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generate_attention.py

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+#-*- coding: utf-8 -*-
+# 64.72% #
+import tensorflow as tf
+import pandas as pd
+import numpy as np
+import os, h5py, sys, argparse
+import ipdb
+import time
+import math
+import cv2
+import codecs, json
+# from tensorflow.python.ops import rnn_cell
+from sklearn.metrics import average_precision_score
+import pdb
+import spacy
+from random import seed
+import itertools
+from numpy import linalg as LA
+
+tf.reset_default_graph()
+random_seed = 320
+np.random.seed(random_seed)
+tf.set_random_seed(random_seed)
+
+triple = json.load(open('/home/liangjic/BMVC/VQA_20170301/vqa_raw_train.json'))
+print triple[210716]
+for xid in xrange(len(triple)):
+	if xid % 4 == 0 and triple[xid]['ans'] == 0:
+		print xid
+		break
+	if xid % 4 > 0 and triple[xid]['ans'] == 1:
+		print xid
+		break