Question

我有一个Keras模型=＆gt;

输入：灰色图像：（1,224,224）

输出：RGB图像：（3,224,224）

我希望通过为其提供灰度图像并获取RGB图像来预测像素颜色。我试图在Keras建立一个大致类似于this one (which has been made in Tensorflow)的网络。

以下是型号代码：

first_input = Input(batch_shape=(None, 1, 224, 224))

conv0_1_3 = Convolution2D(3, 3, 3, activation='relu', name='conv0_1_3', border_mode='same')(first_input)

conv1_1_64 = Convolution2D(64, 3, 3, activation='relu', name='conv1_1', border_mode='same')(conv0_1_3)
conv1_2_64 = Convolution2D(64, 3, 3, activation='relu', name='conv1_2', border_mode='same')(conv1_1_64)
conv1_2_64 = MaxPooling2D((2, 2))(conv1_2_64)

conv2_1_128 = Convolution2D(128, 3, 3, activation='relu', name='conv2_1', border_mode='same')(conv1_2_64)
conv2_2_128 = Convolution2D(128, 3, 3, activation='relu', name='conv2_2', border_mode='same')(conv2_1_128)
conv2_2_128 = MaxPooling2D((2, 2))(conv2_2_128)

conv3_1_256 = Convolution2D(256, 3, 3, activation='relu', name='conv3_1', border_mode='same')(conv2_2_128)
conv3_2_256 = Convolution2D(256, 3, 3, activation='relu', name='conv3_2', border_mode='same')(conv3_1_256)
conv3_3_256 = Convolution2D(256, 3, 3, activation='relu', name='conv3_3', border_mode='same')(conv3_2_256)
conv3_3_256 = MaxPooling2D((2, 2))(conv3_3_256)

conv4_1_512 = Convolution2D(512, 3, 3, activation='relu', name='conv4_1', border_mode='same')(conv3_3_256)
conv4_2_512 = Convolution2D(512, 3, 3, activation='relu', name='conv4_2', border_mode='same')(conv4_1_512)
conv4_3_512 = Convolution2D(512, 3, 3, activation='relu', name='conv4_3', border_mode='same')(conv4_2_512)
conv4_3_512 = MaxPooling2D((2, 2))(conv4_3_512)

residual1 = BatchNormalization(axis=1, name='batch1')(conv4_3_512)
residual1 = Convolution2D(256, 3, 3, activation='relu', name='residual1', border_mode='same')(residual1)
residual1 = UpSampling2D(name='upsample1')(residual1)

conv3_3_256_batch_norm = BatchNormalization(axis=1, name='batch2')(conv3_3_256)
merge1 = merge((conv3_3_256_batch_norm, residual1), mode='concat', name='merge1', concat_axis=0)
residual2 = Convolution2D(128, 3, 3, activation='relu', name='residual2', border_mode='same')(merge1)
residual2 = UpSampling2D(name='upsample2')(residual2)

conv2_2_128_batch_norm = BatchNormalization(axis=1, name='batch3')(conv2_2_128)
merge2 = merge((conv2_2_128_batch_norm, residual2), mode='concat', name='merge2', concat_axis=0)
residual3 = Convolution2D(64, 3, 3, activation='relu', name='residual3', border_mode='same')(merge2)
residual3 = UpSampling2D(name='upsample3')(residual3)

conv1_2_64_batch_norm = BatchNormalization(axis=1, name='batch4')(conv1_2_64)
merge3 = merge((conv1_2_64_batch_norm, residual3), mode='concat', name='merge3', concat_axis=0)
residual4 = Convolution2D(3, 3, 3, activation='relu', name='residual4', border_mode='same')(merge3)
residual4 = UpSampling2D(name='upsample4')(residual4)

conv0_1_3_batch_norm = BatchNormalization(axis=1, name='batch5')(conv0_1_3)
merge4 = merge((conv0_1_3_batch_norm, residual4), mode='concat', name='merge4', concat_axis=0)
residual5 = Convolution2D(3, 1, 1, activation='relu', name='residual5', border_mode='same')(merge4)

model = Model(input=first_input, output=residual5)

这是模型摘要：

Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 1, 224, 224)   0                                            
____________________________________________________________________________________________________
conv0_1_3 (Convolution2D)        (None, 3, 224, 224)   30          input_1[0][0]                    
____________________________________________________________________________________________________
conv1_1 (Convolution2D)          (None, 64, 224, 224)  1792        conv0_1_3[0][0]                  
____________________________________________________________________________________________________
conv1_2 (Convolution2D)          (None, 64, 224, 224)  36928       conv1_1[0][0]                    
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D)    (None, 64, 112, 112)  0           conv1_2[0][0]                    
____________________________________________________________________________________________________
conv2_1 (Convolution2D)          (None, 128, 112, 112) 73856       maxpooling2d_1[0][0]             
____________________________________________________________________________________________________
conv2_2 (Convolution2D)          (None, 128, 112, 112) 147584      conv2_1[0][0]                    
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D)    (None, 128, 56, 56)   0           conv2_2[0][0]                    
____________________________________________________________________________________________________
conv3_1 (Convolution2D)          (None, 256, 56, 56)   295168      maxpooling2d_2[0][0]             
____________________________________________________________________________________________________
conv3_2 (Convolution2D)          (None, 256, 56, 56)   590080      conv3_1[0][0]                    
____________________________________________________________________________________________________
conv3_3 (Convolution2D)          (None, 256, 56, 56)   590080      conv3_2[0][0]                    
____________________________________________________________________________________________________
maxpooling2d_3 (MaxPooling2D)    (None, 256, 28, 28)   0           conv3_3[0][0]                    
____________________________________________________________________________________________________
conv4_1 (Convolution2D)          (None, 512, 28, 28)   1180160     maxpooling2d_3[0][0]             
____________________________________________________________________________________________________
conv4_2 (Convolution2D)          (None, 512, 28, 28)   2359808     conv4_1[0][0]                    
____________________________________________________________________________________________________
conv4_3 (Convolution2D)          (None, 512, 28, 28)   2359808     conv4_2[0][0]                    
____________________________________________________________________________________________________
maxpooling2d_4 (MaxPooling2D)    (None, 512, 14, 14)   0           conv4_3[0][0]                    
____________________________________________________________________________________________________
batch1 (BatchNormalization)      (None, 512, 14, 14)   1024        maxpooling2d_4[0][0]             
____________________________________________________________________________________________________
residual1 (Convolution2D)        (None, 256, 14, 14)   1179904     batch1[0][0]                     
____________________________________________________________________________________________________
batch2 (BatchNormalization)      (None, 256, 28, 28)   512         maxpooling2d_3[0][0]             
____________________________________________________________________________________________________
upsample1 (UpSampling2D)         (None, 256, 28, 28)   0           residual1[0][0]                  
____________________________________________________________________________________________________
merge1 (Merge)                   (None, 256, 28, 28)   0           batch2[0][0]                     
                                                                   upsample1[0][0]                  
____________________________________________________________________________________________________
residual2 (Convolution2D)        (None, 128, 28, 28)   295040      merge1[0][0]                     
____________________________________________________________________________________________________
batch3 (BatchNormalization)      (None, 128, 56, 56)   256         maxpooling2d_2[0][0]             
____________________________________________________________________________________________________
upsample2 (UpSampling2D)         (None, 128, 56, 56)   0           residual2[0][0]                  
____________________________________________________________________________________________________
merge2 (Merge)                   (None, 128, 56, 56)   0           batch3[0][0]                     
                                                                   upsample2[0][0]                  
____________________________________________________________________________________________________
residual3 (Convolution2D)        (None, 64, 56, 56)    73792       merge2[0][0]                     
____________________________________________________________________________________________________
batch4 (BatchNormalization)      (None, 64, 112, 112)  128         maxpooling2d_1[0][0]             
____________________________________________________________________________________________________
upsample3 (UpSampling2D)         (None, 64, 112, 112)  0           residual3[0][0]                  
____________________________________________________________________________________________________
merge3 (Merge)                   (None, 64, 112, 112)  0           batch4[0][0]                     
                                                                   upsample3[0][0]                  
____________________________________________________________________________________________________
residual4 (Convolution2D)        (None, 3, 112, 112)   1731        merge3[0][0]                     
____________________________________________________________________________________________________
batch5 (BatchNormalization)      (None, 3, 224, 224)   6           conv0_1_3[0][0]                  
____________________________________________________________________________________________________
upsample4 (UpSampling2D)         (None, 3, 224, 224)   0           residual4[0][0]                  
____________________________________________________________________________________________________
merge4 (Merge)                   (None, 3, 224, 224)   0           batch5[0][0]                     
                                                                   upsample4[0][0]                  
____________________________________________________________________________________________________
residual5 (Convolution2D)        (None, 3, 224, 224)   12          merge4[0][0]                     
====================================================================================================
Total params: 9187699

我不知道我做错了什么，因为摘要完全符合我的想法，但无论如何，我不断收到这个错误：

ValueError：GpuElemwise。输入维度不匹配。输入2（索引从0开始）的形状[0] == 1，但该轴上的输出大小为5。应用导致错误的节点：GpuElemwise {Composite {（（i0 *（i1 + Abs（i1））） - i2）}，no_inplace}（CudaNdarrayConstant {[[[0.5]]]]}，GpuElemwise {Add} [ （0,0）]。0，GpuFromHost.0） Toposort指数：916 输入类型：[CudaNdarrayType（float32，（True，True，True，True）），CudaNdarrayType（float32,4D），CudaNdarrayType（float32,4D）] 输入形状：[（1,1,1,1），（5,3,224,224），（1,3,224,224）] 输入步幅：[（0,0,0,0），（150528,50176,224,1），（0,50176,224,1）] 输入值：[CudaNdarray（[[[[0.5]]]]），'未显示'，'未显示'] 输入type_num：[''，''，'']

我也在这个问题中包含了一个模型图： model

调试这是一场噩梦......其他大多数错误都很容易理解和修复，但这些错误真的很难理解......不幸的是，这不是我第一次遇到这些错误Keras。

请！这个型号出了什么问题？！我做错了什么或者这个模型不应该这样设计？

非常感谢...

Answer 1

你正在错误的轴上合并。 axis = 0实际上是具有不同样本的轴。你可以从你的模型中看到：

batch2 (BatchNormalization)      (None, 256, 28, 28)   512         maxpooling2d_3[0][0]             
upsample1 (UpSampling2D)         (None, 256, 28, 28)   0           residual1[0][0]                  
merge1 (Merge)                   (None, 256, 28, 28)   0           batch2[0][0]

合并后，要素图的数量完全没有变化。设置axis = 1以解决此问题。

Keras和GpuElemwise错误的自动图像修复

1 个答案: