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Reputation: 82

Stack two LSTM layers in Keras dimension mismatch

I want to make an LSTM neural net using Keras which gets as input some length of four features and predicts 10 following values. And I can't manage to set proper input dimensions. X_train is an array of shape (34,5,4) (repeated observations, the sequence of observations, features) y_train is an array of shape(34,10). I can't manage to satisfy the required dimensions.

Any ideas what am I doing wrong?

X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 4))
model.add(LSTM(30, dropout=0.2, batch_size=window_size))
model.add(LSTM(10, activation=None))
model.compile(optimizer='adam',loss='mse')
model.fit(X_train,y_train,epochs= epochs,validation_split=0.2,shuffle=True)

Upvotes: 1

Views: 303

Answers (1)

Ankish Bansal
Ankish Bansal

Reputation: 1902

If you are stacking two lstm layer, you need to use return_sequence for first layer, which return output for each time step, which will be feed into 2nd lstm layer.

Here is explained example, by which you can solve your problem.

Upvotes: 2

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