Image Classification with TFLearn and Python

In today’s post we are going to walk through how to build a flexible image classifier using TFLearn and Python. For those not familiar with TFLearn, it is a wrapper around the very popular Tensorflow library from Google. Building a image classifier with TFLearn is relatively simple and today we are going to walk through how to build your own image classifier.


We are going to need to import a number of different libraries in order to build our classifier. For users on Windows the easiest way to install the Scipy library is to use the pre-compiled wheel which can be found here. Once you have installed all the required imports, we can start building our ImageClassify class.

Initializing Our Class

When initializing our class, we are going to need to know a few pieces of information. We are going to need a list of class names. These are the names of the different objects that our classifier is going to classify. We also need to pass in an image size, the classifier will automatically resize our images into a square image of the specified size. So, if we pass in a value of 100, our classifier will end up resizing our images to be 100×100 pixels in size.

Generally, the larger the image size the better the classification we will end up with. This is provided that your images are larger than the specified value. It should be warned that using larger images will increase the time taken to train the algorithm. We store this value in self.image_size.

We also pass in default values for our learning rate and test split. The learning rate dictates how quickly the machine learning algorithm will discover new features. As a default value 0.001 tends to work well. The test split, defines what percentage of samples we will use to validate our model against. Again, using around ten percent of samples for your test size works pretty well.

We also create empty lists which will end up holding our image data and their respective labels

Labeling An Image

Our extract label function takes an image and extracts the label from the image. We begin by creating an array of zeroes, with a zero for each class to be trained. We then split the file name of the image. Our extract label functions expects images in the following format “class.number.png”. Using this format allows us to extract the class name directly from the image. We then look up the index of the class label and set that value in our array of zeros to a 1. Before returning the array itself.

Processing Images

Our process image function first calls our label function. We then read the image using skimage’s io.imread function. We then resize this image to the size specified when we initialized the class. We then append the image data and the labels to self.image_data and self.labels respectively.

Processing images is simply involves us using our process image function on every single image we provide to our image classification class.

Building Our Model

Our build model function simply builds us a convolutional net model, using the parameters we defined when initializing our class. Explaining the working’s of the net are probably beyond the scope of this post. But I will just note that creating our model like this allows our classifier to be used with images of any size and datasets with any number of classes. Creating a build model function also makes it easier to load and predict using pre-trained models.

Training Our Model

Our train_model function, takes a model name, epochs and a batch size parameter. The epochs parameter determines the number of times the model will be run over the entirety of the dataset. The batch size determines the number of samples to be run through the model at once. Generally, the more epochs the more accurate the model will be. Though too many epochs may mean that your model over fits the dataset and you end up with rather inaccurate predictions when you use the model to make predictions. If accuracy hits 100% and loss goes to 0, this is a very strong indication that you have over fit.

We first begin by creating X, y variables using the self.image_data and self.labels variables. We then use our self.test_split value to split the dataset up into training and test sets. We then call the build model function. We then call the fit method on model using both the train and test sets for validation purposes.

Once we have finished training the model. We save the model using the passed in model name and set self.model to be equivalent to our newly trained model.

Loading A Pre-Trained Model & Predicting Images

We can define a very simple function to load a model. This will be useful when we need to predict images sometime after we have trained a model. We can load a model by simply passing in the model’s name.

We then need another function to take an image and transform it to something we can use in our prediction function. This is much like our process image function, with the exception that we have no need to label the image.

Our predict image function takes a path to an image file. We call our _image_to_array function this data can then be fed straight into the model. Our model will then output an array of probabilities. We can line these up with the classes which we provided to the Image Classify class. We then pull out the most probable label, before returning this and the list of probabilities.

Example Usage: Training A Model

Example Usage: Making  A Prediction With Already Trained Model

Full Code & Example Dataset

The full code and an example data set can be found on my Github here. The Github also contains another image classification model which makes use of Google’s Googlenet model. This model is very highly accurate but takes a considerable amount of time to train and is likely to need to be run for a greater number of epics.

Text Classification with Python & NLTK

Machine learning frameworks such as Tensorflow and Keras are currently all the range, and you can find several tutorials demonstrating the usage of CNN (Convolutional Neural Nets) to classify text. Often this can be overkill and, in this post we are going to show you how to classify text using Python’s NLTK library. The NLTK (Natural Language Toolkit) provides Python users with a number of different tools to deal with text content and provides some basic classification capabilities.

Input Data

In the example, I’m using a set of 10,000 tweets which have been classified as being positive or negative. Our classifier is going to take import in CSV format, with the left column containing the tweet and the right column containing the label. An example of the data can be found below:

Using your own data is very simple and simply requires that your left column contains your text document, while the column on the right contains the correct label. Allowing our classifier to classify a wide range of documents with labels of your choosing. The data used for this example can be downloaded here.

The Bag of Words Approach

We are going to use a bag of words approach. Simply put, we just take a certain number of the most common words found throughout our data set and then for each document we check whether the document contains this word. The bag of words approach is conceptually simple and doesn’t require us to pad documents to ensure that every document in our sample set is the same length. However, the bag of words approach tends to be less accurate than using a word embedding approach. By simply checking whether a document contains a certain set of words we miss out on a lot of valuable information – including the position of the words in a said document. Despite this we can easily train a classifier which can achieve 80%+ accuracy.

Initialising our class and reading our CSV file

Our CSV classifier is going to take several arguments. Firstly, we pass the name of our CSV file. Then as optional parameters we pass featureset_size and a test ratio. By default, our classifier will use the most 1,000 common words found in our dataset to create our feature set. Additionally, we will test the accuracy of our classifier against 10% of the items contained in our data set. We then initialise a few variables which will be used later by our classifier.

We then come on to reading our CSV file. We simply iterate through each line and before splitting by our line by commas. The text after our last comma is the documents label, while everything to the left is the document in question. By applying a regex to our document, we produce a list of words contained in the said document. In the example, I used a very simple regex to pull out the words, but it is possible to replace this with a more complex tokenizer. For each word in the document we append this to the list of words. This will allow us to determine the frequency that words occur in our dataset. We also place the list of words found in our document into the variable where we store all the documents found in our dataset.

Extracting Word Features

We then write our functions for handling generating our feature set. Here, we use NLTK’s Frequency Dist class to store the frequency in which different words where found throughout the dataset. We then iterate through all of the words in the document, creating a new record should we have not seen the word before and incrementing the count should it have already been found. We then limit our bag of words to be equal to the feature set size we passed when we initialised the class.

Now we have got a list of the most frequently found words, we can write a function to generate features for each of the documents in our dataset. As we are using a bag of words approach we are only interested in whether the document contains each word contained in the 1,000 most frequent words. If we find the word we return True, otherwise we return False. Eventually, we get a dictionary of 1,000 features which will be used train the classifier.


We start by shuffling the documents. Some algorithms and classifiers can be sensitive to the order of data. This makes important to shuffle our data before training. We then use our feature set function within a list comprehension which returns us a list of tuples containing our feature set dictionary and the documents label. We then calculate where to split our data into training and test sets. The test set allows us to check how the classifier performs against an unseen dataset. We can then pass our data set to nltk’s naïve bayes classifier. The actual training may take some time and will take longer the larger the dataset used. We then check the classifiers accuracy against both the training and test set. In all likelihood the classifier will perform significantly better against the training set.

Classifying New Documents

Once we have trained a classifier we can then write a function to classify new documents. If we have not already loaded our CSV file and generated the word features, we will have to do this before classifying our new document. We then simply make generate a new set of features for this document before passing this to our classifier class. We will then pass this to our classifier and call the classify method with the feature set. The function will return the string of the predicted label.

Saving and Loading Model

Rather than training the model every time we want to classify the sentence, it would make sense to save the model. We can write two simple functions to allow us to reuse our model whenever we want. The save functions simply saves our classifier and feature words to objects to files, which can then be reloaded by our load model function.


Algorithm Train Test
Naive Bayes Classifier (NLTK) 84.09% 72.89%
BernouliNB (Sklearn) 83.93% 79.78%
MultinomiaNB (Sklearn) 84.58% 74.67%
LogisticRegression (Sklearn) 89.05% 75.33%
SGDClassifier (Sklearn) 81.23% 69.32%

The algorithm performs relatively well against our example data. Being able to correctly classify whether a Tweet is positive or negative around 72% of the time. NLTK gives it’s users the option to replace the standard Naive Bayes Classifier with a number of other classifiers found in the Sci-kit learn package. I ran the same test swapping in these classifiers for the Naive Bayes Classifier, and a number of these classifiers significantly outperformed the standard naive classifier. As you can see the BernouliNB model performed particularly well, correctly classifying documents around 80% of the time.

The accuracy of the classifier could further be improved by using something called an ensemble classifier. To build an ensemble classifier we would simply build several models using different classifiers and then classify new documents against all of these classifiers. We could then select the answer which was provided by the majority of our classifiers (hard voting classifier).  Such a classifier would likely outperform just using on of the above classifiers. The full code below provides a function that allows you to try out other Sklearn classifiers.

Example Usage

The class is pretty easy to use. The above code outlines all of the steps required to train a classifier and classify an unseen sentence. More usage examples and the full code can be found on Github here.

Full Code