CIFAR10 ( root = './data', train = False, download = True, transform = transform ) testloader = torch. DataLoader ( trainset, batch_size = 4, shuffle = True, num_workers = 2 ) testset = torchvision. CIFAR10 ( root = './data', train = True, download = True, transform = transform ) trainloader = torch. Import torchvision import ansforms as transforms # torchvision数据集的输出是在范围内的PILImage图片。 # 我们此处使用归一化的方法将其转化为Tensor,数据范围为 transform = transforms. backward () print ( ' after backward' ) print ( net. zero_grad () # 归零操作 print ( ' before backward' ) print ( net. previous_functions ) # Linear print ( loss. # For illustration, let us follow a few steps backward print ( loss. parameters ()) print ( len ( params )) print ( params. size () # all dimensions except the batch dimension num_features = 1 for s in size : num_features *= s return num_features net = Net () net '''神经网络的输出结果是这样的 fc3 ( x ) return x def num_flat_features ( self, x ): size = x. conv2 ( x )), 2 ) # If the size is a square you can only specify a single number x = x. conv1 ( x )), ( 2, 2 )) # Max pooling over a (2, 2) window x = F. Linear ( 84, 10 ) def forward ( self, x ): x = F. Linear ( 16 * 5 * 5, 120 ) # an affine operation: y = Wx + b self. Conv2d ( 1, 6, 5 ) # 1 input image channel, 6 output channels, 5x5 square convolution kernel self. Module ): def _init_ ( self ): super ( Net, self ). Import torch.nn as nn import torch.nn.functional as F class Net ( nn. add_ ( 1 ) print ( a ) print ( b ) # 将numpy的Array转换为torch的Tensor import numpy as np a = np.
All rights of reproduction in any form reserved.)/Type/Annot/AP 1362 0 R>endobj1362 0 objendobj1363 0 obj/ProcSet>/BBox[0 0 489.344 0 objendobj1365 0 objendobj1366 0 obj 288endobj1367 0 objstream Maybeck, copyright � 1979 by Academic Press, reproduced by permission of the publisher. Chapter 1, "Introduc tion&qu\ot from STOCHASTIC MODELS, ESTIMATION, AND CONTROL, Volume 1, by Peter S.