mnist_training.py

#!/usr/bin/env python

# original code from http://www.mathgram.xyz/entry/chainer/bake/part3

from __future__ import print_function

try:
    import matplotlib
    matplotlib.use('Agg')
except ImportError:
    pass

import argparse

import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions

import os
import cv2
import numpy as np
from chainer.datasets import tuple_dataset


# Network definition
class MLP(chainer.Chain):

    def __init__(self, n_units, n_out):
        super(MLP, self).__init__()
        with self.init_scope():
            # the size of the inputs to each layer will be inferred
            self.l1 = L.Linear(None, n_units)  # n_in -> n_units
            self.l2 = L.Linear(None, n_units)  # n_units -> n_units
            self.l3 = L.Linear(None, n_out)  # n_units -> n_out

    def __call__(self, x):
        h1 = F.relu(self.l1(x))
        h2 = F.relu(self.l2(h1))
        return self.l3(h2)


def main():
    parser = argparse.ArgumentParser(description='Chainer example: MNIST')
    parser.add_argument('--batchsize', '-b', type=int, default=100,
                        help='Number of images in each mini-batch')
    parser.add_argument('--epoch', '-e', type=int, default=20,
                        help='Number of sweeps over the dataset to train')
    parser.add_argument('--frequency', '-f', type=int, default=-1,
                        help='Frequency of taking a snapshot')
    parser.add_argument('--gpu', '-g', type=int, default=-1,
                        help='GPU ID (negative value indicates CPU)')
    parser.add_argument('--out', '-o', default='result',
                        help='Directory to output the result')
    parser.add_argument('--resume', '-r', default='',
                        help='Resume the training from snapshot')
    parser.add_argument('--unit', '-u', type=int, default=1000,
                        help='Number of units')
    args = parser.parse_args()

    print('GPU: {}'.format(args.gpu))
    print('# unit: {}'.format(args.unit))
    print('# Minibatch-size: {}'.format(args.batchsize))
    print('# epoch: {}'.format(args.epoch))
    print('')

    # Set up a neural network to train
    # Classifier reports softmax cross entropy loss and accuracy at every
    # iteration, which will be used by the PrintReport extension below.
    model = L.Classifier(MLP(args.unit, 10))
    if args.gpu >= 0:
        # Make a specified GPU current
        chainer.cuda.get_device_from_id(args.gpu).use()
        model.to_gpu()  # Copy the model to the GPU

    # Setup an optimizer
    optimizer = chainer.optimizers.Adam()
    optimizer.setup(model)

    # Load the MNIST dataset
    # train, test = chainer.datasets.get_mnist()
    
    #その１　------データセット作成------

    #フォルダは整数で名前が付いています。
    #今回0が負例で、１が暦フォルダになっております。
    def getDataSet():
        #リストの作成
        X_train = []
        X_test = []
        y_train = []
        y_test = []

        for i in range(0,2):
            #まずは2値分類を目指すので暦フォルダとothersフォルダの中身だけ引っ張ってきます。
            path = "./" #ここにディレクトリのパスを設定
            imgList = os.listdir(path+str(i))
            #データを4:1の割合でtrainとtestに分けます。
            imgNum = len(imgList)
            cutNum = imgNum - imgNum/5
            for j in range(len(imgList)):
                imgSrc = cv2.imread(path+str(i)+"/"+imgList[j], 0)
                #またimreadはゴミを吸い込んでも、エラーで止まらずNoneを返してくれます。
                #ですので読み込み結果がNoneでしたらスキップしてもらいます。
                if imgSrc is None:continue

                if j < cutNum:
                    X_train.append(imgSrc)
                    y_train.append(i)
                else:
                    X_test.append(imgSrc)
                    y_test.append(i)
        return X_train,y_train,X_test,y_test

    # train, test = datasets.get_mnist(ndim=3)
    X_train,y_train,X_test,y_test = getDataSet()

    X_train = np.array(X_train).astype(np.float32).reshape((len(X_train), 784)) / 255
    y_train = np.array(y_train).astype(np.int32)
    X_test = np.array(X_test).astype(np.float32).reshape((len(X_test), 784)) / 255
    y_test = np.array(y_test).astype(np.int32)

    train = tuple_dataset.TupleDataset(X_train, y_train)
    test  = tuple_dataset.TupleDataset(X_test, y_test)
    
    

    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
    test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
                                                 repeat=False, shuffle=False)

    # Set up a trainer
    updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
    trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)

    # Evaluate the model with the test dataset for each epoch
    trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))

    # Dump a computational graph from 'loss' variable at the first iteration
    # The "main" refers to the target link of the "main" optimizer.
    trainer.extend(extensions.dump_graph('main/loss'))

    # Take a snapshot for each specified epoch
    frequency = args.epoch if args.frequency == -1 else max(1, args.frequency)
    trainer.extend(extensions.snapshot(), trigger=(frequency, 'epoch'))

    # Write a log of evaluation statistics for each epoch
    trainer.extend(extensions.LogReport())

    # Save two plot images to the result dir
    if extensions.PlotReport.available():
        trainer.extend(
            extensions.PlotReport(['main/loss', 'validation/main/loss'],
                                  'epoch', file_name='loss.png'))
        trainer.extend(
            extensions.PlotReport(
                ['main/accuracy', 'validation/main/accuracy'],
                'epoch', file_name='accuracy.png'))

    # Print selected entries of the log to stdout
    # Here "main" refers to the target link of the "main" optimizer again, and
    # "validation" refers to the default name of the Evaluator extension.
    # Entries other than 'epoch' are reported by the Classifier link, called by
    # either the updater or the evaluator.
    trainer.extend(extensions.PrintReport(
        ['epoch', 'main/loss', 'validation/main/loss',
         'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))

    # Print a progress bar to stdout
    trainer.extend(extensions.ProgressBar())

    if args.resume:
        # Resume from a snapshot
        chainer.serializers.load_npz(args.resume, trainer)

    # Run the training
    trainer.run()

    chainer.serializers.save_npz('my_mnist.model', model)

if __name__ == '__main__':
    main()