I try to understand what the difference between this model describde here, the following one:
from keras.layers import Input, LSTM, RepeatVector
from keras.models import Model
inputs = Input(shape=(timesteps, input_dim))
encoded = LSTM(latent_dim)(inputs)
decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(input_dim, return_sequences=True)(decoded)
sequence_autoencoder = Model(inputs, decoded)
encoder = Model(inputs, encoded)
and the sequence to sequence model described here is second describion
What is the difference ? The first one has the RepeatVector while the second does not have that? Is the first model not taking the decoders hidden state as inital state for the prediction ?
Are there a paper describing the first and second one ?
In the model using RepeatVector, they're not using any kind of fancy prediction, nor dealing with states. They're letting the model do everything internally and the RepeatVector is used to transform a (batch, latent_dim) vector (which is not a sequence) into a (batch, timesteps, latent_dim) (which is now a proper sequence).
Now, in the other model, without RepeatVector, the secret lies in this additional function:
def decode_sequence(input_seq):
# Encode the input as state vectors.
states_value = encoder_model.predict(input_seq)
# Generate empty target sequence of length 1.
target_seq = np.zeros((1, 1, num_decoder_tokens))
# Populate the first character of target sequence with the start character.
target_seq[0, 0, target_token_index['\t']] = 1.
# Sampling loop for a batch of sequences
# (to simplify, here we assume a batch of size 1).
stop_condition = False
decoded_sentence = ''
while not stop_condition:
output_tokens, h, c = decoder_model.predict([target_seq] + states_value)
# Sample a token
sampled_token_index = np.argmax(output_tokens[0, -1, :])
sampled_char = reverse_target_char_index[sampled_token_index]
decoded_sentence += sampled_char
# Exit condition: either hit max length
# or find stop character.
if (sampled_char == '\n' or len(decoded_sentence) > max_decoder_seq_length):
stop_condition = True
# Update the target sequence (of length 1).
target_seq = np.zeros((1, 1, num_decoder_tokens))
target_seq[0, 0, sampled_token_index] = 1.
# Update states
states_value = [h, c]
return decoded_sentence
This runs a "loop" based on a stop_condition for creating the time steps one by one. (The advantage of this is making sentences without a fixed length).
It also explicitly takes the states generated in each step (in order to keep the proper connection between each individual step).