How to stack multiple lstm in keras?

deep-learning, keras, keras-layer, lstm, tensorflow

Solution

You need to add `return_sequences=True` to the first layer so that its output tensor has `ndim=3` (i.e. batch size, timesteps, hidden state).

Please see the following example:

# expected input data shape: (batch_size, timesteps, data_dim)
model = Sequential()
model.add(LSTM(32, return_sequences=True,
               input_shape=(timesteps, data_dim)))  # returns a sequence of vectors of dimension 32
model.add(LSTM(32, return_sequences=True))  # returns a sequence of vectors of dimension 32
model.add(LSTM(32))  # return a single vector of dimension 32
model.add(Dense(10, activation='softmax'))

From: https://keras.io/getting-started/sequential-model-guide/ (search for "stacked lstm")

Problem

I am using deep learning library keras and trying to stack multiple LSTM with no luck. Below is my code ``` model = Sequential() model.add(LSTM(100,input_shape =(time_steps,vector_size))) model.add(LSTM(100)) ``` The above code returns error in the third line `Exception: Input 0 is incompatible with layer lstm_28: expected ndim=3, found ndim=2 ` The input X is a tensor of shape (100,250,50). I am running keras on tensorflow backend

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