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mnistdb\n\nmnistdb is a library which loads the\n[MNIST database of handwritten digits](http://yann.lecun.com/exdb/mnist/)\ninto numpy arrays.\n\n## Install\n\n    pip install mnistdb\n\n## Running\n\n```Python\nimport mnistdb.io as mio\n\ndata = mio.load()\n\n# shape of data\nassert x.trainX.shape == (60000, 784)\nassert x.trainY.shape == (60000,)\nassert x.testX.shape == (10000, 784)\nassert x.testY.shape == (10000,)\n\n# With the parameter scaled=True all pixel values are\n# scaled into the interval [0,1]\n\ndata = mio.load(scaled=True)\n```\n\nWhen you're running the code for the first time mnistdb will download\nthe MNIST database of handwritten digits from the Internet. The database\nwill be stored in `~/.mnistdb` so that does not need to download the\ndatabase for subsequent calls.\n\nIf you want one-hot encoded labels call `load` with the parameter\n`one_hot=True`.\n\n```Python\nimport mnistdb.io as mio\n\nn = mio.load()\no = mio.load(one_hot=True)\n\n# print the labels of the first five training examples\nprint(n.trainY[range(5)])\n\n# print the one-hot encoded labels of the first five training examples\nprint(o.trainY[range(5), :])\n```\n\nThe output is:\n\n```\n[5 0 4 1 9]\n[[ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]\n [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]]\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaniel-e%2Fmnistdb","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdaniel-e%2Fmnistdb","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaniel-e%2Fmnistdb/lists"}