The python module chainer has an introduction where it uses its neural network to recognize handwritten digits from the MNIST database.
Assuming that a particular handwritten digit D.png is labeled as a 3. I'm used to the label appearing as an array as follows:
label = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
However, chainer labels with an integer instead:
label = 3
The array-label is more intuitive to me because the output prediction is an array as well. In neural networks that don't deal with images, I want the flexibility to give the label to be a specific array.
I have included code below directly from the chainer introduction. If you parse through train or test dataset, notice that all of the labels are integers and not floats.
How would I run training/test data with arrays as labels instead of integers?
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()
The Classifier accepts a tuple containing image or other data as array(float32) and label as int. This is a convention for chainer and how it works there. If you print your label you will see that you are getting an array with dtype int. Both the image/non-image data and the label will be in arrays but with dtype float and int respectively.
So to answer your question: your labels are in array format itself with dtype int(as it should be for labels).
If you want your labels to be 0's and 1's instead of 1 to 10 then use One Hot Encoding(https://blog.cambridgespark.com/robust-one-hot-encoding-in-python-3e29bfcec77e).