在chainer中使用数组作为MNIST数据的标签

python模块 chainer 有一个 introduction 它使用其神经网络识别来自 MNIST database .

假设一个特定的手写数字 D.png 标记为 3 . 我已经习惯了以数组形式出现的标签,如下所示:

label = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]

然而 chainer 改为使用整数标记:

label = 3

数组标签对我来说更直观,因为输出预测也是一个数组。在不处理图像的神经网络中,我希望能够灵活地将标签设置为特定的数组。

我直接从chainer简介中包含了下面的代码。如果你通过解析 train 或 test 请注意,所有标签都是整数,而不是浮点。

如何使用数组作为标签而不是整数来运行训练/测试数据?

import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions

class MLP(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))
        y = self.l3(h2)
        return y

train, test = datasets.get_mnist()

train_iter = iterators.SerialIterator(train, batch_size=100, shuffle=True)
test_iter = iterators.SerialIterator(test, batch_size=100, repeat=False, shuffle=False)

model = L.Classifier(MLP(100, 10))  # the input size, 784, is inferred
optimizer = optimizers.SGD()
optimizer.setup(model)

updater = training.StandardUpdater(train_iter, optimizer)
trainer = training.Trainer(updater, (20, 'epoch'), out='result')

trainer.extend(extensions.Evaluator(test_iter, model))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(['epoch', 'main/accuracy', 'validation/main/accuracy']))
trainer.extend(extensions.ProgressBar())
trainer.run()