为什么使用自定义图层的模型不能正常工作

Question

我正在定制一个层，以便在我的模型中使用。核心部分是"；呼叫"；功能如

class Custom_Layer(Layer):
// some code

def call(self, inputs, **kwargs):
kernel = mul(self.base, self.diag_start - self.diag_end) 
outputs = matmul(a=inputs, b=kernel)
if self.use_bias:
outputs = tf.nn.bias_add(outputs, self.bias)
if self.activation is not None:
outputs = self.activation(outputs)
return outputs    
// some code

并且它被用于一个简单的模型中。

inputs = tf.keras.layers.Input(shape=(784,),dtype='float32') 
layer1 = Custom_layer(2000, **Custom_layer_config, activation='tanh')(inputs)
layer2 = Custom_layer(200, **Custom_layer_config, activation='tanh')(layer1)
output_lay = Custom_layer(10, **Custom_layer_config, activation='softmax')(layer2)
model = tf.keras.models.Model(inputs=inputs, outputs=output_lay)
opt = tf.keras.optimizers.Adamax(learning_rate=0.02)
model.compile(optimizer=opt,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.summary()

它应该像这样打印：

Model: "functional_13"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_8 (InputLayer)         [(None, 784)]             0         
_________________________________________________________________
CustomLayer_18 (Custom_Layer)       (None, 2000)              1570784   
_________________________________________________________________
CustomLayer_19 (Custom_Layer)       (None, 200)               402200    
_________________________________________________________________
CustomLayer_20 (Custom_Layer)       (None, 10)                2210      
=================================================================
Total params: 1,975,194
Trainable params: 5,194
Non-trainable params: 1,970,000
_________________________________________________________________

但是打印这个：

Model: "model_1"
_________________________________________________________________
Layer (type)                Output Shape              Param #   
=================================================================
input_2 (InputLayer)        [(None, 784)]             0         

tf.linalg.matmul_3 (TFOpLam  (None, 2000)             0         
bda)                                                            

tf.math.tanh_2 (TFOpLambda)  (None, 2000)             0         

tf.linalg.matmul_4 (TFOpLam  (None, 200)              0         
bda)                                                            

tf.math.tanh_3 (TFOpLambda)  (None, 200)              0         

tf.linalg.matmul_5 (TFOpLam  (None, 10)               0         
bda)                                                            

tf.compat.v1.nn.softmax_1 (  (None, 10)               0         
TFOpLambda)                                                     

=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0

第一个摘要是我从作者的存储库中得到的，第二个摘要是在没有更改任何内容的情况下运行相同代码得到的。。

代码并不复杂，但奇怪的是为什么根本没有参数。我的问题是这里出了什么问题。

Answer 1

尝试将其作为本示例的继承类。

示例：自定义LSTM类

import tensorflow as tf
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Class / Definition
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class MyLSTMLayer( tf.keras.layers.LSTM ):
def __init__(self, units, return_sequences, return_state):
super(MyLSTMLayer, self).__init__( units, return_sequences=True, return_state=False )
self.num_units = units
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]),
self.num_units])
def call(self, inputs):
return tf.matmul(inputs, self.kernel)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
start = 3
limit = 12
delta = 3
sample = tf.range( start, limit, delta )
sample = tf.cast( sample, dtype=tf.float32 )
sample = tf.constant( sample, shape=( 1, 1, 3 ) )
layer = MyLSTMLayer( 3, True, False )
model = tf.keras.Sequential([
tf.keras.Input(shape=(1, 3)),
layer,
])
model.summary()
print( sample )
print( model.predict(sample) )

输出：

Model: "sequential"
_________________________________________________________________
Layer (type)                Output Shape              Param #
=================================================================
my_lstm_layer (MyLSTMLayer)  (None, 1, 3)             9
=================================================================
Total params: 9
Trainable params: 9
Non-trainable params: 0
_________________________________________________________________
tf.Tensor([[[3. 6. 9.]]], shape=(1, 1, 3), dtype=float32)
1/1 [==============================] - 1s 575ms/step
[[[-2.8894916 -2.146874  13.688236 ]]]