ValueError: Exception encountered when calling RNN.call() - Undefined shapes are not supported about keras HOT 3 CLOSED

Hi @fchollet, thanks for the quick fix but I can't find it at master branch HEAD.

So model_a and model_b are actually equivalent in the first snippet? And they will behave differently (even if they have the same number of parameters) If I do something like:

from keras.layers import RNN, LSTM, LSTMCell

inputs = keras.Input(shape=(5, 10))
first_lstm_layer_out, *cell_states = LSTM(10, return_sequences=True, return_state=True)(inputs)
second_lstm_layer_out = LSTM(10)(first_lstm_layer_out, initial_state=cell_states)
model_a = keras.Model(inputs, second_lstm_layer_out)
model_a.summary()

inputs = keras.Input(shape=(5, 10))
stacked_lstm_outputs = RNN([LSTMCell(10), LSTMCell(10)])(inputs)
model_b = keras.Model(inputs, stacked_lstm_outputs)
model_b.summary()

from keras.

From what I understand the main difference should be that model_b returns the states of both LSTM layers while model_a returns only the final ones (as expected).

Yes, that's right. The second model would return 4 state tensors (2 per cell).

But in the stacked implementation of model_b are the states of the first layer used to initialize the states of the second one?

No, states are initialized at zero by each cell. To get a non-zero state you would have to pass the initial state when calling the layer.

Is this a Keras issue?

Yes, that's actually a bug. I've fixed it at HEAD. Check that it works for you.

Note that since your layer returns (outputs, [cell_1_state_0, cell_0_state_1], [cell_1_state_1, cell_1_state_2]) you cannot use it with a Sequential model. Instead you could do something like:

inputs = keras.Input(shape=(5, 10))
outputs, cell_1_states, cell_2_states = keras.layers.RNN(
    [keras.layers.LSTMCell(10), keras.layers.LSTMCell(10)],
    return_state=True,
)(inputs)
model = keras.Model(inputs, [outputs] + cell_1_states + cell_2_states)
model.summary()

from keras.

Yes, there's only one gotcha: Functional model inputs/outputs must be flat structured, and here stacked_lstm_outputs is nested. You have to flatten it (like in my example above). If you want to keep it structured, write a subclassed model.

from keras.