are mutually exclusive: alien, ape, zombie, male and female. The remaining classes are not mutually exclusive. For example, there is overlap between Choker and Tiara.
### Download & Install
cpunks-10k is available for installation using pip. In a virtual environment or conda environment, use the following command to install cpunks-10k.
`pip install cpunks-10k`
To get started, use the cpunks command line utility to create a scratch space for notebook development using cpunks.
`cpunks tutorial install`
### Using CPUNKS10
The repository contains a tutorial section that will help you get setup using CPUNKS-10K with Tensorflow and Keras. The [Simple Neural Net Classifier](https://github.com/tnn1t1s/cpunks-10k/blob/main/tutorial/Simple%20Neural%20Net%20Classifier.ipynb) goes through all the steps needed to load the dataset, build nd train a model using it, evaluate the model and visualize the predictions.
### Dataset layout
The archive is distributed as a Python pickle file which is intended to be loaded by the CPUNKS-10K Here is a python routine which will open the files and return training and test data sets for use in Tensorflow with Python 3.
`(x_train, y_train), (x_test, y_test) = cpunks10k.load_data()`
Loaded using the defaults, each of the batch files are grouped into a training set and paired with corresponding test data containing with the following elements:

x_train – an 8000x24x24x4 numpy array of uint8s. Each row of the array stores a 24x24x4 colour image with four channels: red, green, blue & transparency.
x_test – a 2000x24x24x4 numpy array of uint8s. Each row of the array stores a 24x24x4 colour image with four channels: red, green, blue & transparency.
y_train – an 8000 row numpy array with integers in the range 0 … 92. The number at index i indicates the label of the ith image in the array data.
y_test – a 2000 row numpy array with integers in the range 0 … 92. The number at index i indicates the label of the ith image in the array data.

The dataset contains another file, labels.meta ,which contains a Python dictionary object with the following entries:

label_names – a 93-element list which gives meaningful names to the numeric labels in the labels array described above. For example, label_names[0] == “alien”, label_names[1] == “ape”, etc.