How to use Bidirectional RNN and Conv1D in keras when shapes are not matching?

Question

I am brand new to Deep-Learning so I\'m reading though Deep Learning with Keras by Antonio Gulli and learning a lot. I want to start using some of the concepts. I

Answer 1

You don't need to restructure anything at all to get the output of a Conv1D layer into an LSTM layer.

So, the problem is simply the presence of the Flatten layer, which destroys the shape.

These are the shapes used by Conv1D and LSTM:

• Conv1D: (batch, length, channels)
• LSTM: (batch, timeSteps, features)

Length is the same as timeSteps, and channels is the same as features.

Using the Bidirectional wrapper won't change a thing either. It will only duplicate your output features.

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Classifying.

If you're going to classify the entire sequence as a whole, your last LSTM must use return_sequences=False. (Or you may use some flatten + dense instead after)

If you're going to classify each step of the sequence, all your LSTMs should have return_sequences=True. You should not flatten the data after them.