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Python NervanaGPU.exp方法代码示例

本文整理汇总了Python中nervanagpu.NervanaGPU.exp方法的典型用法代码示例。如果您正苦于以下问题:Python NervanaGPU.exp方法的具体用法?Python NervanaGPU.exp怎么用?Python NervanaGPU.exp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.exp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 exp [as 别名]

#.........这里部分代码省略.........
        self.start = drv.Event()
        self.end = drv.Event()
        self.flop_timer = FlopsDecorator(self)
        self.flop_timer.decorate(decorate_fc=decorate_fc,
                                 decorate_conv=decorate_conv,
                                 decorate_ew=decorate_ew)

    def flop_timinig_start(self):
        """
        Start a new FLOP timer.
        Returns:
            None: dummy value (not used)
        """
        return self.start.record()

    def flop_timing_finish(self, start_time):
        """
        Complete current FLOP timing.

        Arguments:
            start_time (unused): ignored.

        Returns:
            float: elapsed time in seconds since prior flop_timing_start call.
        """
        self.end.record()
        self.end.synchronize()
        return self.end.time_since(self.start)

    def uniform(self, low=0.0, high=1.0, shape=1, dtype=default_dtype,
                persist_values=True, name=None, allocator=drv.mem_alloc):
        """
        generate numpy random number and convert to a GPUTensor.
        If called with dype=None it will probably explode
        """
        ary = np.random.uniform(low, high, shape)
        return GPUTensor(ary.shape, dtype, allocator=allocator, name=name,
                         rounding=self.ng.round_mode).set(ary)

    def normal(self, loc=0.0, scale=1.0, size=1, dtype=default_dtype,
               persist_values=True, name=None, allocator=drv.mem_alloc):
        """
        Gaussian/Normal random number sample generation
        """
        ary = np.random.normal(loc, scale, size)
        return GPUTensor(ary.shape, dtype, allocator=allocator, name=name,
                         rounding=self.ng.round_mode).set(ary)

    def fprop_fc(self, out, inputs, weights, layer=None):
        """
        Forward propagate the inputs of a fully connected network layer to
        produce output pre-activations (ready for transformation by an
        activation function).

        Arguments:
            out (GPUTensor): Where to store the forward propagated results.
            inputs (GPUTensor): Will be either the dataset input values (first
                                layer), or the outputs from the previous layer.
            weights (GPUTensor): The weight coefficient values for this layer.
            layer (Layer): The layer object.
        """
        self.ng.dot(weights, inputs, out)

    def bprop_fc(self, out, weights, deltas, layer=None):
        """
        Backward propagate the error through a fully connected network layer.
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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