Visualizing_Understanding_CNN_Implementation
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Implementation of Matthew D. Zeiler's Paper "Visualizing and Understanding Convolutional Neural Networks" [https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf]

When I run your code with keras 2.0.8 tensorflow 1.4.1, I get some problem: tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension 0 in both shapes must be equal, but are 3 and 384 for 'Assign_6'...
When i run alexnet.py, the input is an image of 'elephant', the output is 'cockroach', the "imagenet_class_index.json" is ILSVRC2012?
I was wondering if the deconv filters are trainable, or they are just transpose of the (pretrained) conv filters? If the latter, are we assuming the filters are unitary?
deconvolution.py about Line 164 # Set other layers to zero new_array = np.zeros_like(self.array) new_array[0, self.f - 1] = self.array[0, self.f - 1] # Set other activations in same layer to...
Could you please help me solve it?