如何在不同的conda环境下设置不同的keras后端

Question

如何在不同的conda环境中设置不同的keras后端？因为在特定的环境中，如果我在keras.json中将后端更改为tensorflow，那么在另一个python环境中，keras后端也将是tensorflow。我的文档中只有一个keras.json。在

Answer 1

在水蟒的不同环境中使用不同的keras后端-“env1”和“env2”

• 激活第一个环境“env1”
• 使用默认后端从python导入keras（例如，如果无法加载tensorflow，请在该环境中安装tensorflow）
• 在~文件夹中，将创建一个“.keras”文件夹，其中包含keras.json文件文件
• 对于其他环境，请将“.keras”文件夹的副本创建为“.keras1”
• 改变keras.json文件根据要求在该文件夹中的文件（“后端”字段）
• 要在“env2”中使用该配置，请转到'~/anaconda3/envs/env2/lib/pythonx.x/site packages/keras/backend'，然后编辑\uuinit_uu.py文件
• 进行标记为的更改
• 您将能够在env1和env2中导入具有不同后端的keras

from __future__ import absolute_import
 from __future__ import print_function
 import os
 import json
 import sys
 import importlib
 from .common import epsilon
 from .common import floatx
 from .common import set_epsilon
 from .common import set_floatx
 from .common import cast_to_floatx
 from .common import image_data_format
 from .common import set_image_data_format
 
 # Set Keras base dir path given KERAS_HOME env variable, if applicable.
 # Otherwise either ~/.keras or /tmp.
 if 'KERAS_HOME' in os.environ:
 _keras_dir = os.environ.get('KERAS_HOME')
 else:
 _keras_base_dir = os.path.expanduser('~')
 if not os.access(_keras_base_dir, os.W_OK):
 _keras_base_dir = '/tmp'
 _keras_dir = os.path.join(_keras_base_dir, '.keras1')##
 
 # Default backend: TensorFlow.
 _BACKEND = 'tensorflow'
 
 # Attempt to read Keras config file.
 _config_path = os.path.expanduser(os.path.join(_keras_dir, 'keras.json'))
 if os.path.exists(_config_path):
 try:
 with open(_config_path) as f:
 _config = json.load(f)
 except ValueError:
 _config = {}
 _floatx = _config.get('floatx', floatx())
 assert _floatx in {'float16', 'float32', 'float64'}
 _epsilon = _config.get('epsilon', epsilon())
 assert isinstance(_epsilon, float)
 _backend = _config.get('backend', _BACKEND)
 _image_data_format = _config.get('image_data_format',
 image_data_format())
 assert _image_data_format in {'channels_last', 'channels_first'}
 
 set_floatx(_floatx)
 set_epsilon(_epsilon)
 set_image_data_format(_image_data_format)
 _BACKEND = _backend
 
 # Save config file, if possible.
 if not os.path.exists(_keras_dir):
 try:
 os.makedirs(_keras_dir)
 except OSError:
 # Except permission denied and potential race conditions
 # in multi-threaded environments.
 pass
 
 if not os.path.exists(_config_path):
 _config = {
 'floatx': floatx(),
 'epsilon': epsilon(),
 'backend': _BACKEND,
 'image_data_format': image_data_format()
 }
 try:
 with open(_config_path, 'w') as f:
 f.write(json.dumps(_config, indent=4))
 except IOError:
 # Except permission denied.
 pass
 
 # Set backend based on KERAS_BACKEND flag, if applicable.
 if 'KERAS_BACKEND' in os.environ:
 _backend = os.environ['KERAS_BACKEND']
 _BACKEND = _backend
 
 # Import backend functions.
 if _BACKEND == 'cntk':
 sys.stderr.write('Using CNTK backend\n')
 from .cntk_backend import *
 elif _BACKEND == 'theano':
 sys.stderr.write('Using Theano backend.\n')
 from .theano_backend import *
 elif _BACKEND == 'tensorflow':
 sys.stderr.write('Using TensorFlow backend.\n')
 from .tensorflow_backend import *
 else:
 # Try and load external backend.
 try:
 backend_module = importlib.import_module(_BACKEND)
 entries = backend_module.__dict__
 # Check if valid backend.
 # Module is a valid backend if it has the required entries.
 required_entries = ['placeholder', 'variable', 'function']
 for e in required_entries:
 if e not in entries:
 raise ValueError('Invalid backend. Missing required entry : ' + e)
 namespace = globals()
 for k, v in entries.items():
 # Make sure we don't override any entries from common, such as epsilon.
 if k not in namespace:
 namespace[k] = v
 sys.stderr.write('Using ' + _BACKEND + ' backend.\n')
 except ImportError:
 raise ValueError('Unable to import backend : ' + str(_BACKEND))
 
 
 def backend():
 """Publicly accessible method
 for determining the current backend.
 
 # Returns
 String, the name of the backend Keras is currently using.
 
 # Example
 ```python
 >>> keras.backend.backend()
 'tensorflow'
 ```
 """
 return _BACKEND
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