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Python NervanaGPU.dropout方法代码示例

本文整理汇总了Python中nervanagpu.NervanaGPU.dropout方法的典型用法代码示例。如果您正苦于以下问题:Python NervanaGPU.dropout方法的具体用法?Python NervanaGPU.dropout怎么用?Python NervanaGPU.dropout使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在nervanagpu.NervanaGPU的用法示例。


在下文中一共展示了NervanaGPU.dropout方法的1个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: GPU

# 需要导入模块: from nervanagpu import NervanaGPU [as 别名]
# 或者: from nervanagpu.NervanaGPU import dropout [as 别名]

#.........这里部分代码省略.........
    def softmax(self, x, out):
        """
        Softmax nonlinearity. Computes exp(x-max(x)) / sum_i exp(x_i-max(x_i))

        Arguments:
            x (GPUTensor): input tensor.
            out (GPUTensor): where the result will be stored.

        Returns:
            GPUTensor: reference to out
        """
        out[:] = (self.ng.reciprocal(self.ng.sum(
                  self.ng.exp(x - self.ng.max(x, axis=0)), axis=0)) *
                  self.ng.exp(x - self.ng.max(x, axis=0)))
        return out

    def softmax_gradient(self, y, err, out):
        """
        Gradient of the softmax nonlinearity.

        Arguments:
            y (GPUTensor): input tensor.
            err (GPUTensor): backpropagated error.
            out (GPUTensor): where the result will be stored.

        Returns:
            GPUTensor: reference to out
        """
        raise NotImplementedError("Softmax gradient should use shortcut")
        return out

    def make_binary_mask(self, tsr, keepthresh=0.5, dtype=default_dtype):
        """
        Create a binary mask for dropout layers.

        Arguments:
            tsr (GPUTensor): Output tensor
            keepthresh (float): fraction of ones
        """
        self.ng.dropout(keep=keepthresh, out=tsr)

    def gdm_compound(self, ps_item, us_item, vs_item, momentum_coef,
                     learning_rate, epoch):
        """
        Perform gradient descent update with momentum.

        Arguments:
            ps_item (GPUTensor): parameter tensor (e.g. a weight matrix)
            us_item (GPUTensor): update tensor, contains gradient wrt. weights
            vs_item (GPUTensor): velocity tensor.
            momentum_coef (float): momentum coefficient.
            learning_rate (float): learning rate.
            epoch (int): epoch (used in conjunction with diagnostics).

        Outputs are written to vs_item (updated velocity)
        and ps_item (updated weights)
        """
        vs_item[:] = vs_item * momentum_coef - us_item * learning_rate
        ps_item[:] = ps_item + vs_item

    def gdmwd_compound(self, ps_item, us_item, vs_item, momentum_coef,
                       learning_rate, wd, epoch):
        """
        Perform gradient descent update with momentum and weight decay.

        Arguments:
开发者ID:YouVentures,项目名称:neon,代码行数:70,代码来源:gpu.py


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