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tensorflow - 自定义 keras 层未显示在 model.summary 中

问题描述

当我尝试.summary使用自定义图层查看模型时，我得到以下输出：

Model: "functional_29"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_118 (InputNode)           [(None, 1)]          0                                            
__________________________________________________________________________________________________
input_119 (InputNode)           [(None, 1)]          0                                            
__________________________________________________________________________________________________
tf_op_layer_strided_slice_156 ( [(1,)]               0           input_118[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_strided_slice_157 ( [(1,)]               0           input_119[0][0]                  
__________________________________________________________________________________________________
input_120 (InputNode)           [(None, 1)]          0                                            
_________________________________________________________________________________________________   
tf_op_layer_concat_106 (TensorF [(2,)]               0           tf_op_layer_strided_slice_162[0][
                                                                 tf_op_layer_strided_slice_163[0][

...
__________________________________________________________________________________________________
tf_op_layer_strided_slice_164 ( [(1,)]               0           input_120[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_node_128_output (Te [()]                 0           tf_op_layer_Relu_55[0][0]        
==================================================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
__________________________________________________________________________________________________

这是为什么？如何将所有这些操作包装在标签下MyLayer？

标签： tensorflowkeras

解决方案

您可以通过从tf.keras.layers.Layer.

import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Input

class DirectLayer(tf.keras.layers.Layer):
    
  def __init__(self, name = "direct_layer", **kwargs):
    super(DirectLayer, self).__init__(name=name, **kwargs)

  def build(self, input_shape):
    self.w = self.add_weight(
            shape=input_shape[1:],
            initializer="random_normal",
            trainable=True,
        )
    self.b = self.add_weight(
        shape=input_shape[1:], initializer="random_normal", trainable=True
    )

    
  def call(self, inputs):
      return tf.multiply(inputs, self.w) + self.b

x_in = Input(shape=[10])
x = DirectLayer(name="my_layer")(x_in)
x_out = DirectLayer()(x)

model = Model(x_in, x_out)

x = tf.ones([16,10])
tf.print(model(x))

tf.print(model.summary())

我创建了一个名为 DirectLayer 的简单层。我构建了一个使用该层两次的模型。输入层只是为了指定输入数据的形状。

如您所见，您可以轻松指定图层的名称。

汇总函数产生以下结果：

Model: "functional_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 10)]              0         
_________________________________________________________________
my_layer (DirectLayer)       (None, 10)                20        
_________________________________________________________________
direct_layer (DirectLayer)   (None, 10)                20        
=================================================================
Total params: 40
Trainable params: 40
Non-trainable params: 0
_________________________________________________________________
None

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