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)
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