Tensorflow 当使用load_模型时，keras内核初始值设定项被错误地调用

Keras版本2.2.4， tensorflow版本1.13.1， 我用的是colab笔记本

我正在尝试创建自定义初始值设定项，并使用model.save保存模型，但当我再次加载模型时，出现以下错误：

TypeError:myInit缺少1个必需的位置参数：“input\u shape”

我有以下代码：

import numpy as np

import tensorflow as tf
import keras

from google.colab import drive

from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Flatten, Lambda, Reshape, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras import backend as K
K.set_image_data_format('channels_first')
K.backend()
# the output should be 'tensorflow'

“tensorflow”

该初始值设定项被赋予一个输入_形状，并返回文档中的keras张量：

权重已正确初始化，因为当我调用model.layers[1].get_weights时，我得到了一个满是值的数组。 我使用model.save保存模型：

model.save(somepath)

然后我在另一个笔记本上打电话

model = load_model(somepath, 
           custom_objects={
               'tf' : tf,
               'myInit' : myInit
           }
          )

在本笔记本中，还定义了myInit和所有导入。 调用load_model时，出现以下错误：

TypeError:myInit缺少1个必需的位置参数：“input\u shape”

因此，似乎在加载模型时，输入的_形状不会传递给myInit。有人知道吗

完整跟踪：

TypeError                                 Traceback (most recent call last)

<ipython-input-25-544d137de03f> in <module>()
      2            custom_objects={
      3                'tf' : tf,
----> 4                'myInit' : myInit
      5            }
      6           )

/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in load_model(filepath, custom_objects, compile)
    417     f = h5dict(filepath, 'r')
    418     try:
--> 419         model = _deserialize_model(f, custom_objects, compile)
    420     finally:
    421         if opened_new_file:

/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in _deserialize_model(f, custom_objects, compile)
    223         raise ValueError('No model found in config.')
    224     model_config = json.loads(model_config.decode('utf-8'))
--> 225     model = model_from_config(model_config, custom_objects=custom_objects)
    226     model_weights_group = f['model_weights']
    227 

/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in model_from_config(config, custom_objects)
    456                         '`Sequential.from_config(config)`?')
    457     from ..layers import deserialize
--> 458     return deserialize(config, custom_objects=custom_objects)
    459 
    460 

/usr/local/lib/python3.6/dist-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
     53                                     module_objects=globs,
     54                                     custom_objects=custom_objects,
---> 55                                     printable_module_name='layer')

/usr/local/lib/python3.6/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    143                     config['config'],
    144                     custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 145                                         list(custom_objects.items())))
    146             with CustomObjectScope(custom_objects):
    147                 return cls.from_config(config['config'])

/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py in from_config(cls, config, custom_objects)
    298         for conf in layer_configs:
    299             layer = layer_module.deserialize(conf,
--> 300                                              custom_objects=custom_objects)
    301             model.add(layer)
    302         if not model.inputs and build_input_shape:

/usr/local/lib/python3.6/dist-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
     53                                     module_objects=globs,
     54                                     custom_objects=custom_objects,
---> 55                                     printable_module_name='layer')

/usr/local/lib/python3.6/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    145                                         list(custom_objects.items())))
    146             with CustomObjectScope(custom_objects):
--> 147                 return cls.from_config(config['config'])
    148         else:
    149             # Then `cls` may be a function returning a class.

/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in from_config(cls, config)
   1107             A layer instance.
   1108         """
-> 1109         return cls(**config)
   1110 
   1111     def count_params(self):

/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.6/dist-packages/keras/layers/core.py in __init__(self, units, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
    846         self.activation = activations.get(activation)
    847         self.use_bias = use_bias
--> 848         self.kernel_initializer = initializers.get(kernel_initializer)
    849         self.bias_initializer = initializers.get(bias_initializer)
    850         self.kernel_regularizer = regularizers.get(kernel_regularizer)

/usr/local/lib/python3.6/dist-packages/keras/initializers.py in get(identifier)
    509     elif isinstance(identifier, six.string_types):
    510         config = {'class_name': str(identifier), 'config': {}}
--> 511         return deserialize(config)
    512     elif callable(identifier):
    513         return identifier

/usr/local/lib/python3.6/dist-packages/keras/initializers.py in deserialize(config, custom_objects)
    501                                     module_objects=globals(),
    502                                     custom_objects=custom_objects,
--> 503                                     printable_module_name='initializer')
    504 
    505 

/usr/local/lib/python3.6/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    152             custom_objects = custom_objects or {}
    153             with CustomObjectScope(custom_objects):
--> 154                 return cls(**config['config'])
    155     elif isinstance(identifier, six.string_types):
    156         function_name = identifier

TypeError: myInit() missing 1 required positional argument: 'input_shape'

---

注意：我也发布了这篇文章，但我认为这是一个更好的地方。

在查看源代码后，我得到了以下工作代码，这应该是定义初始值设定项的正确方法，尤其是在使用load\u model加载模型时：

import numpy as np

import tensorflow as tf
import keras

from google.colab import drive

from keras.models import Sequential, load_model
from keras.layers import Dense
from keras import backend as K
from keras.initializers import Initializer

K.backend()
# the output should be 'tensorflow'

构建模型：

model = Sequential()
model.add( 
  Dense( 2, input_shape=(784,) )
)
model.add(
  Dense( 3, kernel_initializer=myInit( 2019 ) )
)
model.add(
  Dense( 5 )
)
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

model.save( somepath )

保存模型：

model = Sequential()
model.add( 
  Dense( 2, input_shape=(784,) )
)
model.add(
  Dense( 3, kernel_initializer=myInit( 2019 ) )
)
model.add(
  Dense( 5 )
)
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

model.save( somepath )

现在我们可以将模型加载到不同的笔记本中。从另一个笔记本的导入也应该在这里导入，myInit也应该在这个笔记本中定义

model = load_model( somepath, 
           custom_objects={
               'tf' : tf,
               'myInit' : myInit
           }
          )

model = load_model( somepath, 
           custom_objects={
               'tf' : tf,
               'myInit' : myInit
           }
          )