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Machine Learning¶

The dataset takes four features of flowers: sepal length, sepal width, petal length and petal width, and classifies them into three flower species (labels): setosa, versicolor or virginica.

Running the above code gives: The first line contains the labels (i.e flower species) of the testing data as predicted by our classifier, and the second line contains the actual flower species as given in the dataset.

Machine Learning¶

The dataset takes four features of flowers: sepal length, sepal width, petal length and petal width, and classifies them into three flower species (labels): setosa, versicolor or virginica.

Running the above code gives: The first line contains the labels (i.e flower species) of the testing data as predicted by our classifier, and the second line contains the actual flower species as given in the dataset.

How to Retrain an Image Classifier for New Categories

Modern image recognition models have millions of parameters.

requires a lot of labeled training data and a lot of computing power (hundreds

of this by taking a piece of a model that has already been trained on a related

extraction capabilities from powerful image classifiers trained on ImageNet

for many applications, works with moderate amounts of training data (thousands,

not millions of labeled images), and can be run in as little as thirty

example script on your own images, and will explain some of the options you have

Image by Kelly Sikkema Before you start any training, you'll need a set of images to teach the network about

how to prepare your own images, but to make it easy we've created an archive

of flower photos, run these commands: Once you have the images, you can download the example code from GitHub (it

is not part of the library installation): In the simplest cases the retrainer can then be run like this (takes

You can get a full listing with: This script loads the pre-trained module and trains a new classifier on top for

magic of transfer learning is that lower layers that have been trained to distinguish

The script can take thirty minutes or more to complete, depending on the speed of

This penultimate layer has been trained to output a set of values

our final layer retraining can work on new classes is that it turns out the

Because every image is reused multiple times during training and calculating each

bottleneck takes a significant amount of time, it speeds things up to cache

By default they're stored in the /tmp/bottleneck directory, and if

you rerun the script they'll be reused so you don't have to wait for this part

Once the bottlenecks are complete, the actual training of the top layer of the network

what percent of the images used in the current training batch were labeled

the training accuracy is based on images that the network has been able to

learn from so the network can overfit to the noise in the training data.

measure of the performance of the network is to measure its performance on a

data set not contained in the training data -- this is measured by the validation

low, that means the network is overfitting and memorizing particular features

so you can tell if the learning is working by keeping an eye on whether

the loss keeps trending downwards, ignoring the short-term noise.

By default this script will run 4,000 training steps.

at random from the training set, finds their bottlenecks from the cache, and

compared against the actual labels to update the final layer's weights through

reported accuracy improve, and after all the steps are done, a final test accuracy

evaluation is run on a set of images kept separate from the training and

value of between 90% and 95%, though the exact value will vary from run to

percent of the images in the test set that are given the correct label after

The script includes TensorBoard summaries that make it easier to understand, debug, and optimize the retraining.

For example, you can visualize the graph and statistics, such as how the weights or accuracy varied during training.

To launch TensorBoard, run this command during or after retraining: Once TensorBoard is running, navigate your web browser to localhost:6006

The script will write out the new model trained on your categories to /tmp/output_graph.pb,

read in, so you can start using your new model immediately.

the top layer, you will need to specify the new name in the script, for example

color values in the fixed range [0,1], so you do not need to set the --input_mean

You should see a list of flower labels, in most cases with daisy on top (though

If you've managed to get the script working on the flower example images, you can

Here's what the folder structure of the flowers archive looks like, to give you and

The first place to start is by looking at the images you've gathered, since the most

common issues we see with training come from the data that's being fed in.

For example, if you take all your photos indoors against a blank wall and

your users are trying to recognize objects outdoors, you probably won't see good

end up basing its prediction on the background color, not the features of the

only things you'll ever be asked to categorize are the classes of object you know

example: pictures tagged #daisy might also include people and characters named

If you're happy with your images, you can take a look at improving your results by

common way of improving the results of image training is by deforming, cropping,

of expanding the effective size of the training data thanks to all the possible

variations of the same images, and tends to help the network learn to cope

that the bottleneck caching is no longer useful, since input images are never reused

it's recommended you try this as a way of polishing your model only after you

mirror half of the images horizontally, which makes sense as long as those

images as a check to make sure that overfitting isn't occurring, since if we

split is to put 80% of the images into the main training set, keep 10% aside

test set, since they are likely to merely reflect more general problems in the

By default the script uses an image feature extraction module with a pretrained instance

it provides high accuracy results with moderate running time for the retraining

On the other hand, if you intend to deploy your model on mobile devices or other resource-constrained

with the module URL, for example: This will create a 9 MB model file in /tmp/output_graph.pb with a model that uses

number of weights (and hence the file size and speed) shrinks with the square

will need to specify the image size that your model expects, for example: For more information on deploying the retrained model to a mobile device, see the

Machine Learning in R for beginners

Now that you have loaded the Iris data set into RStudio, you should try to get a thorough understanding of what your data is about.

You see that there is a high correlation between the sepal length and the sepal width of the Setosa iris flowers, while the correlation is somewhat less high for the Virginica and Versicolor flowers: the data points are more spread out over the graph and don’t form a cluster like you can see in the case of the Setosa flowers.

Of course, you probably need to test this hypothesis a bit further if you want to be really sure of this: You see that when you combined all three species, the correlation was a bit stronger than it is when you look at the different species separately: the overall correlation is 0.96, while for Versicolor this is 0.79.

After a general visualized overview of the data, you can also view the data set by entering However, as you will see from the result of this command, this really isn’t the best way to inspect your data set thoroughly: the data set takes up a lot of space in the console, which will impede you from forming a clear idea about your data.

On the other hand, if you want to check the percentual division of the Species attribute, you can ask for a table of proportions: Note that the round argument rounds the values of the first argument, prop.table(table(iris$Species))*100 to the specified number of digits, which is one digit after the decimal point.

This will give you the minimum value, first quantile, median, mean, third quantile and maximum value of the data set Iris for numeric data types.

For the class variable, the count of factors will be returned: As you can see, the c() function is added to the original command: the columns petal width and sepal width are concatenated and a summary is then asked of just these two columns of the Iris data set.

To illustrate the KNN algorithm, this tutorial works with the package class: If you don’t have this package yet, you can quickly and easily do so by typing the following line of code: Remember the nerd tip: if you’re not sure if you have this package, you can run the following command to find out!

For example, if your dataset has just two attributes, X and Y, and X has values that range from 1 to 1000, while Y has values that only go from 1 to 100, then Y’s influence on the distance function will usually be overpowered by X’s influence.

The Iris data set doesn’t need to be normalized: the Sepal.Length attribute has values that go from 4.3 to 7.9 and Sepal.Width contains values from 2 to 4.4, while Petal.Length’s values range from 1 to 6.9 and Petal.Width goes from 0.1 to 2.5.

You can then use this argument in another command, where you put the results of the normalization in a data frame through after the function lapply() returns a list of the same length as the data set that you give in.

Tip: to more thoroughly illustrate the effect of normalization on the data set, compare the following result to the summary of the Iris data set that was given in step two.

In practice, the division of your data set into a test and a training sets is disjoint: the most common splitting choice is to take 2/3 of your original data set as the training set, while the 1/3 that remains will compose the test set.

Note that the replace argument is set to TRUE: this means that you assign a 1 or a 2 to a certain row and then reset the vector of 2 to its original state.

Remember that you want your training set to be 2/3 of your original data set: that is why you assign “1” with a probability of 0.67 and the “2”s with a probability of 0.33 to the 150 sample rows.

You can then use the sample that is stored in the variable ind to define your training and test sets: Note that, in addition to the 2/3 and 1/3 proportions specified above, you don’t take into account all attributes to form the training and test sets.

You therefore need to store the class labels in factor vectors and divide them over the training and test sets: After all these preparation steps, you have made sure that all your known (training) data is stored.

An easy way to do these two steps is by using the knn() function, which uses the Euclidian distance measure in order to find the k-nearest neighbours to your new, unknown instance.

In case of classification, the data point with the highest score wins the battle and the unknown instance receives the label of that winning data point.

To build your classifier, you need to take the knn() function and simply add some arguments to it, just like in this example: You store into iris_pred the knn() function that takes as arguments the training set, the test set, the train labels and the amount of neighbours you want to find with this algorithm.

For a more abstract view, you can just compare the results of iris_pred to the test labels that you had defined earlier: You see that the model makes reasonably accurate predictions, with the exception of one wrong classification in row 29, where “Versicolor” was predicted while the test label is “Virginica”.

That’s where the caret package can come in handy: it’s short for “Classification and Regression Training” and offers everything you need to know to solve supervised machine learning problems: it provides a uniform interface to a ton of machine learning algorithms.

You just have to change the method argument, just like in this example: Now that you have trained your model, it’s time to predict the labels of the test set that you have just made and evaluate how the model has done on your data: Additionally, you can try to do the same test as before, to examine the effect of preprocessing, such as scaling and centering, on your model.

Hierarchical cluster analysis on famous data sets - enhanced with the dendextend package

We may ask ourselves how many different results we could get if we would use different clustering algorithms (hclust has 8 different algorithms implemented).

For the purpose of this analysis, we will create all 8 hclust objects, and chain them together into a single dendlist object (which, as the name implies, can hold a bunch of dendrograms together for the purpose of further analysis).

From the above figure, we can easily see that most clustering methods yield very similar results, except for the complete method (the default method in hclust), which yields a correlation measure of around 0.6.

We can see that the correlations are not so strong, indicating a behavior that is dependent on some items which are very distant from one another having an influence on the pearson’s correlation more than that of the spearman’s correlation.

To further explore the similarity and difference between the alternative clustering algorithms, we can turn to using the tanglegram function (which works for either two dendrograms, or a dendlist).

We have 39 sub-trees that are identical between the two dendrograms: What we can learn from this is that actually the two algorithms seem to give quite different results in the high resolution (higher cuts).

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