Python chainer 模块，datasets() 实例源码

def get_mnist():
    train, test = chainer.datasets.get_mnist(ndim=3)

    train_data = [t for t in train]
    test_data = [t for t in test]

    train_data = np.array(train_data)
    test_data = np.array(test_data)

    train_data = np.expand_dims(train_data, 1)
    test_data = np.expand_dims(test_data, 1)

    train_xs = train_data[:,:,0].T
    train_ys = train_data[:,:,1].T

    test_xs = test_data[:,:,0].T
    test_ys = test_data[:,:,1].T

    train = TupleDataset(*(train_xs.tolist() + train_ys.tolist()))
    test = TupleDataset(*(test_xs.tolist() + test_ys.tolist()))

    return train,test

def score_core(self, X, y=None, sample_weight=None, batchsize=16):
        # Type check
        X, y = self._check_X_y(X, y)
        # during GridSearch, which only assumes score(X, y) interface.
        if y is None:
            test = X
            if isinstance(test, numpy.ndarray):  # TODO: reivew
                print('score_core numpy.ndarray received...')
                test = chainer.datasets.TupleDataset(test)
        else:
            test = chainer.datasets.TupleDataset(X, y)
        # For Classifier
        # `accuracy` is calculated as score, using `forward_batch`
        # For regressor
        # `loss` is calculated as score, using `forward_batch`
        self.forward_batch(test, batchsize=batchsize, retain_inputs=False, calc_score=True)
        return self.total_score

def main(args):
    # get datasets
    source_train, source_test = chainer.datasets.get_svhn()
    target_train, target_test = chainer.datasets.get_mnist(ndim=3, rgb_format=True)
    source = source_train, source_test

    # resize mnist to 32x32
    def transform(in_data):
        img, label = in_data
        img = resize(img, (32, 32))
        return img, label

    target_train = TransformDataset(target_train, transform)
    target_test = TransformDataset(target_test, transform)

    target = target_train, target_test

    # load pretrained source, or perform pretraining
    pretrained = os.path.join(args.output, args.pretrained_source)
    if not os.path.isfile(pretrained):
        source_cnn = pretrain_source_cnn(source, args)
    else:
        source_cnn = Loss(num_classes=10)
        serializers.load_npz(pretrained, source_cnn)

    # how well does this perform on target domain?
    test_pretrained_on_target(source_cnn, target, args)

    # initialize the target cnn (do not use source_cnn.copy)
    target_cnn = Loss(num_classes=10)
    # copy parameters from source cnn to target cnn
    target_cnn.copyparams(source_cnn)

    train_target_cnn(source, target, source_cnn, target_cnn, args)