如何解决错误'UnicodeDecodeError：'charmap'编解码器无法解码位置36188的字节0x9d：字符映射到<undefined>'

如何解决如何解决错误'UnicodeDecodeError：'charmap'编解码器无法解码位置36188的字节0x9d：字符映射到<undefined>'

我正在训练AI使用TensorFlow 1.14和python 2.6.7编写一本书。每当我运行培训python代码时，我都会收到错误消息UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 36188: character maps to <undefined>，我已经重新安装了TensorFlow和python并搜索了论坛以尝试找到答案。追溯将我带到encodings文件夹中的一个名为cp1252.py的文件

我正在运行的代码是

import numpy as np
import tensorflow as tf

import argparse
import time
import os
from six.moves import cPickle

from utils import TextLoader
from model import Model

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--data_dir',type=str,default='data/tinyshakespeare',help='data directory containing input.txt')
    parser.add_argument('--input_encoding',default=None,help='character encoding of input.txt,from https://docs.python.org/3/library/codecs.html#standard-encodings')
    parser.add_argument('--log_dir',default='logs',help='directory containing tensorboard logs')
    parser.add_argument('--save_dir',default='save',help='directory to store checkpointed models')
    parser.add_argument('--rnn_size',type=int,default=256,help='size of RNN hidden state')
    parser.add_argument('--num_layers',default=2,help='number of layers in the RNN')
    parser.add_argument('--model',default='lstm',help='rnn,gru,or lstm')
    parser.add_argument('--batch_size',default=50,help='minibatch size')
    parser.add_argument('--seq_length',default=25,help='RNN sequence length')
    parser.add_argument('--num_epochs',help='number of epochs')
    parser.add_argument('--save_every',default=1000,help='save frequency')
    parser.add_argument('--grad_clip',type=float,default=5.,help='clip gradients at this value')
    parser.add_argument('--learning_rate',default=0.002,help='learning rate')
    parser.add_argument('--decay_rate',default=0.97,help='decay rate for rmsprop')
    parser.add_argument('--gpu_mem',default=0.666,help='%% of gpu memory to be allocated to this process. Default is 66.6%%')
    parser.add_argument('--init_from',help="""continue training from saved model at this path. Path must contain files saved by previous training process:
                            'config.pkl'        : configuration;
                            'words_vocab.pkl'   : vocabulary definitions;
                            'checkpoint'        : paths to model file(s) (created by tf).
                                                  Note: this file contains absolute paths,be careful when moving files around;
                            'model.ckpt-*'      : file(s) with model definition (created by tf)
                        """)
    args = parser.parse_args()
    train(args)

def train(args):
    data_loader = TextLoader(args.data_dir,args.batch_size,args.seq_length,args.input_encoding)
    args.vocab_size = data_loader.vocab_size

    # check compatibility if training is continued from previously saved model
    if args.init_from is not None:
        # check if all necessary files exist
        assert os.path.isdir(args.init_from)," %s must be a path" % args.init_from
        assert os.path.isfile(os.path.join(args.init_from,"config.pkl")),"config.pkl file does not exist in path %s"%args.init_from
        assert os.path.isfile(os.path.join(args.init_from,"words_vocab.pkl")),"words_vocab.pkl.pkl file does not exist in path %s" % args.init_from
        ckpt = tf.train.get_checkpoint_state(args.init_from)
        assert ckpt,"No checkpoint found"
        assert ckpt.model_checkpoint_path,"No model path found in checkpoint"

        # open old config and check if models are compatible
        with open(os.path.join(args.init_from,'config.pkl'),'rb') as f:
            saved_model_args = cPickle.load(f)
        need_be_same=["model","rnn_size","num_layers","seq_length"]
        for checkme in need_be_same:
            assert vars(saved_model_args)[checkme]==vars(args)[checkme],"Command line argument and saved model disagree on '%s' "%checkme

        # open saved vocab/dict and check if vocabs/dicts are compatible
        with open(os.path.join(args.init_from,'words_vocab.pkl'),'rb') as f:
            saved_words,saved_vocab = cPickle.load(f)
        assert saved_words==data_loader.words,"Data and loaded model disagree on word set!"
        assert saved_vocab==data_loader.vocab,"Data and loaded model disagree on dictionary mappings!"

    with open(os.path.join(args.save_dir,'wb') as f:
        cPickle.dump(args,f)
    with open(os.path.join(args.save_dir,'wb') as f:
        cPickle.dump((data_loader.words,data_loader.vocab),f)

    model = Model(args)

    merged = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter(args.log_dir)
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_mem)

    with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
        train_writer.add_graph(sess.graph)
        tf.global_variables_initializer().run()
        saver = tf.train.Saver(tf.global_variables())
        # restore model
        if args.init_from is not None:
            saver.restore(sess,ckpt.model_checkpoint_path)
        for e in range(model.epoch_pointer.eval(),args.num_epochs):
            sess.run(tf.assign(model.lr,args.learning_rate * (args.decay_rate ** e)))
            data_loader.reset_batch_pointer()
            state = sess.run(model.initial_state)
            speed = 0
            if args.init_from is None:
                assign_op = model.epoch_pointer.assign(e)
                sess.run(assign_op)
            if args.init_from is not None:
                data_loader.pointer = model.batch_pointer.eval()
                args.init_from = None
            for b in range(data_loader.pointer,data_loader.num_batches):
                start = time.time()
                x,y = data_loader.next_batch()
                feed = {model.input_data: x,model.targets: y,model.initial_state: state,model.batch_time: speed}
                summary,train_loss,state,_,_ = sess.run([merged,model.cost,model.final_state,model.train_op,model.inc_batch_pointer_op],feed)
                train_writer.add_summary(summary,e * data_loader.num_batches + b)
                speed = time.time() - start
                if (e * data_loader.num_batches + b) % args.batch_size == 0:
                    print("{}/{} (epoch {}),train_loss = {:.3f},time/batch = {:.3f}" \
                        .format(e * data_loader.num_batches + b,args.num_epochs * data_loader.num_batches,e,speed))
                if (e * data_loader.num_batches + b) % args.save_every == 0 \
                        or (e==args.num_epochs-1 and b == data_loader.num_batches-1): # save for the last result
                    checkpoint_path = os.path.join(args.save_dir,'model.ckpt')
                    saver.save(sess,checkpoint_path,global_step = e * data_loader.num_batches + b)
                    print("model saved to {}".format(checkpoint_path))
        train_writer.close()

if __name__ == '__main__':
    main()

任何帮助将不胜感激 我可以提供所需的任何信息 编辑：我的回溯是

  File "train.py",line 134,in <module>
    main()
  File "train.py",line 54,in main
    train(args)
  File "train.py",line 57,in train
    data_loader = TextLoader(args.data_dir,args.input_encoding)
  File "C:\Users\Josh\Desktop\word-rnn-tensorflow-master\utils.py",line 23,in __init__
    self.preprocess(input_file,vocab_file,tensor_file,encoding)
  File "C:\Users\Josh\Desktop\word-rnn-tensorflow-master\utils.py",line 66,in preprocess
    data = f.read()
  File "C:\Users\Josh\anaconda3\envs\tensorenviron\lib\encodings\cp1252.py",in decode
    return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 36188: character maps to <undefined>```

解决方法

因此，事实证明文本文件中存在一个奇怪的字符。我要做的就是用正确的符号替换所有奇怪的符号。感谢他的帮助！