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        "author": "Felix Lau",
        "author_email": "felixlaumon@gmail.com",
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            "Programming Language :: Python :: 2.7"
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        "description": "# nolearn-utils\n\n[![Build Status](https://travis-ci.org/felixlaumon/nolearn_utils.svg?branch=master)](https://travis-ci.org/felixlaumon/nolearn_utils)\n\nIterators and handlers for nolearn.lasagne to allow efficient real-time image augmentation and training progress monitoring\n\n## Real-time image augmentation\n\n- `ShuffleBatchIteratorMixin` to shuffle training samples\n- `ReadImageBatchIteratorMixin` to transform image file path into image as color or as gray, and with specified image size\n- `RandomFlipBatchIteratorMixin` to randomly (uniform) flip the image horizontally or verticaly\n- `AffineTransformBatchIteratorMixin` to apply affine transformation (scale, rotate, translate) to randomly selected images from the given transformation options - `BufferedBatchIteratorMixin` to perform transformation in another thread automatically and put the result in a buffer (default size = 5)\n- `LCNBatchIteratorMixin` to perform local contrast normalization to images\n- `MeanSubtractBatchIteratorMixin` to subtract samples from the pre-calculated mean\n\nExample of using iterators as below:\n\n    train_iterator_mixins = [\n        ShuffleBatchIteratorMixin,\n        ReadImageBatchIteratorMixin,\n        RandomFlipBatchIteratorMixin,\n        AffineTransformBatchIteratorMixin,\n        BufferedBatchIteratorMixin,\n    ]\n    TrainIterator = make_iterator('TrainIterator', train_iterator_mixins)\n\n    train_iterator_kwargs = {\n        'buffer_size': 5,\n        'batch_size': batch_size,\n        'read_image_size': (image_size, image_size),\n        'read_image_as_gray': False,\n        'read_image_prefix_path': './data/train/',\n        'flip_horizontal_p': 0.5,\n        'flip_vertical_p': 0,\n        'affine_p': 0.5,\n        'affine_scale_choices': np.linspace(0.75, 1.25, 5),\n        'affine_translation_choices': np.arange(-3, 4, 1),\n        'affine_rotation_choices': np.arange(-45, 50, 5)\n    }\n    train_iterator = TrainIterator(**train_iterator_kwargs)\n\nThe `BaseBatchIterator` is also modified from `nolearn.lasagne` to provide a progress bar for training process for each iteration\n\n## Handlers\n\n- `EarlyStopping` stops training when loss stop improving\n- `StepDecay` to gradually reduce a parameter (e.g. learning rate) over time\n- `SaveTrainingHistory` to save training history (e.g. training loss)\n- `PlotTrainingHistory` to plot out training loss and validation accuracy\n  over time after each iteration with matplotlib\n\n## Examples\n\nExample code requires `scikit-learn`\n\n### MNIST\n\n`example/mnist/train.py` should produce a model of about 99.5% accuracy in less than 50 epoch.\n\nMNIST data can be downloaded from\n[Kaggle](https://www.kaggle.com/c/digit-recognizer).\n\n### CIFAR10\n\nCIFAR10 images can be downloaded from [Kaggle](https://www.kaggle.com/c/cifar-10/data). Place the downloaded data as follows:\n\n    examples/cifar10\n    \u251c\u2500\u2500 data\n    \u2502\u00a0\u00a0 \u251c\u2500\u2500 train\n    \u2502   |   \u251c\u2500\u2500 1.png\n    \u2502   |   \u251c\u2500\u2500 2.png\n    \u2502   |   \u251c\u2500\u2500 3.png\n    \u2502   |   \u251c\u2500\u2500 ...\n    \u2502\u00a0\u00a0 \u2514\u2500\u2500 trainLabels.csv\n    \u2514\u2500\u2500 train.py\n\n`example/cifat10/train.py` should produce a model at about 85% accuracy at 100 epoch. Images are read from disk and augmented at training time (from another thread)\n\n\n## TODO\n\n- [ ] Embarrassingly parallelize transform\n\n\n## License\n\nMIT & BSD\n\n\n\n\n",
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