This tutorial is a beginner’s guide to TensorFlow embeddings with an example. It will cover the basic concepts of embeddings, how to create them in TensorFlow, and how to use them.
For more information check out our video:
In this tutorial, you will learn how to use the Embedding layer in TensorFlow. You will first need to install TensorFlow (either via pip or conda). For this tutorial, we will be using TensorFlow version 2.2.0.
The embedding layer is used to embed input data in a high-dimensional space. The learned embedding can be used for various downstream tasks such as dimensionality reduction, text classification, and so on.
In this tutorial, you will learn how to:
– Load data into TensorFlow
– Define an Embedding layer
– Train the model
– Evaluate the model
What is TensorFlow?
TensorFlow is an open source software library for numerical computation using data flow graphs. In other words, the library allows developers to create data flow graphs – structures that describe how data moves through a graph. The nodes in the graph represent mathematical operations, while the edges represent the data, or tensors, that flow between them.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
What is an Embedding?
An embedding is a mapping of a discrete set of variables to a continuous vector space. In the simplest case, the variables could be words in some vocabulary, and the vectors could be their coordinate representations in a Euclidean space. In this case, the Euclidean distance between any two vectors would encode some notion of semantic similarity: words with similar meanings would be mapped to nearby points in the space.
Why Use an Embedding?
Embeddings are a way of representing data in a vector space. By doing this, we can make use of mathematical properties of vector spaces to our advantage. For example, by representing data points as vectors, we can easily calculate the distance between them. This is useful for tasks like clustering, where we want to group together similar data points.
How to Embed in TensorFlow?
Embedding in TensorFlow is a technique used to represent data in a low-dimensional vector space. The vectors can be used to represent words, phrases, or even entire documents. The benefits of using embedding include the ability to capture relationships between similar data points and the ability to reduce the dimensionality of data while preserving important information.
Creating the Embedding
TensorFlow’s embedding operation is defined in the tf.nn.embedding_lookup() function. This function takes two arguments:
The first argument is the tensor that we want to embed. For this tutorial, we’ll use a tensor of shape  , which corresponds to five vectors of eight dimensions each.
The second argument is the tf.Variable that contains our embedding matrix. In this case, it’s a matrix of shape [10, 8] , which means it contains 10 vectors of eight dimensions each.
Specifying the Input
Before we can train our model, we need to specify the input. In this case, we’re going to use a text file containing a list of names. Each name will be its own line in the file, like this:
We’ll also need to specify what kind of data our model will be training on. In this case, we’re going to use embeddings, which are essentially a way of representing data in a vector format. For our purposes, we’ll be using 128-dimensional embeddings.
Training the Model
We’re now going to train the model.
First, we need to define the variables that we’re going to use. The `inputs` variable will be used for the input data, which in this case is simply a list of integers. The `labels` variable will be used for the labels (i.e. the correct answers), which are also just a list of integers.
We also need to define the embedding size, which is the number of dimensions that we want to use for our embedding vector. In this case, we’ll use 64 dimensions.
Next, we create a `tf.placeholder` for each of our variables (`inputs` and `labels`). A `tf.placeholder` is simply a placeholder for a value that we’ll input later. We need to do this because we can’t input our data directly into our model – TensorFlow needs to know what sort of data we’re going to be passing in so that it can create the correct tensors.
After that, we create an embedding matrix with shape `[vocabulary_size, embedding_size]`. This will be our lookup table – each row will correspond to a word in our vocabulary, and each column will correspond to a dimension in our embedding vector. We initialize this matrix with random values using `tf.random_uniform`.
Finally, we get to the interesting part – defining our model! We do this by creating a `nnlm` Model object from TensorFlow’s `contrib.legacy_seq2seq` library. This object takes care of everything for us: creating the placeholders, initializing the embedding matrix, etc. All we need to do is tell it how many words our vocabulary contains (i sent one additional argument specifying that we’re using 64-dimensional embeddings). Your code should look something like this:
Evaluating the Model
After we have trained our model, it is time to evaluate it. To do so, we’ll need to run our model on the test dataset that we’ve withheld. This will give us a sense of how well our model performs on data it’s never seen before.
We can evaluate the model by feeding it batches of data from the test set and keeping track of accuracy. accuracy is simply the percentage of examples that our model predicted correctly. We’ll also keep track of loss, which is a metric telling us how far off our predictions are. Ideally, we want both accuracy and loss to be as low as possible.
Let’s set up our evaluation:
# Set up evaluation graph
correct_prediction = tf.equal(tf.argmax(model, 1), tf.argmax(y_, 1)) # Check if correct prediction
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # Calculate accuracy
loss = tf.reduce_sum(-y_ * tf.log(model)) / BATCH_SIZE # Compute loss function per batch
In this tutorial, we’ve seen how to create an embedding in TensorFlow and how to use that embedding in a simple machine learning model. We’ve also seen how to visualize the embedding to understand what it has learned.
Hopefully, this has given you a good understanding of how embeddings work and how to use them in your own models.
Keyword: TensorFlow Embedding Example – A Beginner’s Tutorial