Predicting next word using the language model tensorflow example

Question

The tensorflow tutorial on language model allows to compute the probability of sentences :

probabilities = tf.nn.softmax(logits)

in the comments below it also specifies a way of predicting the next word instead of probabilities but does not specify how this can be done. So how to output a word instead of probability using this example?

lstm = rnn_cell.BasicLSTMCell(lstm_size)
# Initial state of the LSTM memory.
state = tf.zeros([batch_size, lstm.state_size])

loss = 0.0
for current_batch_of_words in words_in_dataset:
    # The value of state is updated after processing each batch of words.
    output, state = lstm(current_batch_of_words, state)

    # The LSTM output can be used to make next word predictions
    logits = tf.matmul(output, softmax_w) + softmax_b
    probabilities = tf.nn.softmax(logits)
    loss += loss_function(probabilities, target_words)

Answer 1

Your output is a TensorFlow list and it is possible to get its max argument (the predicted most probable class) with a TensorFlow function. This is normally the list that contains the next word's probabilities.

At "Evaluate the Model" from this page, your output list is y in the following example:

First we'll figure out where we predicted the correct label. tf.argmax is an extremely useful function which gives you the index of the highest entry in a tensor along some axis. For example, tf.argmax(y,1) is the label our model thinks is most likely for each input, while tf.argmax(y_,1) is the true label. We can use tf.equal to check if our prediction matches the truth. correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

Another approach that is different is to have pre-vectorized (embedded/encoded) words. You could vectorize your words (therefore embed them) with Word2vec to accelerate learning, you might want to take a look at this. Each word could be represented as a point in a 300 dimensions space of meaning, and you could find automatically the "N words" closest to the predicted point in space at the output of the network. In that case, the argmax way to proceed does not work anymore and you could probably compare on cosine similarity with the words you truly wanted to compare to, but for that I am not sure actually how does this could cause numerical instabilities. In that case y will not represent words as features, but word embeddings over a dimensionality of, let's say, 100 to 2000 in size according to different models. You could Google something like this for more info: "man woman queen word addition word2vec" to understand the subject of embeddings more.

Note: when I talk about word2vec here, it is about using an external pre-trained word2vec model to help your training to only have pre-embedded inputs and create embedding outputs. Those outputs' corresponding words can be re-figured out by word2vec to find the corresponding similar top predicted words.

Notice that the approach I suggest is not exact since it would be only useful to know if we predict EXACTLY the word that we wanted to predict. For a more soft approach, it would be possible to use ROUGE or BLEU metrics for evaluating your model in case you use sentences or something longer than a word.