def forward(self, inputs, **kwargs): key = 'batch_size' if key in kwargs: batch_size = kwargs[key] else: raise ValueError('batch_size not specified') z = self.pre_forward(inputs, batch_size) # z = super().forward(z) z = T.tanh(z + self.b.dimshuffle('x', 0, 'x', 'x')) # z = z.flatten(2) # reshape to 2d return z
def core_func(z): return T.tanh(z)