

def fprop(self, inputs, inference=False, beta=0.0):
    "\n        TODO:  Handle final layers that don't own their own outputs (bias, activation)\n        "
    x = inputs
    if inference:
        inference_revert_list = []
    for l in self.layers:
        l.revert_list = []
        altered_tensor = l.be.distribute_data(x, l.parallelism)
        if altered_tensor:
            if inference:
                inference_revert_list.append((l, altered_tensor))
            else:
                l.revert_list.append(altered_tensor)
        if ((l is self.layers[(- 1)]) and (beta != 0)):
            x = l.fprop(x, inference, beta=beta)
        else:
            x = l.fprop(x, inference)
    if inference:
        for (layer, tensor) in inference_revert_list:
            layer.be.revert_tensor(tensor)
    return x

dataset/ETHPy150Open NervanaSystems/neon/neon/layers/container.py Sequential.fprop/original