response = Sequential()
response.add(Merge([match, input_encoder_c], mode="sum"))
// output: (samples, story_maxlen, query_maxlen)
response.add(Permute((2, 1))) // output: (samples, query_maxlen, story_maxlen)
// concatenate the match vector with the question vector,
// and do logistic regression on top
After Change
// placeholders
input_sequence = Input((story_maxlen,))
question = Input((query_maxlen,))
// encoders
// embed the input sequence into a sequence of vectors
input_encoder_m = Sequential()
input_encoder_m.add(Embedding(input_dim=vocab_size,
output_dim=64))
input_encoder_m.add(Dropout(0.3))
// output: (samples, story_maxlen, embedding_dim)
// embed the input into a sequence of vectors of size query_maxlen
input_encoder_c = Sequential()
input_encoder_c.add(Embedding(input_dim=vocab_size,
output_dim=query_maxlen))
input_encoder_c.add(Dropout(0.3))
// output: (samples, story_maxlen, query_maxlen)
// embed the question into a sequence of vectors
question_encoder = Sequential()
question_encoder.add(Embedding(input_dim=vocab_size,
output_dim=64,
input_length=query_maxlen))
question_encoder.add(Dropout(0.3))
// output: (samples, query_maxlen, embedding_dim)
// encode input sequence and questions (which are indices) to sequences of dense vectors
input_encoded_m = input_encoder_m(input_sequence)
input_encoded_c = input_encoder_c(input_sequence)
question_encoded = question_encoder(question)
// compute a "match" between the first input vector sequence
// and the question vector sequence
match = dot([input_encoded_m, question_encoded], axes=(2, 2)) // (samples, story_maxlen, query_maxlen)
match = Activation("softmax")(match)
// add the match matrix with the second input vector sequence
response = add([match, input_encoded_c]) // (samples, story_maxlen, query_maxlen)
response = Permute((2, 1))(response) // (samples, query_maxlen, story_maxlen)
// concatenate the match matrix with the question vector sequence
answer = concatenate([response, question_encoded])
// the original paper uses a matrix multiplication for this reduction step.
// we choose to use a RNN instead.
answer = LSTM(32)(answer) // (samples, 32)
// one regularization layer -- more would probably be needed.
answer = Dropout(0.3)(answer)answer = Dense(vocab_size)(answer) // (samples, vocab_size)
// we output a probability distribution over the vocabulary
answer = Activation("softmax")(answer)
// build the final model
model = Model([input_sequence, question], answer)
model.compile(optimizer="rmsprop", loss="categorical_crossentropy",
metrics=["accuracy"])
// train
