LSTM fully connected architecture

Hi everyone, First, I would like to express my gratitude to all people who work daily trying to improve Keras software and its documentation. Thank you guys 😉

After reading many posts trying to sort out all my questions…I still have some doubts related to LSTM recurrent networks, so, I hope you can help me.

Input shape: (nb_samples, timesteps, input_dim). So, I have 11200 nb_samples. Each nb_samples contains 3000 timesteps and finally, each timesteps contains 22 values. Therefore, this is my input shape (11200, 3000, 22).

Output: every nb_samples must be classified in one class (‘0’ or ‘1’).

Goal: classify every nb_samples in one of the two possibles classes (‘0’ or ‘1’) using a LSTM fully-connected network.

Architecture to follow:

In the following posts I found very useful information related to my problem: #2673 and #2496. However, I still have many doubts:

1. As far as I know, a LSTM layer at the beginning of the model is not fully connected as @carlthome and @fchollet explained in #2673.
2. As the goal is to classify each nb_samples to one class (‘0’ or ‘1’), TimeDistributed(Dense(...)) shouldn’t be used due to, as far as I know, this layer provides an output per timestep and what I want is to classify one nb_samples on the whole to ‘0’ or ‘1’ classes.
3. In this easy architecture there is only one LSTM layer and therefore, the use of return_sequence doesn’t matter. However, in case of having two LSTM layers, should return_secuence = true or false? I think in my model it should be return_sequence=true as it was explained in #2496 but 'm not pretty sure about it.

Let’s start with the first approach of the model (although I know it is wrong).

timesteps = 3000
input_dim = 22 

model = Sequential()
model.add(LSTM(22, input_shape=(timesteps, data_dim)))
model.add(Dense(1, activation='sigmoid'))

Does anyone can help me to build my model and solve all my questions? Thank you very much in advanced!

[carlthome]

No, that doesn’t help.

[aisopos]

OK, will go through these, thanks again 😃