Metadata-Version: 1.1
Name: nolearn_utils
Version: 0.3.1
Summary: Utilities for nolearn.lasagne
Home-page: https://github.com/4Catalyzer/nolearn-utils
Author: Felix Lau
Author-email: felixlaumon@gmail.com
License: MIT
Description: # nolearn-utils
        
        [![Build Status](https://travis-ci.org/felixlaumon/nolearn_utils.svg?branch=master)](https://travis-ci.org/felixlaumon/nolearn_utils)
        
        Iterators and handlers for nolearn.lasagne to allow efficient real-time image augmentation and training progress monitoring
        
        ## Real-time image augmentation
        
        - `ShuffleBatchIteratorMixin` to shuffle training samples
        - `ReadImageBatchIteratorMixin` to transform image file path into image as color or as gray, and with specified image size
        - `RandomFlipBatchIteratorMixin` to randomly (uniform) flip the image horizontally or verticaly
        - `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)
        - `LCNBatchIteratorMixin` to perform local contrast normalization to images
        - `MeanSubtractBatchIteratorMixin` to subtract samples from the pre-calculated mean
        
        Example of using iterators as below:
        
            train_iterator_mixins = [
                ShuffleBatchIteratorMixin,
                ReadImageBatchIteratorMixin,
                RandomFlipBatchIteratorMixin,
                AffineTransformBatchIteratorMixin,
                BufferedBatchIteratorMixin,
            ]
            TrainIterator = make_iterator('TrainIterator', train_iterator_mixins)
        
            train_iterator_kwargs = {
                'buffer_size': 5,
                'batch_size': batch_size,
                'read_image_size': (image_size, image_size),
                'read_image_as_gray': False,
                'read_image_prefix_path': './data/train/',
                'flip_horizontal_p': 0.5,
                'flip_vertical_p': 0,
                'affine_p': 0.5,
                'affine_scale_choices': np.linspace(0.75, 1.25, 5),
                'affine_translation_choices': np.arange(-3, 4, 1),
                'affine_rotation_choices': np.arange(-45, 50, 5)
            }
            train_iterator = TrainIterator(**train_iterator_kwargs)
        
        The `BaseBatchIterator` is also modified from `nolearn.lasagne` to provide a progress bar for training process for each iteration
        
        ## Handlers
        
        - `EarlyStopping` stops training when loss stop improving
        - `StepDecay` to gradually reduce a parameter (e.g. learning rate) over time
        - `SaveTrainingHistory` to save training history (e.g. training loss)
        - `PlotTrainingHistory` to plot out training loss and validation accuracy
          over time after each iteration with matplotlib
        
        ## Examples
        
        Example code requires `scikit-learn`
        
        ### MNIST
        
        `example/mnist/train.py` should produce a model of about 99.5% accuracy in less than 50 epoch.
        
        MNIST data can be downloaded from
        [Kaggle](https://www.kaggle.com/c/digit-recognizer).
        
        ### CIFAR10
        
        CIFAR10 images can be downloaded from [Kaggle](https://www.kaggle.com/c/cifar-10/data). Place the downloaded data as follows:
        
            examples/cifar10
            ├── data
            │   ├── train
            │   |   ├── 1.png
            │   |   ├── 2.png
            │   |   ├── 3.png
            │   |   ├── ...
            │   └── trainLabels.csv
            └── train.py
        
        `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)
        
        
        ## TODO
        
        - [ ] Embarrassingly parallelize transform
        
        
        ## License
        
        MIT & BSD
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 2.7
