即使在图像转换后，Keras CNN图像和内核大小也不匹配

Question

I'm trying to run a CNN similar to the one in the Keras documantation "VGG-like convnet" but for a custom set of images and binary classification instead of a 10-class output.

When I try to fit the CNN, i get this longwinded error that I assume is telling me my input image size is not the right size for the CNN input.

ValueError: GpuDnnConv images and kernel must have the same stack size

Apply node that caused the error: GpuDnnConv{algo='small', inplace=True}(GpuContiguous.0, GpuContiguous.0, GpuAllocEmpty.0, GpuDnnConvDesc{border_mode='valid', subsample=(1, 1), conv_mode='conv', precision='float32'}.0, Constant{1.0}, Constant{0.0})
Toposort index: 130
Inputs types: [CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D), <theano.gof.type.CDataType object at 0x7f0eefc8d790>, Scalar(float32), Scalar(float32)]
Inputs shapes: [(32, 232, 300, 3), (300, 1, 3, 3), (32, 300, 298, 1), 'No shapes', (), ()]
Inputs strides: [(208800, 900, 3, 1), (9, 0, 3, 1), (89400, 298, 1, 0), 'No strides', (), ()]
Inputs values: ['not shown', 'not shown', 'not shown', <PyCObject object at 0x7f0efaba8e68>, 1.0, 0.0]
Inputs name: ('image', 'kernel', 'output', 'descriptor', 'alpha', 'beta')

The thing is I thought I reshaped all my images to fit. My input is a stack of 4000 232x300 px RBG images and the output is an array of 4000 boolean values.

Input : im_list.shape Out[49]: (4000, 232, 300, 3)

Output : np.asarray(cls).shape Out[50]: (4000,)

This is the function to build the CNN

CNN = buildCNN(3, 232, 300, 2)
CNN.fit(im_list, cls, batch_size=32, nb_epoch=1)

    def buildCNN(depth,width,height,outputShape):
    CNN = Sequential()
        # input: 232x300 images with 3 channels -> (3, 100, 100) tensors.
        # this applies 32 convolution filters of size 3x3 each.
        CNN.add(Convolution2D(32, 3, 3, border_mode='valid', input_shape=(depth,width,height)))
        CNN.add(Activation('relu'))
        CNN.add(Convolution2D(32, 3, 3))
        CNN.add(Activation('relu'))
        CNN.add(MaxPooling2D(pool_size=(2, 2)))
        CNN.add(Dropout(0.25))
        #
        CNN.add(Convolution2D(64, 3, 3, border_mode='valid'))
        CNN.add(Activation('relu'))
        CNN.add(Convolution2D(64, 3, 3))
        CNN.add(Activation('relu'))
        CNN.add(MaxPooling2D(pool_size=(2, 2)))
        CNN.add(Dropout(0.25))
        #
        CNN.add(Flatten())
        # Note: Keras does automatic shape inference.
        CNN.add(Dense(256))
        CNN.add(Activation('relu'))
        CNN.add(Dropout(0.5))
        #
        CNN.add(Dense(outputShape))
        CNN.add(Activation('softmax'))
        #
        sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
        CNN.compile(loss='categorical_crossentropy', optimizer=sgd)
        #
    return CNN

I've been banging my head against the wall with this long enough that I thought maybe someone else has had this problem. Any thoughts? Thanks in advance.

Answer 1

You specified input as (depth,width,height) . So the array you must must have dimensions (N,depth,width,height) , where N is the number of training examples.

The input you are actually passing, (4000, 232, 300, 3) , doesn't match. It should be reshaped to be (4000, depth, width, height) . This means you'll have to resize each image, and reorder the axes.

Answer 2

上面的答案是正确的：对于后代，我的问题通过一个简单的解决：

im_list = im_list.transpose((0,3,1,2))