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def counterfact_ipw(self, loss_cvr, ctr_num, O, ctr_out_one): PS = paddle.multiply( ctr_out_one, paddle.cast( ctr_num, dtype="float32")) min_v = paddle.full_like(PS, 0.000001) PS = paddle.maximum(PS, min_v) IPS = paddle.reciprocal(PS) batch_shape = paddle.full_like(O, 1) batch_size = paddle.sum(paddle.cast( batch_shape, dtype="float32"), axis=0) #TODO this shoud be a hyparameter IPS = paddle.clip(IPS, min=-15, max=15) #online trick IPS = paddle.multiply(IPS, batch_size) IPS.stop_gradient = True loss_cvr = paddle.multiply(loss_cvr, IPS) loss_cvr = paddle.multiply(loss_cvr, O) return paddle.mean(loss_cvr)
IPW方法为什么要乘batch_size,论文中也没发现需要做这个操作?IPS = paddle.multiply(IPS, batch_size)
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IPW方法为什么要乘batch_size,论文中也没发现需要做这个操作?IPS = paddle.multiply(IPS, batch_size)
The text was updated successfully, but these errors were encountered: