9f2a83c4ce192da805e18617ce2421bb80cfe8fe,snntoolbox/core/inisim.py,SpikeConv2DReLU,get_output,#SpikeConv2DReLU#Any#,220

Before Change

        else:
            if border_mode == "same":
                border_mode = "full"
            conv_out = T.nnet.conv2d(inp, self.get_weights()[0],
                                     border_mode=border_mode,
                                     subsample=self.subsample,
                                     filter_flip=self.filter_flip)

After Change

        inp, time, updates = get_input(self)

        // Modify parameters if firing rate of layer too low
        self.fac = theano.ifelse.ifelse(
            T.eq(time / settings["dt"] % 10, 0) *
            T.gt(self.max_spikerate, 0.1) *
            T.gt(1 / settings["dt"] - self.max_spikerate, epsilon),
            1 / (self.max_spikerate + 0.001), 1.0)
        updates.append((self.W, self.W * self.fac))
        updates.append((self.b, self.b * self.fac))

        // CALCULATE SYNAPTIC SUMMED INPUT

Instances