问Keras / Theano:如何添加Convolution2D层？

EN

ValueError: GpuCorrMM shape inconsistency:
  bottom shape: 128 32 30 30
  weight shape: 3 32 3 3
  top shape: 128 1 28 28 (expected 128 3 28 28)

Apply node that caused the error: GpuCorrMM_gradInputs{valid, (1, 1)}(GpuContiguous.0, GpuContiguous.0)
Inputs types: [CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D)]
Inputs shapes: [(3, 32, 3, 3), (128, 1, 28, 28)]
Inputs strides: [(288, 9, 3, 1), (784, 0, 28, 1)]
Inputs values: ['not shown', 'not shown']

model = Sequential()

model.add(Convolution2D(32, 1, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(32, 32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))

model.add(Convolution2D(64, 32, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(64*8*8, 512))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(512, nb_classes))
model.add(Activation('softmax'))

# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)