convolutional_autoencoder.py

class ConvolutionalAutoEncoder(chainer.Chain):
    def __init__(self):
        super(ConvolutionalAutoEncoder).__init__(
            c1=L.Convolution2D(...),
            c2=L.Convolution2D(...),
            dc1=L.Deconvolution2D(...),
            dc2=L.Deconvolution2D(...),
       )

    def convolve(self, x):
        return self.c2(self.c1(x))  # optionally insert F.max_pooling_2d
    
    def deconvolve(self, h):
        return self.dc2(self.dc1(h))
    
    def __call__(self, x):
        h = self.convolve(x)
        x_hat = self.deconvolve(h)

        # Instead of using L.Classifier, we calculate the loss value inside of the autoencoder.
        # If we want to get reconstructed images, we need to implement a method like thist:
        # 
        # def reconstruct(self, x):
        #     return self.deconvolve(self.convolve(x))
        # 
        # As we compare with mean_squared_error, x and x_hat must have shapes.
        loss = F.mean_squared_error(x, x_hat)

        return loss

model = ConvolutionalAutoEncoder()

# Create optimizer, updater, trainer as usual

trainer.run()

# convolve and deconvolve test images
images = get_test_batch()
convolved_images = model.convolve(images)  # convolved_images is an instance of chainer.Variable, you can have access to raw data with .data attribute
deconvolved_images = model.deconvolve(convolved_images)