import tensorflow as tf import numpy as np import sys from datahelper import DataHelper VOCAB_SIZE=10000 EMBEDDING_SIZE=1 LEARNING_RATE=1e-3 MINI_BATCH_SIZE=256 NORMALIZE_LAYER=0 data_helper = DataHelper(_voc_size = VOCAB_SIZE) data_helper.load_train_ins_and_process("data/train.50_51.ins") data_helper.load_eval_ins("data/eval.52.ins") print "data loaded" def eval_auc(eval_res, eval_label): sorted_res = np.argsort(eval_res, axis=0) m = 0 n = 0 rank = 0 for k in range(sorted_res.shape[0]): idx = sorted_res[k][0] if eval_label[idx][0] == 1: m += 1 rank += k + 1
import tensorflow as tf import numpy as np import sys from datahelper import DataHelper VOCAB_SIZE = 10000 EMBEDDING_SIZE = 1 LEARNING_RATE = 1e-3 MINI_BATCH_SIZE = 256 NORMALIZE_LAYER = 0 data_helper = DataHelper(_voc_size=VOCAB_SIZE) data_helper.load_train_ins_and_process("data/train.50_51.ins") data_helper.load_eval_ins("data/eval.52.ins") print "data loaded" def eval_auc(eval_res, eval_label): sorted_res = np.argsort(eval_res, axis=0) m = 0 n = 0 rank = 0 for k in range(sorted_res.shape[0]): idx = sorted_res[k][0] if eval_label[idx][0] == 1: m += 1