170 lines
3.4 KiB
Plaintext
170 lines
3.4 KiB
Plaintext
Metadata-Version: 2.1
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Name: python-mnist
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Version: 0.7
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Summary: Simple MNIST and EMNIST data parser written in pure Python
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Home-page: https://github.com/sorki/python-mnist
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Author: Richard Marko
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Author-email: srk@48.io
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License: BSD
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Platform: UNKNOWN
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Intended Audience :: Developers
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Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: Python
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python-mnist
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============
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Simple MNIST and EMNIST data parser written in pure Python.
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MNIST is a database of handwritten digits available on
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http://yann.lecun.com/exdb/mnist/. EMNIST is an extended MNIST database
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https://www.nist.gov/itl/iad/image-group/emnist-dataset.
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Requirements
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------------
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- Python 2 or Python 3
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Usage
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-----
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- ``git clone https://github.com/sorki/python-mnist``
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- ``cd python-mnist``
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- Get MNIST data:
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::
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./bin/mnist_get_data.sh
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- Check preview with:
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::
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PYTHONPATH=. ./bin/mnist_preview
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Installation
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------------
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Get the package from PyPi:
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::
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pip install python-mnist
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or install with ``setup.py``:
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::
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python setup.py install
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Code sample:
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::
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from mnist import MNIST
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mndata = MNIST('./dir_with_mnist_data_files')
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images, labels = mndata.load_training()
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To enable loading of gzip-ed files use:
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::
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mndata.gz = True
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Library tries to load files named t10k-images-idx3-ubyte
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train-labels-idx1-ubyte train-images-idx3-ubyte and
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t10k-labels-idx1-ubyte. If loading throws an exception check if these
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names match.
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EMNIST
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------
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- Get EMNIST data:
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::
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./bin/emnist_get_data.sh
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- Check preview with:
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::
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PYTHONPATH=. ./bin/emnist_preview
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To use EMNIST datasets you need to call:
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::
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mndata.select_emnist('digits')
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Where digits is one of the available EMNIST datasets. You can choose
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from
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- balanced
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- byclass
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- bymerge
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- digits
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- letters
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- mnist
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EMNIST loader uses gziped files by default, this can be disabled by by
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setting:
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::
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mndata.gz = False
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You also need to unpack EMNIST files as bin/emnist_get_data.sh script
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won't do it for you. EMNIST loader also needs to mirror and rotate
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images so it is a bit slower (If this is an issue for you, you should
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repack the data to avoid mirroring and rotation on each load).
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Notes
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-----
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This package doesn't use numpy by design as when I've tried to find a
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working implementation all of them were based on some archaic version of
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numpy and none of them worked. This loads data files with struct.unpack
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instead.
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Example
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-------
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::
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$ PYTHONPATH=. ./bin/mnist_preview
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Showing num: 3
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