session_conf.gpu_options.allow_growth = True
# sess = tf.Session(config=session_conf)
sess = tf.InteractiveSession()
model = models.setup(params)
model.build_graph()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for i in range(params.epochs):
for data in reader.getTrain(
overlap_feature=False): #model=model,sess=sess,
# for data in data_helper.getBatch48008(train,alphabet,args.batch_size):
_, summary, step, loss, accuracy, score12, score13, see = model.train(
sess, data)
time_str = datetime.datetime.now().isoformat()
print(
"{}: step {}, loss {:g}, acc {:g} ,positive {:g},negative {:g}"
.format(time_str, step, loss, accuracy, np.mean(score12),
np.mean(score13)))
# logger.info("{}: step {}, loss {:g}, acc {:g} ,positive {:g},negative {:g}".format(time_str, step, loss, accuracy,np.mean(score12),np.mean(score13)))
test_datas = reader.getTest()
predicted_test = predict(model, sess, test_datas, reader.datas["test"])
map_mrr_test = evaluation.evaluationBypandas(reader.datas["test"],
predicted_test)
# logger.info('map_mrr test' +str(map_mrr_test))
print('epoch ' + str(i) + ' map_mrr test' + str(map_mrr_test))
def evaluate(self, predicted, mode="test", acc=False):
return evaluation.evaluationBypandas(self.datas[mode],
predicted,
acc=acc)