Metadata-Version: 2.1
Name: numpy-datasets
Version: 0.0.1
Summary: Datasets downloading/batching/processing in Numpy
Home-page: https://github.com/RandallBalestriero/numpy-datasets.git
Author: Randall Balestriero
Author-email: randallbalestriero@gmail.com
License: Apache-2.0
Description: 
        /* ![SymJAX logo](./docs/img/symjax_logo.png)
        */
        
        # All dataset utilities (downloading/loading/batching/processing) in Numpy ![Continuous integration](https://github.com/RandallBalestriero/numpy-datasets/workflows/Continuous%20integration/badge.svg) ![license](https://img.shields.io/badge/license-Apache%202-blue) <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
        This is an under-development research project, not an official product, expect bugs and sharp edges; please help by trying it out, reporting bugs.
        [**Reference docs**](https://numpy-datasets.readthedocs.io/en/latest/)
        
        
        ## What is and why doing numpy-datasets ?
        
        * First, numpy-datasets offers out-of-the-box dataset download and loading only based on Numpy and core Python libraries.
        * Second, numpy-datasets offers utilities such as (mini-)batching a.k.a looping through a dataset one chunk at a time, or preprocessing techniques that are highly suited for machine learning and deep learning pipelines.
        * Third, numpy-datasets offers many options to transparently deal with very large datasets. For example, automatic mini-batching with a priori caching of the next batch, online preprocessing, and the likes.
        * Fourth, numpy-datasets does not only focus on computer vision datasets but also offers plenty in time-series datasets, with a constantly groing collection of implemented datasets.
        
        ## Examples
        
        ```python
        import sys
        import symjax as sj
        import symjax.tensor as T
        
        # create our variable to be optimized
        mu = T.Variable(T.random.normal((), seed=1))
        
        # create our cost
        cost = T.exp(-(mu-1)**2)
        
        # get the gradient, notice that it is itself a tensor that can then
        # be manipulated as well
        g = sj.gradients(cost, mu)
        print(g)
        
        # (Tensor: shape=(), dtype=float32)
        
        # create the compield function that will compute the cost and apply
        # the update onto the variable
        f = sj.function(outputs=cost, updates={mu:mu-0.2*g})
        
        for i in range(10):
            print(f())
        
        # 0.008471076
        # 0.008201109
        # 0.007946267
        # ...
        ```
        
        ## Installation
        
        Installation is direct with pip as described in this [**guide**](https://numpy-datasets.readthedocs.io/en/latest/user/installation.html).
        
Platform: UNKNOWN
Classifier: Natural Language :: English
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Requires-Python: >=3.6
Description-Content-Type: text/markdown
