beam search for Keras RNN

beamsearch.py

# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,
               k=1, maxsample=400, use_unk=False, oov=oov, empty=empty, eos=eos):
    """return k samples (beams) and their NLL scores, each sample is a sequence of labels,
    all samples starts with an `empty` label and end with `eos` or truncated to length of `maxsample`.
    You need to supply `predict` which returns the label probability of each sample.
    `use_unk` allow usage of `oov` (out-of-vocabulary) label in samples
    """
    
    dead_k = 0 # samples that reached eos
    dead_samples = []
    dead_scores = []
    live_k = 1 # samples that did not yet reached eos
    live_samples = [[empty]]
    live_scores = [0]

    while live_k and dead_k < k:
        # for every possible live sample calc prob for every possible label 
        probs = predict(live_samples, empty=empty)

        # total score for every sample is sum of -log of word prb
        cand_scores = np.array(live_scores)[:,None] - np.log(probs)
        if not use_unk and oov is not None:
            cand_scores[:,oov] = 1e20
        cand_flat = cand_scores.flatten()

        # find the best (lowest) scores we have from all possible samples and new words
        ranks_flat = cand_flat.argsort()[:(k-dead_k)]
        live_scores = cand_flat[ranks_flat]

        # append the new words to their appropriate live sample
        voc_size = probs.shape[1]
        live_samples = [live_samples[r//voc_size]+[r%voc_size] for r in ranks_flat]

        # live samples that should be dead are...
        zombie = [s[-1] == eos or len(s) >= maxsample for s in live_samples]
        
        # add zombies to the dead
        dead_samples += [s for s,z in zip(live_samples,zombie) if z]  # remove first label == empty
        dead_scores += [s for s,z in zip(live_scores,zombie) if z]
        dead_k = len(dead_samples)
        # remove zombies from the living 
        live_samples = [s for s,z in zip(live_samples,zombie) if not z]
        live_scores = [s for s,z in zip(live_scores,zombie) if not z]
        live_k = len(live_samples)

    return dead_samples + live_samples, dead_scores + live_scores   

@jeetp465 is your issue resolved ?

@jeetp465, @andersonzhu, According to my understanding, Beam search is not the part of model definition. It is The way how we decode the output of LSTM(RNN). So, BeamSearch is used where you are generating words from the predicted output received from LSTM. So The output of full_model.predict() will be passed to BeamSearch to get output captions.