I was looking at a logistic regression tutorial and I noticed that the MNIST images were not one-hot. Does anyone know why?

```
import numpy as np
import torch
from torch.autograd import Variable
from torch import optim
from data_util import load_mnist
def build_model(input_dim, output_dim):
# We don't need the softmax layer here since CrossEntropyLoss already
# uses it internally.
model = torch.nn.Sequential()
model.add_module("linear",
torch.nn.Linear(input_dim, output_dim, bias=False))
return model
def train(model, loss, optimizer, x_val, y_val):
x = Variable(x_val, requires_grad=False)
y = Variable(y_val, requires_grad=False)
# Reset gradient
optimizer.zero_grad()
# Forward
fx = model.forward(x)
output = loss.forward(fx, y)
# Backward
output.backward()
# Update parameters
optimizer.step()
return output.data[0]
def predict(model, x_val):
x = Variable(x_val, requires_grad=False)
output = model.forward(x)
return output.data.numpy().argmax(axis=1)
def main():
torch.manual_seed(42)
trX, teX, trY, teY = load_mnist(onehot=False)
trX = torch.from_numpy(trX).float()
teX = torch.from_numpy(teX).float()
trY = torch.from_numpy(trY).long()
n_examples, n_features = trX.size()
n_classes = 10
model = build_model(n_features, n_classes)
loss = torch.nn.CrossEntropyLoss(size_average=True)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
batch_size = 100
for i in range(100):
cost = 0.
num_batches = n_examples // batch_size
for k in range(num_batches):
start, end = k * batch_size, (k + 1) * batch_size
cost += train(model, loss, optimizer,
trX[start:end], trY[start:end])
predY = predict(model, teX)
print("Epoch %d, cost = %f, acc = %.2f%%"
% (i + 1, cost / num_batches, 100. * np.mean(predY == teY)))
if __name__ == "__main__":
main()
```