gradient_based.py 文件源码

def _init_explain_function(self, patterns=None, **kwargs):
        with umisc.ignore_sigmoids(self.output_layer) as output_layer:
            Y = L.get_output(output_layer, deterministic=True)
        X = self.input_layer.input_var  # original
        I = T.iscalar()  # Output neuron
        S = T.iscalar()  # Sample that is desired
        E = T.grad(Y[S].flatten()[I], X)
        self.grad_function = theano.function(inputs=[X, S, I], outputs=E)