如何修復錯誤 'UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 36188: character maps to<undefined> '</undefined>

Question

我正在訓練 AI 使用 TensorFlow 1.14 和 python 2.6.7 寫書。 Whenever I run my training python code, I get the error message UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 36188: character maps to <undefined> I have reinstalled TensorFlow and python as well as searched the forums to try and找到答案。 回溯將我帶到 encodings 文件夾中名為 cp1252.py 的文件

我正在運行的代碼是

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', type=str, default=None,
                       help='character encoding of input.txt, from https://docs.python.org/3/library/codecs.html#standard-encodings')
    parser.add_argument('--log_dir', type=str, default='logs',
                       help='directory containing tensorboard logs')
    parser.add_argument('--save_dir', type=str, 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', type=int, default=2,
                       help='number of layers in the RNN')
    parser.add_argument('--model', type=str, default='lstm',
                       help='rnn, gru, or lstm')
    parser.add_argument('--batch_size', type=int, default=50,
                       help='minibatch size')
    parser.add_argument('--seq_length', type=int, default=25,
                       help='RNN sequence length')
    parser.add_argument('--num_epochs', type=int, default=50,
                       help='number of epochs')
    parser.add_argument('--save_every', type=int, 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', type=float, default=0.002,
                       help='learning rate')
    parser.add_argument('--decay_rate', type=float, default=0.97,
                       help='decay rate for rmsprop')
    parser.add_argument('--gpu_mem', type=float, default=0.666,
                       help='%% of gpu memory to be allocated to this process. Default is 66.6%%')
    parser.add_argument('--init_from', type=str, default=None,
                       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, 'config.pkl'), 'wb') as f:
        cPickle.dump(args, f)
    with open(os.path.join(args.save_dir, 'words_vocab.pkl'), '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, train_loss, 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()

任何幫助將不勝感激我可以提供任何需要的信息編輯：我的追溯是

  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.batch_size, args.seq_length, 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", line 23, 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>```

Answer 1

所以事實證明文本文件中有一個奇怪的字符。 我所要做的就是用正確的符號替換所有奇怪的符號。 感謝他的所有幫助！