rdwords.sort(key = lambda x : x[1], reverse = True)
outcnt = 0
for i in range(len(rdwords)):
if owords[rdwords[i][0]] > 20:
print(rdwords[i], owords[rdwords[i][0]])
outcnt += 1
if outcnt > 20:
break;
avelenth = float(total_lenth) / float(len(test_data))
avedis = float(total_dis) / float(len(test_data))
#print "average length", avelenth
#print "average distilled length", avedis
return float(acc) / len(test_data)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config = config) as sess:
#model
critic = LSTM_CriticNetwork(sess, args.dim, args.optimizer, args.lr, args.tau, args.grained, args.maxlenth, args.dropout, word_vector)
actor = ActorNetwork(sess, args.dim, args.optimizer, args.lr, args.tau)
#print variables
for item in tf.trainable_variables():
print (item.name, item.get_shape())
saver = tf.train.Saver()
saver.restore(sess, "checkpoints/best816")
print(test(sess, actor, critic, dev_data))
else:
actions, action_pos = sampling_random(lenth, paction)
if len(actions) != args.maxlenth:
print(inputs)
#predict
out = critic.predict_target([inputs], [actions], [action_pos], [lenth], [len(action_pos)])
if np.argmax(out) == np.argmax(solution):
acc += 1
return float(acc) / len(test_data)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config = config) as sess:
#model
critic = LSTM_CriticNetwork(sess, args.dim, args.optimizer, args.lr, args.tau, args.grained, args.attention, args.maxlenth, args.dropout, word_vector)
actor = ActorNetwork(sess, args.dim, args.optimizer, args.lr, args.tau, critic.get_num_trainable_vars())
state_size = critic.state_size
#print variables
for item in tf.trainable_variables():
print((item.name, item.get_shape()))
saver = tf.train.Saver()
#LSTM pretrain
if args.RLpretrain != '':
pass
elif args.LSTMpretrain == '':
sess.run(tf.global_variables_initializer())
for i in range(0,2):
