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[英]ValueError: Output tensors of a Functional model must be the output of a TensorFlow `Layer` (thus holding past layer metadata)
[英]Output tensors to a Model must be the output of a TensorFlow `Layer` (thus holding past layer metadata) in Model Api Tensorfow
def generator_model(self):
input_images = Input(shape=[64,64,1])
layer1= Conv2D(self.filter_size,self.kernel_size,(2,2),padding='same',use_bias=False,kernel_initializer='random_uniform')(input_images)
layer1=LeakyReLU(0.2)(layer1)
layer2= Conv2D(self.filter_size*2,self.kernel_size,(2,2),padding='same',use_bias=False,kernel_initializer='random_uniform')(layer1)
layer2=BatchNormalization()(layer2)
layer2=LeakyReLU(0.2)(layer2)
layer3=Conv2D(self.filter_size*4,self.kernel_size,(2,2),padding='same',use_bias=False,kernel_initializer='random_uniform')(layer2)
layer3=BatchNormalization()(layer3)
layer3=LeakyReLU(0.2)(layer3)
layer4=Conv2D(self.filter_size*8,self.kernel_size,(2,2),padding='same',use_bias=False,kernel_initializer='random_uniform')(layer3)
layer4=BatchNormalization()(layer4)
layer4=LeakyReLU(0.2)(layer4)
layer5=Conv2D(self.filter_size*16,self.kernel_size,(2,2),padding='same',use_bias=False,kernel_initializer='random_uniform')(layer4)
layer5=BatchNormalization()(layer5)
layer5=LeakyReLU(0.2)(layer5)
up_layer5 = Conv2DTranspose(self.filter_size*8,self.kernel_size,strides = (2,2),padding='same',use_bias=False)(layer5)
up_layer5=BatchNormalization()(up_layer5)
up_layer5=LeakyReLU(0.2)(up_layer5)
#shape = 4*4*512
up_layer5_concat = tf.concat([up_layer5,layer4],0)
up_layer6 = Conv2DTranspose(self.filter_size*4,self.kernel_size,strides = (2,2),padding='same',use_bias=False)(up_layer5_concat)
up_layer6 =BatchNormalization()(up_layer6)
up_layer6 =LeakyReLU(0.2)(up_layer6)
up_layer_6_concat = tf.concat([up_layer6,layer3],0)
up_layer7 = Conv2DTranspose(self.filter_size*2,self.kernel_size,strides = (2,2),padding='same',use_bias=False)(up_layer_6_concat)
up_layer7 =BatchNormalization()(up_layer7)
up_layer7 =LeakyReLU(0.2)(up_layer7)
up_layer_7_concat = tf.concat([up_layer7,layer2],0)
up_layer8 = Conv2DTranspose(self.filter_size,self.kernel_size,strides = (2,2),padding='same',use_bias=False)(up_layer_7_concat)
up_layer8 =BatchNormalization()(up_layer8)
up_layer8 =LeakyReLU(0.2)(up_layer8)
up_layer_8_concat = tf.concat([up_layer8,layer1],0)
output = Conv2D(3,self.kernel_size,strides = (1,1),padding='same',use_bias=False)(up_layer_8_concat)
final_output = LeakyReLU(0.2)(output)
model = Model(input_images,output)
model.summary()
return model
这就是我的generator_model的样子,并且我遵循了一份研究论文来构建体系结构。 但是,我对错误有疑问。 我已经在SO中检查了针对给定问题的其他解决方案,但是它们对我没有用,因为它们可能有点不同。 我的猜测是,问题应该存在于tf.concat()
函数中,该函数应作为Lambda的tensorflow keras层放置,但是我也尝试了这一点,但没有帮助。 关于这个问题有帮助吗? 烦我两天了。
使用Keras功能API定义模型时, 必须使用Keras图层来构建模型。
因此,您是对的,问题出在您的tf.concat
调用中。
但是,在tf.keras.layers
包中,您可以找到Concatenate
层,该层也使用功能性API。
因此,您可以从以下位置替换concat层:
up_layer5_concat = tf.concat([up_layer5,layer4],0)
至
up_layer5_concat = tf.keras.layers.Concatenate()([up_layer5, layer4])
以此类推,对于网络中的所有其他tf.concat
调用
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