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运行我的代码需要帮助时显示以下错误发生异常，请使用％tb查看完整的追溯

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强调文本此代码与以下链接中提供的matlab文件集成运行

https://github.com/zhongyuanzhao/dl_ofdm

在替换了上面链接中提供的python代码中的某些库之后，该代码开始运行并生成混淆矩阵，但是几分钟后，标题上提到的错误出现了。  我尝试运行的python代码在下面的代码部分中提供


#! /usr/bin/python
from numpy import genfromtxt
#import tensorflow as tf
import numpy as np
import os
import time
import scipy.io as sio
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

flags = tf.app.flags
flags.DEFINE_string('data_dir','./mat/','directory .mat data is saved')
flags.DEFINE_string('save_dir','./model/','directory where model graph and weights are saved')
#flags.DEFINE_string('data_dir','../mat/','directory .mat data is saved')
#flags.DEFINE_string('save_dir','../model/','directory where model graph and weights are saved')
flags.DEFINE_integer('nbits',1,'bits per symbol')
flags.DEFINE_integer('msg_length',102400,'Message Length of Dataset')
flags.DEFINE_integer('batch_size',512,'')
flags.DEFINE_integer('max_epoch_num',1000,'')
flags.DEFINE_integer('seed','random seed')
flags.DEFINE_integer('nfft',64,'Dropout rate TX conv block')
flags.DEFINE_integer('nsymbol',8,'number of OFDM symbols per frame')
flags.DEFINE_integer('npilot','number of pilot cells per OFDM symbol')
flags.DEFINE_integer('nguard','number of guard bands per OFDM symbol (without DC)')
flags.DEFINE_integer('nfilter',80,'number of filters')
flags.DEFINE_float('snR',3.0,'Signal to Noise Ratio')
flags.DEFINE_integer('early_stop',100,'number of epoches for early stop')
flags.DEFINE_string('channel','AWGN','AWGN or Rayleigh Channel')
flags.DEFINE_boolean('cp',False,'If include cyclic prefix')
flags.DEFINE_boolean('load_model','Set True if run a test')
flags.DEFINE_string('token','OFDM','Name of model to be saved')
flaGS = flags.flaGS


class testflag:
    def __init__(self):
        self.token='OFDM'
        self.split=1.0
        self.load_model=True
        self.cp = False
        self.early_stop = 100
        self.snR = 3.0
        self.nfilter=80
        self.nguard = 8
        self.npilot=8
        self.nsymbol=8
        self.nfft=64
        self.seed=1
        self.max_epoch_num=1000
        self.batch_size=512
        self.msg_length=102400
        self.nbits=1
        self.save_dir='./model/'


class ofdm_tx:
    def __init__(self,sflag):
        self.K=sflag.nfft
        self.CP = sflag.nfft//4
        self.DC = 2
        self.G = sflag.nguard
        self.P = sflag.npilot
        self.nSymbol = sflag.nsymbol
        self.nfft=64
        self.nbits=sflag.nbits


def load_model_np(path,session):
    graph = session.graph
    saver = tf.train.import_meta_graph(path + '.meta',clear_devices=True)

    saver.restore(session,path)
    print("Load Model: %s" % (path))

    y = graph.get_tensor_by_name('bits_in:0')
    x = graph.get_tensor_by_name('tx_ofdm:0')
    iq_receiver = graph.get_tensor_by_name('input:0')
    outputs = graph.get_tensor_by_name('output:0')
    total_loss = graph.get_tensor_by_name('cost:0')
    ber = graph.get_tensor_by_name('log_ber:0')
    berlin = graph.get_tensor_by_name('linear_ber:0')
    conf_matrix = graph.get_tensor_by_name('conf_matrix:0')
    power_tx = graph.get_tensor_by_name('tx_power:0')
    noise_pwr = graph.get_tensor_by_name('noise_power:0')
    iq_rx = graph.get_tensor_by_name('iq_rx:0')
    iq_tx = graph.get_tensor_by_name('iq_tx:0')
    ce_mean = graph.get_tensor_by_name('ce_mean:0')
    snR = graph.get_tensor_by_name('snR:0')
    return y,x,iq_receiver,outputs,total_loss,ber,berlin,conf_matrix,power_tx,noise_pwr,iq_rx,iq_tx,ce_mean,snR


def test_model(path_prefix_min,ofdmobj,session):
    y,snR = load_model_np(path_prefix_min,session)
    print("Final Test snR: -10-30 dB")
    mod_names = ['BPSK','QPSK','8QAM','16QAM']
    nfft = ofdmobj.K
    nbits = ofdmobj.nbits
    npilot = ofdmobj.P # last carrier as pilot
    nguard = ofdmobj.G
    nsymbol = ofdmobj.nSymbol
    DC = ofdmobj.DC
    frame_size = nfft - nguard - npilot - DC
    msg_length = 16000
    n_fr = msg_length//nsymbol
    n_sc = nfft + ofdmobj.CP
    for snr_t in range(-10,30):
        mat_name = '%sofdm_awgn_%s_%ddB.mat'%(flaGS.data_dir,mod_names[nbits-1],snr_t)
        matfile = sio.loadmat(mat_name)
        iq_matlab = matfile['y']
        txbits_matlab = matfile['txbits']
        test_xs = np.transpose(iq_matlab,axes=[1,0])
        test_xs = np.reshape(test_xs,[msg_length//nsymbol,nsymbol,-1])
        xs_real = np.reshape(np.real(test_xs),[n_fr,n_sc,1])
        xs_imag = np.reshape(np.imag(test_xs),1])
        test_xs = np.concatenate([xs_real,xs_imag],axis=-1)
        test_ys = np.reshape(txbits_matlab,[msg_length,frame_size,nbits])
        snr_test = 100 * np.ones((msg_length//nsymbol,1)) # noise is already included in test_xs from matlab
        confmax,berl,pwr_tx,pwr_noise,test_loss,tx_sample,rx_sample = session.run([conf_matrix,iq_rx],{x: test_xs,y: test_ys,snR:snr_test})

        print("snR: %.2f,ber: %.8f,Loss: %f"%(snr_t,test_loss))
        print("Test Confusion Matrix: ")
        print(str(confmax))
    session.close()


def main(argv):
    # Get current working directory
    cwd = os.getcwd()
    token = "OFDM_Dense3"
    # From Previous Test,max_epoch_num = 120~200 is ok
    batchsize = 512
    n_filter = flaGS.nfilter # 128

    # save_dir = "./ofdm_saved/"
    # save_dir = "./ofdm_np_dc/"
    save_dir = flaGS.save_dir
    data_dir = flaGS.data_dir
    ebno = 3.0
    for nbits in range(1,5):
        snr = float(ebno*nbits)
        max_epoch_num = 1200 * nbits
        cond = "%dmod"%(nbits)
        for cp in ['False','True']:
            tflag = testflag()
            chan = 'AWGN'
            # chan = 'EPA'
            tflag.batch_size=batchsize
            tflag.cp=cp
            tflag.channe=chan
            tflag.save_dir=save_dir
            tflag.early_stop=100
            tflag.nfilter=n_filter
            tflag.max_epoch_num=max_epoch_num
            tflag.snR=snr
            tflag.nbits=nbits

            token1 = "%s_%s_snr%d_cp%s" % (token,cond,int(snr),cp)
            tflag.token = token1

            path_prefix_min = '%s%s'%(flaGS.save_dir,token1)
            ofdmobj = ofdm_tx(tflag)
            session = tf.Session()
            session.run(tf.global_variables_initializer())
            test_model(path_prefix_min,session)
            session.close()
            tf.reset_default_graph()
            time.sleep(2)



if __name__ == "__main__":
    tf.app.run()

使用％tb查看完整的追溯之后，我收到以下错误消息。

％tb

回溯（最近通话最近一次）：

    File "<ipython-input-1-062c2c99acc1>",line 1,in <module>
runfile('C:/Users/User/Desktop/OFDM/dl_ofdm-master/test_ofdm_cdnn_awgn.py',wdir='C:/Users/User/Desktop/OFDM/dl_ofdm-master')

文件“ C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ spyder_kernels \ customize \ spydercustomize.py”，行文件中的第827行     execfile（文件名，命名空间）

exec文件中的第110行，文件“ C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ spyder_kernels \ customize \ spydercustomize.py”     exec（compile（f.read（），文件名，'exec'），命名空间）

文件“ C：/用户/用户/桌面/OFDM/dl_ofdm-master/test_ofdm_cdnn_awgn.py”，第172行，在     tf.app.run（）

文件“ C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ tensorflow_core \ python \ platform \ app.py”，第40行，正在运行     _run（main = main，argv = argv，flags_parser = _parse_flags_tolerate_undef）

文件“ C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ absl \ app.py”，行299，正在运行     _run_main（main，args）

_run_main中的文件“ C：\ Users \ User \ Anaconda3 \ lib \ site-packages \ absl \ app.py”，第250行     sys.exit（main（argv））

SystemExit

hhxxttxsj 回答：运行我的代码需要帮助时显示以下错误发生异常，请使用％tb查看完整的追溯
暂时没有好的解决方案，如果你有好的解决方案，请发邮件至：iooj@foxmail.com
runtime-errortensorflowtracebackwindows
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