我正在使用Keras函数式API引入一个自定义层GaussianLayer，该层返回一个包含两个元素的列表（见下面的call方法）：
import tensorflow as tffrom keras.layers import Input, Densefrom keras.models import Modelfrom tensorflow.keras.layers import Layerdef gaussian_loss(y_true, y_pred): """ y_true是标量（浮点数） y_pred是张量[mu, sigma] """ print(y_pred.shape) return tf.reduce_mean(0.5*tf.log(y_pred[1]) + 0.5*tf.div(tf.square(y_true - y_pred[0]), y_pred[0])) + 1class GaussianLayer(Layer): def __init__(self, output_dim, **kwargs): self.output_dim = output_dim super(GaussianLayer, self).__init__(**kwargs) def build(self, input_shape): self.kernel_1 = self.add_weight(name='kernel', shape=(30, self.output_dim), initializer='uniform', trainable=True) self.kernel_2 = self.add_weight(name='kernel', shape=(30, self.output_dim), initializer='uniform', trainable=True) self.bias_1 = self.add_weight(name='bias', shape=(self.output_dim), initializer='zeros', trainable=True) self.bias_2 = self.add_weight(name='bias', shape=(self.output_dim), initializer='zeros', trainable=True) super(GaussianLayer, self).build(input_shape) def call(self, x): output_mu = K.dot(x, self.kernel_1) + self.bias_1 output_sig = K.dot(x, self.kernel_2) + self.bias_2 output_sig_pos = K.log(1 + K.exp(output_sig)) + 1e-06 return [output_mu, output_sig_pos] def compute_output_shape(self, input_shape): return (input_shape[0], self.output_dim)# This returns a tensorinputs = Input(shape=(1,))x = Dense(24, activation='relu')(inputs)x = Dense(30, activation='relu')(x)predictions = GaussianLayer(1)(x)model = Model(inputs, predictions)model.compile(loss=gaussian_loss, optimizer='adam')model.fit(train_x, train_y, epochs=600)遗憾的是这段代码在执行时出现了AttributeError错误：
----------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-200-e93218491799> in <module>() 49 x = Dense(24, activation='relu')(inputs) 50 x = Dense(30, activation='relu')(x)---> 51 predictions = GaussianLayer(1)(x) 52 53 model = Model(inputs, predictions)/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs) 783 if in_deferred_mode or build_graph and have_all_keras_metadata(inputs): 784 inputs, outputs = self._set_connectivity_metadata_(--> 785 inputs, outputs, args, kwargs) 786 if context.executing_eagerly(): 787 return outputs/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _set_connectivity_metadata_(self, inputs, outputs, args, kwargs) 895 kwargs.pop('mask', None) # `mask` should not be serialized. 896 self._add_inbound_node(--> 897 input_tensors=inputs, output_tensors=outputs, arguments=kwargs) 898 return inputs, outputs 899 /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _add_inbound_node(self, input_tensors, output_tensors, arguments) 1076 input_tensors=input_tensors, 1077 output_tensors=output_tensors,-> 1078 arguments=arguments) 1079 1080 # Update tensor history metadata./Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __init__(self, outbound_layer, inbound_layers, node_indices, tensor_indices, input_tensors, output_tensors, arguments) 1747 # For compatibility with external Keras, we use the deprecated 1748 # accessor here.-> 1749 layer.outbound_nodes.append(self) 1750 # For compatibility with external Keras, we use the deprecated 1751 # accessor here.AttributeError: 'Dense' object has no attribute 'outbound_nodes'我知道为了将列表传递给gaussian_loss，我需要使用函数式API，但遗憾的是这似乎也行不通。
回答：
首先，不要混合使用keras和tensorflow.keras模块。在你的代码中只使用其中一个。
其次，通过在bias变量的形状后面添加一个,来修正它们的形状，使其成为元组，即shape=(self.output_dim,)。
第三，如果你的自定义层返回一个包含两个张量的列表作为输出，那么输出形状必须与此一致，即：
def compute_output_shape(self, input_shape): return [(input_shape[0], self.output_dim), (input_shape[0], self.output_dim)]