问题描述
我正在尝试使用keras中的Model子类实现Wideresnet。我不明白我的代码有什么问题：
class ResidualBlock(layers.Layer):
def __init__(self,filters,kernel_size,dropout,dropout_percentage,strides=1,**kwargs):
super(ResidualBlock,self).__init__(**kwargs)
self.conv_1 = layers.Conv2D(filters,(1,1),strides=strides)
self.bn_1 = layers.Batchnormalization()
self.rel_1 = layers.ReLU()
self.conv_2 = layers.Conv2D(filters,padding="same",strides=strides)
self.dropout = layers.Dropout(dropout_percentage)
self.bn_2 = layers.Batchnormalization()
self.rel_2 = layers.ReLU()
self.conv_3 = layers.Conv2D(filters,padding="same")
self.add = layers.Add()
self.dropout = dropout
self.strides = strides
def call(self,inputs):
x = inputs
if self.strides > 1:
x = self.conv_1(x)
res_x = self.bn_1(x)
res_x = self.rel_1(x)
res_x = self.conv_2(x)
if self.dropout:
res_x = self.dropout(x)
res_x = self.bn_2(x)
res_x = self.rel_2(x)
res_x = self.conv_3(x)
inputs = self.add([x,res_x])
return inputs
class WideResidualNetwork(models.Model):
def __init__(self,input_shape,n_classes,d,k,kernel_size=(3,3),dropout=False,dropout_percentage=0.3,**kwargs):
super(WideResidualNetwork,self).__init__(**kwargs)
if (d-4)%6 != 0:
raise ValueError('Please choose a correct depth!')
self.rel_1 = layers.ReLU()
self.conv_1 = layers.Conv2D(16,(3,padding='same')
self.conv_2 = layers.Conv2D(16*k,1))
self.dense = layers.Dense(n_classes)
self.dropout = dropout
self.dropout_percentage = dropout_percentage
self.N = int((d - 4) / 6)
self.k = k
self.d = d
self.kernel_size = kernel_size
def build(self,input_shape):
self.bn_1 = layers.Batchnormalization(input_shape=input_shape)
def call(self,inputs):
x = self.bn_1(inputs)
x = self.rel_1(x)
x = self.conv_1(x)
x = self.conv_2(x)
for _ in range(self.N):
x = ResidualBlock(16*self.k,self.kernel_size,self.dropout,self.dropout_percentage)(x)
x = ResidualBlock( 32*self.k,self.dropout_percentage,strides=2)(x)
for _ in range(self.N-1):
x = ResidualBlock( 32*self.k,self.dropout_percentage)(x)
x = ResidualBlock( 64*self.k,strides=2)(x)
for _ in range(self.N-1):
x = ResidualBlock( 64*self.k,self.dropout_percentage)(x)
x = layers.GlobalAveragePooling2D()(x)
x = self.dense(x)
x = layers.Activation("softmax")(x)
return x
当我尝试以这种方式拟合模型时：
(x_train,y_train),(x_test,y_test) = tf.keras.datasets.cifar10.load_data()
model = WideResidualNetwork(x_train[0].shape,10,28,1)
x_train,x_test = x_train/255.,x_test/255.
model = WideResidualNetwork(x_train[0].shape,1)
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
epochs = 40
batch_size = 64
validation_split = 0.2
h = model.fit(x_train,y_train,epochs=epochs,batch_size=batch_size,validation_split=validation_split)
我遇到以下错误：
...
<ipython-input-26-61c1bdb3546c>:31 call *
x = ResidualBlock(16*self.k,self.dropout_percentage)(x)
<ipython-input-9-3fea1e77cb6e>:23 call *
res_x = self.bn_1(x)
...
ValueError: tf.function-decorated function tried to create variables on non-first call.
所以我不明白问题出在哪里，我也尝试将初始化移到构建中，但是没有结果，错误仍然存在。我的知识可能有些不足 预先谢谢你
解决方法
您正在将ResidualBlocks，GlobalAveragePooling2D和Activation层初始化为call方法。像将它们移到其他层一样，尝试将它们移到init中，它不应该给您该错误。