torch.nn.``Dropout(p=0.5, inplace=False) During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution`. proven to be an effective technique for regularization and preventing the co-adaptation of neurons as described in the paper Improving neural networks by preventing co-adaptation of feature detectors . [docs]class Dropout(_DropoutNd): def forward(self, input: Tensor) -> Tensor: return F.dropout(input, self.p, self.training, self.inplace)

BN，LN，IN，GN从学术化上解释差异： BatchNorm：batch方向做归一化，算NHW的均值，对小batchsize效果不好；BN主

torch.nn.``Linear(in_features, out_features, bias=True) [docs]class Linear(Module): r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b` Examples:: >>> m = nn.Linear(20, 30) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 30]) """ __constants__ = ['in_features', 'out_features'] in_features: int out_features: int weight: Tensor def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None: super(Linear, self).__init__() self.in_features = in_features self.out_features = out_features self.weight =

统计学提供的是一套有关数据收集、处理、分析、解释并从数据中得出结论的方法；数据分析则是选择适当的统计方法研究数据，并从数据中提取有用信息进而

1. attributes ndarray.ndim： the number of axes (dimensions) of the array. ndarray.shape: For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim. ndarray.size: the total number of elements of the array. ndarray.dtype: numpy.int32, numpy.int16, and numpy.float64 create: np.ar

# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version` import serial, pynmea2, time, threading as td import logging logger = logging.getLogger(__name__) class ReadGPSData(td.Thread): """ Class to read the data off of the EC25-E's GPS module. Can be easily adapted to work with any other GPS module. """ def __init__(self, write_port, read_port, baudrate, name="GPS"): """ write_port - The serial