Simple LSTM failing due undefined input dimension

Question

I'm trying to build an LSTM model for classifying the ATIS dataset.

From a sentence of undefined size N, I generate a context window word embedding matrix. That's what I need to feed on my model, but I can't figure out how to make it so.

When I define my input layer as:

def build_lstm(input_var=None):
    l_in = lasagne.layers.InputLayer(shape=(None, 1, None, None), input_var=input_var)
    l_hid = l_lstm = lasagne.layers.LSTMLayer(l_in, num_units=300)
    l_out = lasagne.layers.DenseLayer(l_hid, num_units=127, nonlinearity=lasagne.nonlinearities.softmax)

return l_out

I get:

TypeError: unsupported operand type(s) for *: 'NoneType' and 'NoneType'

While if I define the input shape in the l_in declaration it works, for example:

l_in = lasagne.layers.InputLayer(shape=(None, 1, 30, 30), input_var=input_var)

The point is that each sentence has a different size, thus resulting in a context window word embedding matrix of different shape. What can I do?

score 0 · Answer 1 · answered Mar 07 '16 at 00:02

Because of the way Lasagne/Theano handle initialization of Tensors, you can't simply specify (None, 1, None, None). As you've already found out, you need to give a size. In fact, as seen in this example, LSTMLayers seem to expect an input of size (batch size, SEQ_LENGTH, num_features).

As I understand it, your options are to:

Choose the maximum sequence length and then pad your batches with 0's (or bucket your batches into batches with all sentences in the batch having the same sequence length)
have the batch size be 1, and then you can vary the sequence length to be the size of your current sentence (i.e. your batch)

Some other relevant links:

https://groups.google.com/forum/#!msg/lasagne-users/9nMD5VJPLXA/sNzqxON_DwAJ https://www.reddit.com/r/MachineLearning/comments/3dqdqr/keras_lstm_limitations/

Simple LSTM failing due undefined input dimension

1 Answers1