I am having trouble around certain aspects of the Keras implementation of LSTM. This is a description of my problem:
I am trying to train a model for word correctness prediction. My model has two types of inputs:
e.g.
input_1 = ['we', 'have', 'two', 'review']
input_2 = [
[1.25, 0.01, 0.000787, 5.235, 0.0, 0.002091],
[ 0.0787, 0.02342, 5.4595, 0.002091, 0.003477, 0.0],
[0.371533, 0.529893, 0.371533, 0.6, 0.0194156, 0.003297],
[0.471533, 0.635, 0.458, 0.7, 0.0194156, 0.0287]
]
gives output = [1, 1, 2, 1]
As each sentence in my training set has different length, I should zero-pad all of my sentences such that they all have the same length.
My question is how about the second input, should I do padding! and how? as they are vectors.
Model Architecture :
input1 = Input(shape=(seq_length,), dtype='int32')
emb = Embedding(input_dim=num_words, output_dim = num_dimension,
input_length=seq_length, weights=[embeddings], mask_zero=True,trainable=False)(input_layer)
input2 = Input(shape=(seq_length,6 ))
x = keras.layers.concatenate([emb, input2],axis=2)
lstm = LSTM(64, return_sequences=True)(x)
ackwards = LSTM(128, return_sequences=True, go_backwards=True)(x)
common = merge([forwards, backwards], mode='concat', concat_axis=-1)
out = TimeDistributed(Dense(no_targets, activation='softmax'))(lstm)
You are on the right track and yes you would need to pad your second input with zero rows to match the sentence lengths. Essentially it would look like this:
# Input 1
X1 = [[12, 34, 3], [6, 7, 0]] # where numbers are word indices and 0 is padding
# Input 2
X2 = [[[1.23,...,2.4], [1.24, ...], [0.6, ...]], [[3.25, ...], [2.4, ...], [0,0,0,0,0]]]
# So the padded words get zero feature vectors as well and the shapes match
But fear not, because you concatenate emb with input2 the mask_zero=True also gets propagated to the concatenated vector so the LSTM actually ignores the padding from second input as well.