elemwise.py 文件源码

def grad(self, inp, grads):
        x, = inp
        gz, = grads
        gz = as_tensor_variable(gz)
        grad_order = ['x'] * len(x.type.broadcastable)
        for i, v in enumerate(self.new_order):
            if v != 'x':
                grad_order[v] = i
        # Do not make the DimShuffle inplace as an optimization at the
        # canonicalization optimization phase will remove the inplace.
        # The inplace will be reintroduced automatically later in the graph.
        if 'int' in inp[0].dtype:
            return [inp[0].zeros_like(dtype=theano.config.floatX)]
        else:
            return [DimShuffle(gz.type.broadcastable, grad_order)(
                Elemwise(scalar.identity)(gz))]