tdlm_test.py 文件源码

def compute_dt_dist(docs, labels, tags, model, max_len, batch_size, pad_id, idxvocab, output_file):
    #generate batches
    num_batches = int(math.ceil(float(len(docs)) / batch_size))
    dt_dist = []
    t = []
    combined = []
    docid = 0
    for i in xrange(num_batches):
        x, _, _, t, s = get_batch_doc(docs, labels, tags, i, max_len, cf.tag_len, batch_size, pad_id)
        attention, mean_topic = sess.run([model.attention, model.mean_topic], {model.doc: x, model.tag: t})
        dt_dist.extend(attention[:s])

        if debug:
            for si in xrange(s):
                d = x[si]
                print "\n\nDoc", docid, "=", " ".join([idxvocab[item] for item in d if (item != pad_id)])
                sorted_dist = matutils.argsort(attention[si], reverse=True)
                for ti in sorted_dist:
                    print "Topic", ti, "=", attention[si][ti]
                docid += 1

    np.save(open(output_file, "w"), dt_dist)