def mix():return x**2,x**3,t=mix(3)
时间: 2023-08-01 11:14:14 浏览: 51
这段代码存在语法错误。如果想要定义一个函数mix,使其接受一个参数x,并返回x的平方和立方,可以这样写:
```
def mix(x):
return x**2, x**3
result = mix(3)
print(result) # 输出:(9, 27)
```
在调用函数后,result变量会被赋值为(9, 27),这是一个元组,可以通过索引访问元组中的每一个值,如result[0]表示平方的结果,result[1]表示立方的结果。
相关问题
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0 and scale_by_keep: random_tensor.div_(keep_prob) return x * random_tensor
这段代码是一个用于实现Stochastic Depth(随机深度)的函数。Stochastic Depth是一种在残差网络主路径中应用的路径丢弃技术。
函数的输入参数包括:
- `x`:输入张量。
- `drop_prob`:丢弃概率,用于控制丢弃的路径比例。
- `training`:是否处于训练模式,用于控制是否应用路径丢弃。
- `scale_by_keep`:是否根据保留概率进行缩放。
函数的返回值是经过路径丢弃处理后的张量。
具体实现中,首先判断`drop_prob`是否为0或者是否处于非训练模式,如果是,则直接返回输入张量`x`,不进行路径丢弃操作。
然后,计算保留概率`keep_prob`,即1减去丢弃概率`drop_prob`。
接着,根据输入张量`x`的形状创建一个与其相同形状的随机张量`random_tensor`,其中的元素服从伯努利分布,并且取值为1的概率为保留概率`keep_prob`。
如果保留概率大于0且`scale_by_keep`为True,则对随机张量进行缩放,将其除以保留概率`keep_prob`。
最后,将输入张量`x`与随机张量`random_tensor`相乘,得到经过路径丢弃处理后的张量,并返回。
总的来说,这段代码实现了Stochastic Depth中的路径丢弃操作,可以根据丢弃概率在训练过程中随机丢弃一部分路径,从而增强模型的鲁棒性和泛化能力。
def drop_path(x, drop_prob: float = 0., training: bool = False): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output 详细讲解一下
这是一个函数,用于在残差块的主路径上每个样本中丢弃路径(随机深度)。如果在EfficientNet等网络中使用,则与我创建的DropConnect实现相同,但原始名称会导致误解,因为“Drop Connect”是另一篇论文中不同形式的丢失连接。详见讨论:https://github.com/tensorflow/tpu/issues/494. 其中,x为输入的张量,drop_prob为float类型,表示丢弃概率,默认值为0,training为布尔类型,表示是否训练,默认为False。
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