试图运行一个聊天机器人，并给了我这个错误。您知道我该怎么做吗？我认为错误出在我头上。 :(每一个帮助将不胜感激。尝试在我的终端中使用python3 train.py运行代码。我没有对图像做任何事情，我在做一个聊天机器人，所以不知道为什么我得到这个图像错误。
Traceback (most recent call last):
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow.py", line 41, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: dlopen(/usr/local/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so, 10): Library not loaded: @rpath/libcublas.8.0.dylib
Referenced from: /usr/local/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so
Reason: image not found
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 8, in <module>
from nmt import nmt
File "/Users/Downloads/nmt-chatbot/nmt/nmt/nmt.py", line 26, in <module>
import tensorflow as tf
File "/usr/local/lib/python3.6/site-packages/tensorflow/__init__.py", line 24, in <module>
from tensorflow.python import *
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/__init__.py", line 51, in <module>
from tensorflow.python import pywrap_tensorflow
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow.py", line 52, in <module>
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow.py", line 41, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: dlopen(/usr/local/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so, 10): Library not loaded: @rpath/libcublas.8.0.dylib
Referenced from: /usr/local/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so
Reason: image not found
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/install_sources#common_installation_problems
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
这是火车。py
import sys
import os
import argparse
from setup.settings import hparams, preprocessing
import math
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/nmt")
from nmt import nmt
import tensorflow as tf
import colorama
from threading import Thread
from setup.custom_summary import custom_summary
colorama.init()
def train():
print('\n\n{}Training model...{}\n'.format(colorama.Fore.GREEN, colorama.Fore.RESET))
# Custom epoch training and decaying
if preprocessing['epochs'] is not None:
# Load corpus size, calculate number of steps
with open('{}/corpus_size'.format(preprocessing['train_folder']), 'r') as f:
corpus_size = int(f.read())
# Load current train progress
try:
with open('{}epochs_passed'.format(hparams['out_dir']), 'r') as f:
initial_epoch = int(f.read())
except:
initial_epoch = 0
# Iterate thru epochs
for epoch, learning_rate in enumerate(preprocessing['epochs']):
# Check if model already passed that epoch
if epoch < initial_epoch:
print('{}Epoch: {}, learning rate: {} - already passed{}'.format(colorama.Fore.GREEN, epoch + 1, learning_rate, colorama.Fore.RESET))
continue
# Calculate new number of training steps - up to the end of current epoch
num_train_steps = math.ceil((epoch + 1) * corpus_size / (hparams['batch_size'] if 'batch_size' in hparams else 128))
print("\n{}Epoch: {}, steps per epoch: {}, epoch ends at {} steps, learning rate: {} - training{}\n".format(
colorama.Fore.GREEN,
epoch + 1,
math.ceil(corpus_size / (hparams['batch_size'] if 'batch_size' in hparams else 128)),
num_train_steps,
learning_rate,
colorama.Fore.RESET
))
# Override hparams
hparams['num_train_steps'] = num_train_steps
hparams['learning_rate'] = learning_rate
hparams['override_loaded_hparams'] = True
# Run TensorFlow threaded (exits on finished training, but we want to train more)
thread = Thread(target=nmt_train)
thread.start()
thread.join()
# Save epoch progress
with open('{}epochs_passed'.format(hparams['out_dir']), 'w') as f:
f.write(str(epoch + 1))
# Standard training
else:
nmt_train()
print('\n\n{}Training finished{}\n'.format(colorama.Fore.GREEN, colorama.Fore.RESET))
def nmt_train():
# Modified autorun from nmt.py (bottom of the file)
# We want to use original argument parser (for validation, etc)
nmt_parser = argparse.ArgumentParser()
nmt.add_arguments(nmt_parser)
# But we have to hack settings from our config in there instead of commandline options
nmt.FLAGS, unparsed = nmt_parser.parse_known_args(['--'+k+'='+str(v) for k,v in hparams.items()])
# Add custom summary function (hook)
nmt.summary_callback = custom_summary
# And now we can run TF with modified arguments
tf.app.run(main=nmt.main, argv=[os.getcwd() + '\nmt\nmt\nmt.py'] + unparsed)
train()