Question

我正在使用Keras Functional API引入自定义层GaussianLayer，该层返回两个元素的列表（请参见下面的call方法）：

import tensorflow as tf
from keras.layers import Input, Dense
from keras.models import Model
from tensorflow.keras.layers import Layer

def gaussian_loss(y_true, y_pred):
    """
    y_true is a scalar (float)
    y_pred is a tensor [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])) + 1

class 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 tensor
inputs = 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)

不幸的是，此代码失败，并显示AttributeError：

----------------------------------------------------------------
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'

我知道我需要使用Functional API才能返回要传递给gaussian_loss的列表，但不幸的是，这似乎也不起作用。

Answer 1

首先，不要混合使用keras和tensorflow.keras模块。在代码中仅使用其中之一。

第二，通过在末尾添加bias以使其成为元组（即,）来固定两个shape=(self.output_dim,)变量的形状。

第三，如果您的自定义层返回两个张量的列表作为输出，则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)]

自定义Keras层无法使用功能性API

1 个答案: