Prompt Title: LSTM

Certainly! Here's an example of a Keras model with an embedding layer, followed by an LSTM layer, and finally a bidirectional LSTM layer that receives the embedding of each timestep as well as the output of the last two timesteps from the LSTM layer:

pythonCopy code
from tensorflow.keras.models import Model
from tensorflow.keras.layers import LSTM, Embedding, Bidirectional, Input

# Define input shape
input_shape = (sequence_length,)  # Replace with your desired sequence length

# Define input layer
inputs = Input(shape=input_shape)

# Embedding layer
embedding_dim = 100  # Replace with your desired embedding dimension
vocab_size = 10000  # Replace with your vocabulary size
embedding = Embedding(vocab_size, embedding_dim)(inputs)

# LSTM layer
lstm_units = 64  # Replace with the number of LSTM units
lstm = LSTM(lstm_units, return_sequences=True)(embedding)

# BiLSTM layer
bilstm_units = 32  # Replace with the number of BiLSTM units
bilstm = Bidirectional(LSTM(bilstm_units))(lstm)

# Define the model
model = Model(inputs=inputs, outputs=bilstm)

In this example, we start by defining the input shape and creating an input layer. Then, we apply an embedding layer to the input layer, which learns and maps the input tokens to dense vectors of the specified embedding dimension.

Next, we add an LSTM layer after the embedding layer, with return_sequences=True to obtain the sequence of outputs for each timestep. These outputs will be fed into the subsequent bidirectional LSTM layer.

Finally, we define the bidirectional LSTM layer that receives the output from the previous LSTM layer. By default, the bidirectional LSTM layer will consider all timesteps of the input sequence. The resulting output of the bidirectional LSTM layer is the final output of the model.

Note that you'll need to replace sequence_length, embedding_dim, vocab_size, lstm_units, and bilstm_units with the appropriate values for your specific model.