How is the embedding layer trained in Keras Embedding layer?
(say using tensorflow backend, meaning is it similar to word2vec, glove or fasttext)
Assume we do not use a pretrained embedding.
Solved – How the embedding layer is trained in Keras Embedding layer
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I think this means that pretrained embeddings (e.g. using Word2Vec) are not used.
Correct
Are there any variables/weights (e.g. adjusted during back prop) associated with the word_embedding layer in this RNN? Or is it purely a static lookup table to randomly assigned variables?
First option: there are any variables/weights (e.g. adjusted during back prop) associated with the word_embedding layer in this RNN.
Do random word embeddings have any advantages over just adding a regular hidden layer with the same dimensions as the randomly initialized word embedding layer?
It is the equivalent: adding a random word embedding layer means adding a regular hidden layer.
In fact, the output vectors are not computed from the input using any mathematical operation. Instead, each input integer is used as the index to access a table that contains all possible vectors. That is the reason why you need to specify the size of the vocabulary as the first argument (so the table can be initialized).
The most common application of this layer is for text processing. Let's see a simple example. Our training set consists only of two phrases:
Hope to see you soon
Nice to see you again
So we can encode these phrases by assigning each word a unique integer number (by order of appearance in our training dataset for example). Then our phrases could be rewritten as:
[0, 1, 2, 3, 4]
[5, 1, 2, 3, 6]
Now imagine we want to train a network whose first layer is an embedding layer. In this case, we should initialize it as follows:
Embedding(7, 2, input_length=5)
The first argument (7) is the number of distinct words in the training set. The second argument (2) indicates the size of the embedding vectors. The input_length argument, of course, determines the size of each input sequence.
Once the network has been trained, we can get the weights of the embedding layer, which in this case will be of size (7, 2) and can be thought as the table used to map integers to embedding vectors:
+------------+------------+
| index | Embedding |
+------------+------------+
| 0 | [1.2, 3.1] |
| 1 | [0.1, 4.2] |
| 2 | [1.0, 3.1] |
| 3 | [0.3, 2.1] |
| 4 | [2.2, 1.4] |
| 5 | [0.7, 1.7] |
| 6 | [4.1, 2.0] |
+------------+------------+
So according to these embeddings, our second training phrase will be represented as:
[[0.7, 1.7], [0.1, 4.2], [1.0, 3.1], [0.3, 2.1], [4.1, 2.0]]
It might seem counterintuitive at first, but the underlying automatic differentiation engines (e.g., Tensorflow or Theano) manage to optimize these vectors associated with each input integer just like any other parameter of your model.
For an intuition of how this table lookup is implemented as a mathematical operation which can be handled by the automatic differentiation engines, consider the embeddings table from the example as a (7, 2) matrix. Then, for a given word, you create a one-hot vector based on its index and multiply it by the embeddings matrix, effectively replicating a lookup. For instance, for the word "soon" the index is 4, and the one-hot vector is [0, 0, 0, 0, 1, 0, 0]. If you multiply this (1, 7) matrix by the (7, 2) embeddings matrix you get the desired two-dimensional embedding, which in this case is [2.2, 1.4].
It is also interesting to use the embeddings learned by other methods/people in different domains (see https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html) as done in [1].
[1] López-Sánchez, D., Herrero, J. R., Arrieta, A. G., & Corchado, J. M. Hybridizing metric learning and case-based reasoning for adaptable clickbait detection. Applied Intelligence, 1-16.
Best Answer
Embedding layers in Keras are trained just like any other layer in your network architecture: they are tuned to minimize the loss function by using the selected optimization method. The major difference with other layers, is that their output is not a mathematical function of the input. Instead the input to the layer is used to index a table with the embedding vectors [1]. However, the underlying automatic differentiation engine has no problem to optimize these vectors to minimize the loss function...
So, you cannot say that the Embedding layer in Keras is doing the same as word2vec [2]. Remember that word2vec refers to a very specific network setup which tries to learn an embedding which captures the semantics of words. With Keras's embedding layer, you are just trying to minimize the loss function, so if for instance you are working with a sentiment classification problem, the learned embedding will probably not capture complete word semantics but just their emotional polarity...
For example, the following image taken from [3] shows the embedding of three sentences with a Keras Embedding layer trained from scratch as part of a supervised network designed to detect clickbait headlines (left) and pre-trained word2vec embeddings (right). As you can see, word2vec embeddings reflect the semantic similarity between phrases b) and c). Conversely, the embeddings generated by Keras's Embedding layer might be useful for classification, but do not capture the semantical similarity of b) and c).
This explains why when you have a limited amount of training samples, it might be a good idea to initialize your Embedding layer with word2vec weights, so at least your model recognizes that "Alps" and "Himalaya" are similar things, even if they don't both occur in sentences of your training dataset.
[1] How does Keras 'Embedding' layer work?
[2] https://www.tensorflow.org/tutorials/word2vec
[3] https://link.springer.com/article/10.1007/s10489-017-1109-7
NOTE: Actually, the image shows the activations of the layer after the Embedding layer, but for the purpose of this example it does not matter... See more details in [3]