In last week’s post, I introduced the k-NN machine learning algorithm which we then applied to the task of image classification.

Using the k-NN algorithm, we obtained **57.58% classification accuracy** on the Kaggle Dogs vs. Cats dataset challenge:

**The question is: “Can we do better?”**

Of course we can! Obtaining higher accuracy for nearly any machine learning algorithm boils down to tweaking various knobs and levels.

In the case of k-NN, we can tune *k*, the number of nearest neighbors. We can also tune our distance metric/similarity function as well.

Of course, hyperparameter tuning has implications *outside* of the k-NN algorithm as well. In the context of Deep Learning and Convolutional Neural Networks, we can easily have *hundreds* of various hyperparameters to tune and play with (although in practice we try to limit the number of variables to tune to a small handful), each affecting our overall classification to some (potentially unknown) degree.

Because of this, it’s important to understand the concept of *hyperparameter tuning* and how your choice in hyperparameters can *dramatically* impact your classification accuracy.

#### Looking for the source code to this post?

Jump Right To The Downloads Section## How to tune hyperparameters with Python and scikit-learn

In the remainder of today’s tutorial, I’ll be demonstrating how to tune k-NN hyperparameters for the Dogs vs. Cats dataset. We’ll start with a discussion on *what hyperparameters are*, followed by viewing a concrete example on *tuning k-NN hyperparameters.*

We’ll then explore how to tune k-NN hyperparameters using two search methods: *Grid Search* and *Randomized Search.*

As our results will demonstrate, we can improve our classification accuracy from **57.58% to over 64%!**

### What are hyperparameters?

Hyperparameters are simply the knobs and levels you pull and turn when building a machine learning classifier. The process of tuning hyperparameters is more formally called *hyperparameter optimization*.

So what’s the difference between a normal “model parameter” and a “hyperparameter”?

Well, a standard “model parameter” is normally an internal variable that is optimized in some fashion. In the context of Linear Regression, Logistic Regression, and Support Vector Machines, we would think of parameters as the weight vector coefficients found by the learning algorithm.

On the other hand, “hyperparameters” are normally set by a human designer or tuned via algorithmic approaches. Examples of hyperparameters include the number of neighbors *k* in the k-Nearest Neighbor algorithm, the learning rate *alpha* of a Neural Network, or the number of filters learned in a given convolutional layer in a CNN.

In general, model parameters are optimized according to some *loss function*, while hyperparameters are instead *searched for* by exploring various settings to see which values provided the highest level of accuracy.

Because of this, it tends to be easier to tune model parameters (since we’re optimizing some objective function based on our training data) whereas hyperparameters can require a nearly blind search to find optimal ones.

### k-NN hyperparameters

As a concrete example of tuning hyperparameters, let’s consider the k-Nearest Neighbor classification algorithm. For your standard k-NN implementation, there are *two primary hyperparameters *that you’ll want to tune:

- The number of neighbors
*k*. - The distance metric/similarity function.

Both of these values can dramatically affect the accuracy of your k-NN classifier. To demonstrate this in the context of image classification, let’s apply hyperparameter tuning to our Kaggle Dogs vs. Cats dataset from last week.

Open up a new file, name it `knn_tune.py`

, and insert the following code:

# import the necessary packages from sklearn.neighbors import KNeighborsClassifier from sklearn.grid_search import RandomizedSearchCV from sklearn.grid_search import GridSearchCV from sklearn.cross_validation import train_test_split from imutils import paths import numpy as np import argparse import imutils import time import cv2 import os

**Lines 2-12** start by importing our required Python packages. We’ll be making heavy use of the scikit-learn library, so if you do not have it installed, make sure you follow these instructions.

We’ll also be using my personal imutils library, so make sure you have it installed as well:

$ pip install imutils

Next, we’ll define our `extract_color_histogram`

function:

def extract_color_histogram(image, bins=(8, 8, 8)): # extract a 3D color histogram from the HSV color space using # the supplied number of `bins` per channel hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) hist = cv2.calcHist([hsv], [0, 1, 2], None, bins, [0, 180, 0, 256, 0, 256]) # handle normalizing the histogram if we are using OpenCV 2.4.X if imutils.is_cv2(): hist = cv2.normalize(hist) # otherwise, perform "in place" normalization in OpenCV 3 (I # personally hate the way this is done else: cv2.normalize(hist, hist) # return the flattened histogram as the feature vector return hist.flatten()

This function accepts an input `image`

along with a number of `bins`

for each channel of the image.

We convert the image to the HSV color space and compute a 3D color histogram to characterize the color distribution of the image (**Lines 17-19**).

This histogram is then flattened into a single *8 x 8 x 8 = 512-d* feature vector that is returned to the calling function.

For a more detailed review of this method, please refer to last week’s blog post.

# construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-d", "--dataset", required=True, help="path to input dataset") ap.add_argument("-j", "--jobs", type=int, default=-1, help="# of jobs for k-NN distance (-1 uses all available cores)") args = vars(ap.parse_args()) # grab the list of images that we'll be describing print("[INFO] describing images...") imagePaths = list(paths.list_images(args["dataset"])) # initialize the data matrix and labels list data = [] labels = []

**Lines 34-39** handle parsing our command line arguments. We only need two switches here:

`--dataset`

: The path to our input Dogs vs. Cats dataset from the Kaggle challenge.`--jobs`

: The number of processors/cores to utilize when computing the nearest neighbors for a particular data point. Setting this value to`-1`

indicates*all*available processors/cores should be used. Again, for a more detailed review of these arguments, please refer to last week’s tutorial.

**Line 43** grabs the paths to our 25,000 input images while **Lines 46 and 47** initializes the `data`

list (where we’ll store the color histogram extracted from each image) and `labels`

list (either “dog” or “cat” for each input image), respectively.

Next, we can loop over our `imagePaths`

and describe them:

# loop over the input images for (i, imagePath) in enumerate(imagePaths): # load the image and extract the class label (assuming that our # path as the format: /path/to/dataset/{class}.{image_num}.jpg image = cv2.imread(imagePath) label = imagePath.split(os.path.sep)[-1].split(".")[0] # extract a color histogram from the image, then update the # data matrix and labels list hist = extract_color_histogram(image) data.append(hist) labels.append(label) # show an update every 1,000 images if i > 0 and i % 1000 == 0: print("[INFO] processed {}/{}".format(i, len(imagePaths)))

**Line 50** starts looping over each of the `imagePaths`

. For each `imagePath`

, we load it from disk and extract the `label`

(**Lines 53 and 54**).

Now that we have our `image`

, we compute a color histogram (**Line 58**), followed by updating the `data`

and `labels`

lists (**Lines 59 and 60**).

Finally, **Lines 63 and 64** display the feature extraction progress to our screen.

In order to train and evaluate our k-NN classifier, we’ll need to partition our `data`

into two splits: a *training split* and a *testing split*:

# partition the data into training and testing splits, using 75% # of the data for training and the remaining 25% for testing print("[INFO] constructing training/testing split...") (trainData, testData, trainLabels, testLabels) = train_test_split( data, labels, test_size=0.25, random_state=42)

Here we’ll be using 75% of our data for training and the remaining 25% for evaluation.

Finally, let’s define the set of hyperparameters we are going to optimize over:

# construct the set of hyperparameters to tune params = {"n_neighbors": np.arange(1, 31, 2), "metric": ["euclidean", "cityblock"]}

The above code block defines a `params`

dictionary which contains two keys:

`n_neighbors`

: The number of nearest neighbors*k*in the k-NN algorithm. Here we’ll search over the*odd integers*in the range*[0, 29]*(keep in mind that the`np.arange`

function is*exclusive*).`metric`

: This is the distance function/similarity metric for k-NN. Normally this defaults to the Euclidean distance, but we could also use any function that returns a single floating point value representing how “similar” two images are. In this case, we’ll search over both the Euclidean distance and Manhattan/City block distance.

Now that we have defined the hyperparameters we want to search over, we need a method that actually *applies* the search. Luckily, the scikit-learn library already has two methods that can perform hyperparameter search for us: **Grid Search** and **Randomized Search**.

As we’ll find out, it’s normally preferable to used Randomized Search over Grid Search in nearly all circumstances.

### Grid Search hyperparameters

The Grid Search tuning algorithm will methodically (and exhaustively) train and evaluate a machine learning classifier for ** each and every combination** of hyperparameter values.

In this case, given 16 unique values of *k* and 2 unique values for our *distance metric*, a Grid Search will apply **30 different experiments to determine the optimal value.**

You can see how a Grid Search is performed in the following code segment:

# tune the hyperparameters via a cross-validated grid search print("[INFO] tuning hyperparameters via grid search") model = KNeighborsClassifier(n_jobs=args["jobs"]) grid = GridSearchCV(model, params) start = time.time() grid.fit(trainData, trainLabels) # evaluate the best grid searched model on the testing data print("[INFO] grid search took {:.2f} seconds".format( time.time() - start)) acc = grid.score(testData, testLabels) print("[INFO] grid search accuracy: {:.2f}%".format(acc * 100)) print("[INFO] grid search best parameters: {}".format( grid.best_params_))

The primary benefit of the Grid Search algorithm is also it’s major drawback: as an *exhaustive search* your number of possible parameter values *explodes* as both the number of *hyperparameters and hyperparameter values* increases.

Sure, you get to evaluate each and every combination of hyperparameter — **but you pay a cost **— it’s a *very* time consuming cost. And in most cases, *it’s hardly worth it.*

As I explain in the *“Use Randomized Search for hyperparameter tuning (in most situations)”* section below, there are *rarely* just *one* set of hyperparameters that obtain the highest accuracy.

Instead, there are “hot zones” of hyperparameters that all obtain near identical accuracy. The goal is to explore as many of these “zones” of hyperparameters a quickly as possible and locate one of these “hot zones”. It turns out that a random search is a great way to do this.

### Randomized Search hyperparameters

The Random Search approach to hyperparameter tuning will *sample* hyperparameters from our `params`

dictionary via a **random, uniform distribution. **Given a set of randomly sampled parameters, a model is then trained and evaluated.

We perform this set of random hyperparameter sampling and model construction/evaluation for a *preset number of times. *You set the number of evaluations to be **as long as you’re willing to wait**. If you’re impatient and in a hurry, make this value low. And if you have the time to spend on a longer experiment, increase the number of iterations.

In either case, the goal of a Randomized Search is to explore a large set of possible hyperparameter spaces quickly — and the best way to accomplish this is via simple random sampling. **And in practice, it works quite well!**

You can find the code to perform a Randomized Search of hyperparameters for the k-NN algorithm below:

# tune the hyperparameters via a randomized search grid = RandomizedSearchCV(model, params) start = time.time() grid.fit(trainData, trainLabels) # evaluate the best randomized searched model on the testing # data print("[INFO] randomized search took {:.2f} seconds".format( time.time() - start)) acc = grid.score(testData, testLabels) print("[INFO] grid search accuracy: {:.2f}%".format(acc * 100)) print("[INFO] randomized search best parameters: {}".format( grid.best_params_))

### Hyperparameter tuning with Python and scikit-learn results

To tune the hyperparameters of our k-NN algorithm, make sure you:

- Download the source code to this tutorial using the
form at the bottom of this post.*“Downloads”* - Head over to the Kaggle Dogs vs. Cats competition page and download the dataset.

From there, you can execute the following command to tune the hyperparameters:

$ python knn_tune.py --dataset kaggle_dogs_vs_cats

You’ll probably want to go for a nice walk and stretch your legs will the `knn_tune.py`

script executes.

On my machine, it took **19m 26s** to complete, with over 86% of this time spent Grid Searching:

As you can see from the output screenshot, the Grid Search method found that *k=25* and *metric=’cityblock’* obtained the highest accuracy of 64.03%. **However, this Grid Search took 13 minutes.**

On the other hand, the Randomized Search obtained an identical accuracy of 64.03% — **and it completed in under 5 minutes.**

Both of these hyperparameter tuning methods improved our classification accuracy (64.03% accuracy, up from 57.58% from last week’s post) — but the Randomized Search was *much* more efficient.

### Use Randomized Search for hyperparameter tuning (in most situations)

Unless your search space is small and can easily be enumerated, a Randomized Search will tend to be more efficient and yield *better results faster.*

As our experiments demonstrated, Randomized Search was able to obtain **64.03% accuracy in < 5 minutes** while an exhaustive Grid Search took a much longer **13 minutes to obtain an identical 64.03% accuracy **— that’s a 202% increase in evaluation time for identical accuracy!

In general, there isn’t just *one* set of hyperparameters that obtains optimal results — instead, there are usually a *set* of them that exist towards the bottom of a concave bowl (i.e., the optimization surface).

As long as you hit just *one* of these parameters towards the bottom of the bowl, you’ll still obtain the same accuracy as if you enumerated *all possibilities* along the bowl. Furthermore, you’ll be able to explore various regions of this bowl *faster* by applying a Randomized Search.

Overall, this will lead to faster, more efficient hyperparameter tunings in most situations.

## Summary

In today’s blog post, I demonstrated how to tune hyperparameters to machine learning algorithms using the Python programming language and the scikit-learn library.

First, I defined, the difference between standard “model parameters” and the “hyperparameters” that need to be tuned.

From there, we applied two methods to tune hyperparameters:

- An exhaustive Grid Search
- A Randomized Search

Both of these hyperparameter tuning routines were then applied to the k-NN algorithm and the Kaggle Dogs vs. Cats dataset.

Each respective tuning algorithm obtained identical accuracy — *but the Randomized Search was able to obtain this increase of accuracy in a fraction of the time!*

**In general, I highly encourage you to use Randomized Search when tuning hyperparameters.** You’ll often find that there is rarely just

*one*set of hyperparameters that obtains optimal accuracy. Instead, there are “hot zones” of hyperparameters that will obtain near-identical accuracy — the goal is to explore as many zones and try to land on one of these zones as fast as possible.

Given no *a priori* knowledge of good hyperparameter choices, a Randomized Search to hyperparameter tuning is the most optimal way to find reasonable hyperparameter values in a short amount of time as it allows you to explore many areas of the optimization surface.

Anyway, I hope you enjoyed this blog post! I’ll be back next week to discuss the basics of linear classification (and the role it plays in Neural Networks and image classification).

**But before you go, be sure to signup for the PyImageSearch Newsletter using the form below to be notified when future blog posts are published!**

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Paulo

Of course we can!

John

Great Post! Thanks for the great content.

Adrian Rosebrock

Thanks John, I’m happy you found it useful!

Sah San

Hi,

How do we know which hyperparameter to include in either the gridsearchcv or randomsearch?

For example, decision trees has many hyperparameter such as

min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, max_leaf_nodes.

I don’t know if we can include all these hyperparameter .

Any comment would be appreciated. Thanks

Adrian Rosebrock

Typically you would research the model you want to apply and the associated hyperparameters. This normally involves reading the original publication(s) where the authors share which hyperparameters are the most important.

Sah San

Thank you. It was so helpful

nawal

Hi Adrian,

I thinks that’s in line 101you want to type:

print(“[INFO] randomized search accuracy: {:.2f}%”.format(acc * 100))

Not

print(“[INFO] grid search accuracy: {:.2f}%”.format(acc * 100))

??