3.7. Concise Implementation of Softmax Regression Colab

Open the notebook in Colab

Just as Gluon made it much easier to implement linear regression in Section 3.3, we will find it similarly (or possibly more) convenient for implementing classification models. Again, we begin with our import ritual.

import d2l
from mxnet import gluon, init, npx
from mxnet.gluon import nn
npx.set_np()

Let’s stick with the Fashion-MNIST dataset and keep the batch size at \(256\) as in the last section.

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

3.7.1. Initializing Model Parameters

As mentioned in Section 3.4, the output layer of softmax regression is a fully-connected (Dense) layer. Therefore, to implement our model, we just need to add one Dense layer with 10 outputs to our Sequential. Again, here, the Sequential is not really necessary, but we might as well form the habit since it will be ubiquitous when implementing deep models. Again, we initialize the weights at random with zero mean and standard deviation \(0.01\).

net = nn.Sequential()
net.add(nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))

3.7.2. The Softmax

In the previous example, we calculated our model’s output and then ran this output through the cross-entropy loss. Mathematically, that is a perfectly reasonable thing to do. However, from a computational perspective, exponentiation can be a source of numerical stability issues (as discussed in Section 17.8). Recall that the softmax function calculates \(\hat y_j = \frac{e^{z_j}}{\sum_{i=1}^{n} e^{z_i}}\), where \(\hat y_j\) is the \(j^\mathrm{th}\) element of yhat and \(z_j\) is the \(j^\mathrm{th}\) element of the input y_linear variable, as computed by the softmax.

If some of the \(z_i\) are very large (i.e., very positive), then \(e^{z_i}\) might be larger than the largest number we can have for certain types of float (i.e., overflow). This would make the denominator (and/or numerator) inf and we wind up encountering either \(0\), inf, or nan for \(\hat y_j\). In these situations we do not get a well-defined return value for cross_entropy. One trick to get around this is to first subtract \(\text{max}(z_i)\) from all \(z_i\) before proceeding with the softmax calculation. You can verify that this shifting of each \(z_i\) by constant factor does not change the return value of softmax.

After the subtraction and normalization step, it might be that possible that some \(z_j\) have large negative values and thus that the corresponding \(e^{z_j}\) will take values close to zero. These might be rounded to zero due to finite precision (i.e underflow), making \(\hat y_j\) zero and giving us -inf for \(\text{log}(\hat y_j)\). A few steps down the road in backpropagation, we might find ourselves faced with a screenful of the dreaded not-a-number (nan) results.

Fortunately, we are saved by the fact that even though we are computing exponential functions, we ultimately intend to take their log (when calculating the cross-entropy loss). By combining these two operators (softmax and cross_entropy) together, we can escape the numerical stability issues that might otherwise plague us during backpropagation. As shown in the equation below, we avoided calculating \(e^{z_j}\) and can instead \(z_j\) directly due to the canceling in \(\log(\exp(\cdot))\).

(3.7.1)\[\begin{split}\begin{aligned} \log{(\hat y_j)} & = \log\left( \frac{e^{z_j}}{\sum_{i=1}^{n} e^{z_i}}\right) \\ & = \log{(e^{z_j})}-\text{log}{\left( \sum_{i=1}^{n} e^{z_i} \right)} \\ & = z_j -\log{\left( \sum_{i=1}^{n} e^{z_i} \right)}. \end{aligned}\end{split}\]

We will want to keep the conventional softmax function handy in case we ever want to evaluate the probabilities output by our model. But instead of passing softmax probabilities into our new loss function, we will just pass the logits and compute the softmax and its log all at once inside the softmax_cross_entropy loss function, which does smart things like the log-sum-exp trick (see on Wikipedia).

loss = gluon.loss.SoftmaxCrossEntropyLoss()

3.7.3. Optimization Algorithm

Here, we use minibatch stochastic gradient descent with a learning rate of \(0.1\) as the optimization algorithm. Note that this is the same as we applied in the linear regression example and it illustrates the general applicability of the optimizers.

trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})

3.7.4. Training

Next we call the training function defined in the last section to train a model.

num_epochs = 10
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)

As before, this algorithm converges to a solution that achieves an accuracy of 83.7%, albeit this time with fewer lines of code than before. Note that in many cases, Gluon takes additional precautions beyond these most well-known tricks to ensure numerical stability, saving us from even more pitfalls that we would encounter if we tried to code all of our models from scratch in practice.

3.7.5. Exercises

1. Try adjusting the hyper-parameters, such as batch size, epoch, and learning rate, to see what the results are.

2. Why might the test accuracy decrease again after a while? How could we fix this?

3.7.6. Discussions