Stochastic Gradient Descent (SGD) with Python
In last week’s blog post, we discussed gradient descent , a first-order optimization algorithm that can be used to learn a set of classifier coefficients for parameterized learning .
However, the “vanilla” implementation of gradient descent can be prohibitively slow to run on large datasets ― in fact, it can even be considered computationally wasteful.
Instead, we should apply Stochastic Gradient Descent (SGD) , a simple modification to the standard gradient descent algorithm that computes the gradient and updates our weight matrix W on small batches of training data , rather than the entire training set itself.
While this leads to “noiser” weight updates, it also allows us to take more steps along the gradient ( 1 step for each batch versus 1 step per epoch ), ultimately leading to faster convergence and no negative affects to loss and classification accuracy.
To learn more about Stochastic Gradient Descent, keep reading.
Looking for the source code to this post?Jump right to the downloads section. Stochastic Gradient Descent (SGD) with python
Taking a look at last week’s blog post , it should be (at least somewhat) obviousthat the gradient descent algorithm will run very slowly on large datasets. The reason for this “slowness” is because each iteration of gradient descent requires that we compute a prediction for each training point in our training data.
For image datasets such as ImageNet where we have over 1.2 million training images, this computation can take a long time.
It also turns out that computing predictions for every training data point before taking a step and updating our weight matrix W is computationally wasteful (and doesn’t help us in the long run).
Instead, what we should do is batch our updates.Updating our gradient descent optimization algorithm
Before I discuss Stochastic Gradient Descent in more detail, let’s first look at the original gradient descent pseudocode and then the updated, SGD pseudocode, both inspired by the CS231n course slides .
Below follows the pseudocode for vanilla gradient descent:while True: Wgradient = evaluate_gradient(loss, data, W) W += -alpha * Wgradient
And here we can see the pseudocode for Stochastic Gradient Descent:while True: batch = next_training_batch(data, 256) Wgradient = evaluate_gradient(loss, batch, W) W += -alpha * Wgradient
As you can see, the implementations are quite similar.
The only difference between vanilla gradient descent and Stochastic Gradient Descent is the addition of the next_training_batch function. Instead of computing our gradient over the entire data set, we instead sample our data, yielding a batch .
We then evaluate the gradient on this batch and update our weight matrix W .
Note:For an implementation perspective, we also randomize our training samples before applying SGD.Batching gradient descent for machine learning
After looking at the pseudocode for SGD, you’ll immediately notice an introduction of a new parameter: the batch size.
In a “purist” implementation ofSGD, your mini-batch size would be set to 1 . However, we often uses mini-batches that are > 1 . Typical values include 32 , 64 , 128 , and 256 .
So, why are these common mini-batch size values?
To start, using batches > 1 helps reduce variance in the parameter update , ultimately leading to a more stable convergence.
Secondly, optimized matrix operation libraries are often more efficient when the input matrix size is a power of 2.
In general, the mini-batch size is not a hyperparameter that you should worry much about. You basically determine how many training examples will fit on your GPU/main memory and then use the nearest power of 2 as the batch size.Implementing Stochastic Gradient Descent (SGD) with Python
We are now ready to update our code from last week’s blog post on vanilla gradient descent . Since I have already reviewed this code in detail earlier, I’ll defer an exhaustive, thorough review of each line of code to last week’s post.
That said, I will still be pointing out the salient, importantlines of code in this example.
To get started, open up a new file, name it sgd . py , and insert the following code:# import the necessary packages import matplotlib.pyplotas plt from sklearn.datasets.samples_generatorimport make_blobs import numpyas np import argparse def sigmoid_activation(x): # compute and return the sigmoid activation value for a # given input value return 1.0 / (1 + np.exp(-x)) def next_batch(X, y, batchSize): # loop over our dataset `X` in mini-batches of size `batchSize` for i in np.arange(0, X.shape, batchSize): # yield a tuple of the current batched data and labels yield (X[i:i + batchSize], y[i:i + batchSize])
Lines 2-5start by importing our required Python packages. Then, Line 7 defines our sigmoid_activation function used during the training process.
In order to apply Stochastic Gradient Descent, we need a function that yields mini-batches of training data ― and that is exactly what the next_batch function on Lines 12-16 does.
The next_method requires tree parameters:X : Our training dataset of feature vectors. y : The class labels associated with each of the training data points. batchSize : The size of each mini-batch that will be returned.
Lines 14-16then loop over our training examples, yielding subsets of both X and y as mini-batches.
Next, let’s parse our command line arguments:# construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-e", "--epochs", type=float, default=100, help="# of epochs") ap.add_argument("-a", "--alpha", type=float, default=0.01, help="learning rate") ap.add_argument("-b", "--batch-size", type=int, default=32, help="size of SGD mini-batches") args = vars(ap.parse_args()) Lines 19-26par
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