I am new to tensorflow and trying to look at different examples of tensorflow to understand it better.
Now I have seen this line being used in many tensorflow examples without mentioning of any specific embedding algorithm being used for getting the words embeddings.
embeddings = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))
embed = tf.nn.embedding_lookup(embeddings, input_data)
Here are some examples:
I understand that the first line will initialize the embedding of the words by random distribution but will the embedding vectors further be trained in the model to give more accurate representation of the words (and change the initial random values to more accurate numbers) and if yes what is the actual method being used when there is no mention of any obvious embedding methods such as using word2vec and glove inside the code (or feeding the pre_tained vectors of these methods instead of random numbers in the beginning)?
Yes, those embeddings are trained further just like weights and biases otherwise representing words with some random values wouldn't make any sense. Those embeddings are updated while training like you would update a weight matrix, that is, by using optimization methods like Gradient Descent or Adam optimizer, etc.
When we use pre-trained embeddings like word2vec, they're already trained on very large datasets and are quite accurate representations of words already hence, they don't need any further training. If you are asking how those are trained, there are two main training algorithms that can be used to learn the embedding from the text; they are Continuous Bag of Words (CBOW) and Skip Grams. Explaining them completely is not possible here but I would suggest taking help from Google. This article might get you started.