guided_backprop.py

class ModifiedBackprop(object):

    def __init__(self, nonlinearity):
        self.nonlinearity = nonlinearity
        self.ops = {}  # memoizes an OpFromGraph instance per tensor type

    def __call__(self, x):
        # OpFromGraph is oblique to Theano optimizations, so we need to move
        # things to GPU ourselves if needed.
        if theano.sandbox.cuda.cuda_enabled:
            maybe_to_gpu = theano.sandbox.cuda.as_cuda_ndarray_variable
        else:
            maybe_to_gpu = lambda x: x
        # We move the input to GPU if needed.
        x = maybe_to_gpu(x)
        # We note the tensor type of the input variable to the nonlinearity
        # (mainly dimensionality and dtype); we need to create a fitting Op.
        tensor_type = x.type
        # If we did not create a suitable Op yet, this is the time to do so.
        if tensor_type not in self.ops:
            # For the graph, we create an input variable of the correct type:
            inp = tensor_type()
            # We pass it through the nonlinearity (and move to GPU if needed).
            outp = maybe_to_gpu(self.nonlinearity(inp))
            # Then we fix the forward expression...
            op = theano.OpFromGraph([inp], [outp])
            # ...and replace the gradient with our own (defined in a subclass).
            op.grad = self.grad
            # Finally, we memoize the new Op
            self.ops[tensor_type] = op
        # And apply the memoized Op to the input we got.
        return self.ops[tensor_type](x)
class GuidedBackprop(ModifiedBackprop):
    def grad(self, inputs, out_grads):
        (inp,) = inputs
        (grd,) = out_grads
        dtype = inp.dtype
        return (grd * (inp > 0).astype(dtype) * (grd > 0).astype(dtype),)
modded_relu = GuidedBackprop(keras.activations.relu)  # important: only instantiate this once!
for layer in model.layers:
    if 'activation' in layer.get_config() and layer.get_config()['activation'] == 'relu':
        layer.activation = modded_relu
        # layer.activation = theano.function([],[])