I’m learning how to implement LSTM using Keras API, and got a question regard how I can handle two different cases; y = x and y = x
For example, the following code builds a simple LSTM in Keras:
inputs = tf.random.normal([32, 10, 8]) lstm = tf.keras.layers.LSTM(4, return_sequences=True, return_state=True) whole_seq_output, final_memory_state, final_carry_state = lstm(inputs)
When doing training for name entity task y equals to x, but when doing inferencing then y = x. But it seems like there is no argument/parameter specifying these cases when initializing LSTM in Keras.
Is it that these things are being handled internally all by Keras API by itself or is there something I’m missing?