python - Keras“模型”对象没有属性“_callable_losses”

Question

我有以下由一个网络组成的类，该网络采用给定的 np.array 输入和输入形状并返回具有相同形状的输出。我想实现一个需要三个参数的自定义损失。为此，我使用 Keras 方法 .add_loss，但它似乎会引发错误。我使用的类如下（简化）：

class Net():

    def __init__(self, **kwargs: dict):
        
        self._input_shape = kwargs.get('input_shape', (12, 86, 98,1))
        
    def _build(self):
       """
       This method generates the output x.
       """ 
       
        
       inputs = Input(shape = self._input_shape)
        
       x = self.f1(inputs)
       x = self.f2(x)
       
        
       self.get_optimiser()
       model = self.get_loss(inputs, x)
            
       return model
    
    def f1(self, inputs):
       
        return inputs
    
    def f2(self, inputs):
       
        return inputs
    
    def get_optimiser(self):
        
        self._optimiser = SGD(lr = self._learnrate, momentum = self._momentum, decay = self._decay, nesterov = False)
    
    def get_loss(self, inputs, outputs):
        
        self._model = Model(inputs=[inputs], outputs=[outputs])
                   
        y_true = Input(self._input_shape, name = 'y_true')
        is_weight = Input(self._input_shape, name = 'is_weight')

        self._model = Model(inputs=[inputs, y_true, is_weight], outputs=[outputs])
            
        self._model.add_loss(weighted_dice_loss(y_true, outputs, is_weight))
        self._model.compile(optimizer = self._optimiser, loss = None, metrics = [dice_coef])
        
        return self._model

但是，在实例化类并运行 _build 方法时：

net = Net()
net._build()

我收到以下错误：

<ipython-input-124-46b9e7f916ce> in _build(self)
     50 
     51         self.get_optimiser()
---> 52         model = self.get_loss(inputs, x)
     53 
     54         return model

<ipython-input-124-46b9e7f916ce> in get_loss(self, inputs, outputs)
    151             model = Model(inputs=[inputs, y_true, is_weight], outputs=[outputs])
    152 
--> 153             model.add_loss(weighted_dice_loss(y_true, outputs, is_weight))
    154             self._model.compile(optimizer = self._optimiser, loss = None, metrics = [dice_coef])
    155 

~/miniconda3/envs/segment/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in add_loss(self, losses, inputs)
    899         eager_losses.append(_tag_unconditional(loss))
    900 
--> 901     self._callable_losses += callable_losses
    902 
    903     call_context = base_layer_utils.is_in_call_context()

AttributeError: 'Model' object has no attribute '_callable_losses'

编辑 我已将 tensorflow 更新到 v 2.2.0，并且我正在导入以下模块：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import Input, optimizers
from tensorflow.keras.models import Sequential, load_model


from keras.layers.convolutional import Conv3D, Conv3DTranspose
from keras.layers.normalization import BatchNormalization
from keras.layers import Cropping3D, UpSampling3D, AveragePooling3D

抛出的错误现在已更改为：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-4-c4c292a59224> in <module>
     11 
     12 
---> 13 unet_model = unet._build()

<ipython-input-2-9245d04f54db> in _build(self)
     44         inputs = Input(shape = self._input_shape)
     45 
---> 46         x = self.first_layers(inputs)
     47         x = self.contractive_path(x)
     48         x = self.middle_path(x)

<ipython-input-2-9245d04f54db> in first_layers(self, inputs)
     57 
     58         layer = Conv3D(filters = self._n_filters, kernel_size = self._patch, activation = self._activation, kernel_initializer = self._kernel_initializer,
---> 59                 padding = self._padding)(inputs)
     60 
     61         return layer

~/miniconda3/envs/segment/lib/python3.7/site-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

~/miniconda3/envs/segment/lib/python3.7/site-packages/keras/layers/convolutional.py in __init__(self, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
    617             kernel_constraint=kernel_constraint,
    618             bias_constraint=bias_constraint,
--> 619             **kwargs)
    620 
    621     def get_config(self):

~/miniconda3/envs/segment/lib/python3.7/site-packages/keras/layers/convolutional.py in __init__(self, rank, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
    103                  bias_constraint=None,
    104                  **kwargs):
--> 105         super(_Conv, self).__init__(**kwargs)
    106         self.rank = rank
    107         self.filters = filters

~/miniconda3/envs/segment/lib/python3.7/site-packages/keras/engine/base_layer.py in __init__(self, **kwargs)
    130         if not name:
    131             prefix = self.__class__.__name__
--> 132             name = _to_snake_case(prefix) + '_' + str(K.get_uid(prefix))
    133         self.name = name
    134 

~/miniconda3/envs/segment/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py in get_uid(prefix)
     72     """
     73     global _GRAPH_UID_DICTS
---> 74     graph = tf.get_default_graph()
     75     if graph not in _GRAPH_UID_DICTS:
     76         _GRAPH_UID_DICTS[graph] = defaultdict(int)

AttributeError: module 'tensorflow' has no attribute 'get_default_graph'

标签： pythontensorflowkerasdeep-learning