tmp_scaling.py 文件源码

def get_f1(scale):
    global best_f1
#    idx = np.random.choice(np.arange(len(crop)), 10000 if len(target)>10000 else len(target), replace=False)
    idx = np.arange(len(target))
#    pred = cnn.predict_proba((crop[idx])/scale, 1024, 0)
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        res = keras_utils.test_data_cnn_rnn((crop[idx])/scale, target, groups, cnn, rnn, verbose=0, only_lstm = True, cropsize=0)
        f1 = res[3]
        acc= res[2]
#        f1_score(np.argmax(target[idx],1), np.argmax(pred,1), average='macro')
    print(res[2],f1)
    return -acc