使用 Keras 和 GpuElemwise 错误自动修复图像
Automatic Image Inpainting with Keras and GpuElemwise Errors
我有一个 Keras 模型 =>
Input : Gray Image : (1, 224, 224)
Output : RGB Image: (3, 224, 224)
我想通过给它灰度图像并获得 RGB 图像来预测像素颜色。
我试图在 Keras 中建立一个网络，它主要类似于 this one (which has been made in Tensorflow).
这是模型代码：
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，因为总结完全符合我的想法，但无论如何，我总是收到这个错误:
ValueError: GpuElemwise. Input dimension mis-match. Input 2 (indices start at 0) has shape[0] == 1, but the output's size on that axis is 5.
Apply node that caused the error: GpuElemwise{Composite{((i0 * (i1 + Abs(i1))) - i2)},no_inplace}(CudaNdarrayConstant{[[[[ 0.5]]]]}, GpuElemwise{Add}[(0, 0)].0, GpuFromHost.0)
Toposort index: 916
Inputs types: [CudaNdarrayType(float32, (True, True, True, True)), CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D)]
Inputs shapes: [(1, 1, 1, 1), (5, 3, 224, 224), (1, 3, 224, 224)]
Inputs strides: [(0, 0, 0, 0), (150528, 50176, 224, 1), (0, 50176, 224, 1)]
Inputs values: [CudaNdarray([[[[ 0.5]]]]), 'not shown', 'not shown']
Inputs type_num: ['', '', '']
我也在这个问题中包含了模型图：
调试这是一场噩梦...大多数其他错误很容易理解和修复，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 错误。
拜托！这个模型有什么问题？！我是不是做错了什么，或者这个模型不应该这样设计？
非常感谢...
您在错误的轴上合并。 axis = 0实际上是不同样本的轴。从你的模型可以看出：
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feature maps的数量完全没有变化。设置 axis = 1 来解决这个问题。
我有一个 Keras 模型 =>
Input : Gray Image : (1, 224, 224)
Output : RGB Image: (3, 224, 224)
我想通过给它灰度图像并获得 RGB 图像来预测像素颜色。 我试图在 Keras 中建立一个网络，它主要类似于 this one (which has been made in Tensorflow).
这是模型代码：
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，因为总结完全符合我的想法，但无论如何，我总是收到这个错误:
ValueError: GpuElemwise. Input dimension mis-match. Input 2 (indices start at 0) has shape[0] == 1, but the output's size on that axis is 5. Apply node that caused the error: GpuElemwise{Composite{((i0 * (i1 + Abs(i1))) - i2)},no_inplace}(CudaNdarrayConstant{[[[[ 0.5]]]]}, GpuElemwise{Add}[(0, 0)].0, GpuFromHost.0) Toposort index: 916 Inputs types: [CudaNdarrayType(float32, (True, True, True, True)), CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D)] Inputs shapes: [(1, 1, 1, 1), (5, 3, 224, 224), (1, 3, 224, 224)] Inputs strides: [(0, 0, 0, 0), (150528, 50176, 224, 1), (0, 50176, 224, 1)] Inputs values: [CudaNdarray([[[[ 0.5]]]]), 'not shown', 'not shown'] Inputs type_num: ['', '', '']
我也在这个问题中包含了模型图：
调试这是一场噩梦...大多数其他错误很容易理解和修复，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 错误。
拜托！这个模型有什么问题？！我是不是做错了什么，或者这个模型不应该这样设计？
非常感谢...
您在错误的轴上合并。 axis = 0实际上是不同样本的轴。从你的模型可以看出：
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feature maps的数量完全没有变化。设置 axis = 1 来解决这个问题。