解释这段代码的意思Neck_Fz_sim = np.loadtxt("Neck Lower Fz_sim.txt", delimiter=",", ) Neck_Fz_sled = np.loadtxt("Neck Lower Fz_sled140ms.txt", delimiter=",", ) from objective_rating_metrics.rating import ISO18571 iso_rating = ISO18571(reference_curve=Neck_Fz_sled_sim, comparison_curve=Neck_Fz_sim_140ms) overall_rating = iso_rating.overall_rating() print(str(overall_rating)) print('overall_rating: ', iso_rating.overall_rating()) print('corridor_rating: ', iso_rating.corridor_rating()) print('phase_rating: ', iso_rating.phase_rating()) print('magnitude_rating: ', iso_rating.magnitude_rating()) print('slope_rating: ', iso_rating.slope_rating()) x_data_sim = Neck_Fz_sim[...,0] y_data_sim = Neck_Fz_sim[...,1] x_data_sled = Neck_Fz_sled[...,0] y_data_sled = Neck_Fz_sled[...,1] plt.plot(x_data_sim,y_data_sim,linewidth=2,label = 'simulation') plt.plot(x_data_sled,y_data_sled,linewidth=2,label = 'sled') plt.fill_between(x_data_sled,y_data_sled + max(np.abs(y_data_sled) * 0.05),y_data_sled - max(np.abs(y_data_sled) * 0.05), color='g', alpha=0.2) plt.fill_between(x_data_sled,y_data_sled + max(np.abs(y_data_sled) * 0.5),y_data_sled - max(np.abs(y_data_sled) * 0.5), color='y', alpha=0.2) plt.text(0,1500,'overall_rating: \roverall_rating\ncorridor_rating:\nphase_rating:\nmagnitude_rating:\nslope_rating:',fontsize = 10) plt.title("Neck Lower Fz") plt.xlabel("Time(s)") plt.ylabel("Force(N)") plt.show()

MCU.rar_LPC2124 PWM_bottleneck

【MCU.rar_LPC2124_PWM_bottleneck】是一个关于微控制器LPC2124使用PWM（脉宽调制）技术的资源压缩包，旨在帮助开发者深入理解和优化单片机应用中的性能瓶颈。LPC2124是NXP公司生产的一款基于ARM7TDMI内核的微控制器...

企业存储的市场细分-Solid State Disk(SZ).rar_Solid State Disk_bottleneck

而SSD则通过并行处理多个I/O请求，实现几乎无延迟的读写操作，极大地缓解了这一问题，从而提升了系统的整体吞吐量和响应时间。其次，SSD在企业存储市场的细分体现在其多种形态和应用场景。例如，企业可以选择使用...

解释这段代码的意思import numpy as np import matplotlib.pyplot as plt Neck_Fz_sim = np.loadtxt("Neck Lower Fz_sim.txt", delimiter=",",) Neck_Fz_sled = np.loadtxt("Neck Lower Fz_sled140ms.txt", delimiter=",",) from objective_rating_metrics.rating import ISO18571 iso_rating = ISO18571(reference_curve=Neck_Fz_sled, comparison_curve=Neck_Fz_sim) overall_rating = iso_rating.overall_rating() print(str(overall_rating)) print('overall_rating: ', iso_rating.overall_rating()) print('corridor_rating: ', iso_rating.corridor_rating()) print('phase_rating: ', iso_rating.phase_rating()) print('magnitude_rating: ', iso_rating.magnitude_rating()) print('slope_rating: ', iso_rating.slope_rating()) x_data_sim = Neck_Fz_sim[...,0] y_data_sim = Neck_Fz_sim[...,1] x_data_sled = Neck_Fz_sled[...,0] y_data_sled = Neck_Fz_sled[...,1] plt.plot(x_data_sim,y_data_sim,linewidth=2,label = 'simulation') plt.plot(x_data_sled,y_data_sled,linewidth=2,label = 'sled') plt.fill_between(x_data_sled,y_data_sled + max(np.abs(y_data_sled) * 0.05),y_data_sled - max(np.abs(y_data_sled) * 0.05), color='g', alpha=0.2) plt.fill_between(x_data_sled,y_data_sled + max(np.abs(y_data_sled) * 0.5),y_data_sled - max(np.abs(y_data_sled) * 0.5), color='y', alpha=0.2) plt.text(0,1500,'overall_rating: \roverall_rating\ncorridor_rating:\nphase_rating:\nmagnitude_rating:\nslope_rating:',fontsize = 10) plt.title("Neck Lower Fz") plt.xlabel("Time(s)") plt.ylabel("Force(N)") plt.show()

代码首先使用NumPy中的loadtxt函数从两个文件中加载数据，并将其分别存储在Neck_Fz_sim和Neck_Fz_sled变量中。接着，使用objective_rating_metrics包中的ISO18571类来计算仿真数据和实验数据之间的差异，并计算了五...

解释这段代码if use_bottleneck == True: self.bottleneck = nn.Sequential( nn.Linear(n_hiddens[-1], bottleneck_width), nn.Linear(bottleneck_width, bottleneck_width), nn.BatchNorm1d(bottleneck_width), nn.ReLU(), nn.Dropout(), ) self.bottleneck[0].weight.data.normal_(0, 0.005) self.bottleneck[0].bias.data.fill_(0.1) self.bottleneck[1].weight.data.normal_(0, 0.005) self.bottleneck[1].bias.data.fill_(0.1) self.fc = nn.Linear(bottleneck_width, n_output) torch.nn.init.xavier_normal_(self.fc.weight) else: self.fc_out = nn.Linear(n_hiddens[-1], self.n_output)

这段代码是一个条件语句，用于根据use_bottleneck的值来创建模型中的不同层。如果use_bottleneck为True，代码块中的内容将被执行。首先，创建一个nn.Sequential的模块self.bottleneck，该模块按顺序包含了几个层：...

if use_bottleneck == True: self.bottleneck = nn.Sequential( nn.Linear(n_hiddens[-1], bottleneck_width), nn.Linear(bottleneck_width, bottleneck_width), nn.BatchNorm1d(bottleneck_width), nn.ReLU(), nn.Dropout(), ) self.bottleneck[0].weight.data.normal_(0, 0.005) self.bottleneck[0].bias.data.fill_(0.1) self.bottleneck[1].weight.data.normal_(0, 0.005) self.bottleneck[1].bias.data.fill_(0.1) self.fc = nn.Linear(bottleneck_width, n_output) torch.nn.init.xavier_normal_(self.fc.weight) else: self.fc_out = nn.Linear(n_hiddens[-1], self.n_output)

这段代码是在模型中添加bottleneck层和全连接层。如果use_bottleneck为True，则会创建一个包含线性层、批归一化层、激活函数层和Dropout层的Sequential模块，并将其赋值给self.bottleneck。同时，还会创建一个线性层...

逐行解释代码： def forward_Boosting(self, x, weight_mat=None): out = self.gru_features(x) fea = out[0] if self.use_bottleneck: fea_bottleneck = self.bottleneck(fea[:, -1, :]) fc_out = self.fc(fea_bottleneck).squeeze() else: fc_out = self.fc_out(fea[:, -1, :]).squeeze() out_list_all = out[1] out_list_s, out_list_t = self.get_features(out_list_all) loss_transfer = torch.zeros((1,)).cuda() if weight_mat is None: weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda() else: weight = weight_mat dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda() for i in range(len(out_list_s)): criterion_transder = TransferLoss( loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2]) for j in range(self.len_seq): loss_trans = criterion_transder.compute( out_list_s[i][:, j, :], out_list_t[i][:, j, :]) loss_transfer = loss_transfer + weight[i, j] * loss_trans dist_mat[i, j] = loss_trans return fc_out, loss_transfer, dist_mat, weight

逐行解释这段代码： 1. def forward_Boosting(self, x, weight_mat=None):：这是一个方法定义，接受输入张量x和权重矩阵weight_mat（可选参数）作为输入。 2. out = self.gru_features(x)：将输入张量x...

def forward_Boosting(self, x, weight_mat=None): out = self.gru_features(x) fea = out[0] if self.use_bottleneck: fea_bottleneck = self.bottleneck(fea[:, -1, :]) fc_out = self.fc(fea_bottleneck).squeeze() else: fc_out = self.fc_out(fea[:, -1, :]).squeeze() out_list_all = out[1] out_list_s, out_list_t = self.get_features(out_list_all) loss_transfer = torch.zeros((1,)).cuda() if weight_mat is None: weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda() else: weight = weight_mat dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda() for i in range(len(out_list_s)): criterion_transder = TransferLoss( loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2]) for j in range(self.len_seq): loss_trans = criterion_transder.compute( out_list_s[i][:, j, :], out_list_t[i][:, j, :]) loss_transfer = loss_transfer + weight[i, j] * loss_trans dist_mat[i, j] = loss_trans return fc_out, loss_transfer, dist_mat, weight

这个方法是用于模型的前向传播过程，实现了Boosting机制。它接收输入张量x和权重矩阵weight_mat（可选参数）作为输入。首先，通过self.gru_features对输入张量x进行特征提取，得到一个输出张量out。其中...

class FuturePrediction(torch.nn.Module): def init(self, in_channels, latent_dim, n_gru_blocks=3, n_res_layers=3): super().init() self.n_gru_blocks = n_gru_blocks # Convolutional recurrent model with z_t as an initial hidden state and inputs the sample # from the probabilistic model. The architecture of the model is: # [Spatial GRU - [Bottleneck] x n_res_layers] x n_gru_blocks self.spatial_grus = [] self.res_blocks = [] for i in range(self.n_gru_blocks): gru_in_channels = latent_dim if i == 0 else in_channels self.spatial_grus.append(SpatialGRU(gru_in_channels, in_channels)) self.res_blocks.append(torch.nn.Sequential([Bottleneck(in_channels) for _ in range(n_res_layers)])) self.spatial_grus = torch.nn.ModuleList(self.spatial_grus) self.res_blocks = torch.nn.ModuleList(self.res_blocks) def forward(self, x, hidden_state): # x has shape (b, n_future, c, h, w), hidden_state (b, c, h, w) for i in range(self.n_gru_blocks): x = self.spatial_grus[i](x, hidden_state, flow=None) b, n_future, c, h, w = x.shape x = self.res_blocks[i](x.view(b n_future, c, h, w)) x = x.view(b, n_future, c, h, w) return x是什么意思？

这是一个基于卷积循环神经网络的模型，用于进行未来预测。该模型包括若干个 SpatialGRU 模块和若干个 Bottleneck 模块，可以接受输入 x 和初始隐藏状态 hidden_state，输出预测结果 x。其中，x 的形状为 (b, n_...

为以下的每句代码做注释：class ResNet(nn.Module): def init(self, block, blocks_num, num_classes=1000, include_top=True): super(ResNet, self).init() self.include_top = include_top self.in_channel = 64 self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(self.in_channel) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, blocks_num[0]) self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) if self.include_top: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') def _make_layer(self, block, channel, block_num, stride=1): downsample = None if stride != 1 or self.in_channel != channel * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(channel * block.expansion)) layers = [] layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride)) self.in_channel = channel * block.expansion for _ in range(1, block_num): layers.append(block(self.in_channel, channel)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x

这段代码定义了一个名为 ResNet 的类，继承自 nn.Module 类。ResNet 是一个深度卷积神经网络模型，常用于图像分类任务。在 __init__ 方法中，首先定义了一些基本参数： - block：指定 ResNet 中的基本块类型，如 ...

class TemporalModel(nn.Module): def init( self, in_channels, receptive_field, input_shape, start_out_channels=64, extra_in_channels=0, n_spatial_layers_between_temporal_layers=0, use_pyramid_pooling=True): super().init() self.receptive_field = receptive_field n_temporal_layers = receptive_field - 1 h, w = input_shape modules = [] block_in_channels = in_channels block_out_channels = start_out_channels for _ in range(n_temporal_layers): if use_pyramid_pooling: use_pyramid_pooling = True pool_sizes = [(2, h, w)] else: use_pyramid_pooling = False pool_sizes = None temporal = TemporalBlock( block_in_channels, block_out_channels, use_pyramid_pooling=use_pyramid_pooling, pool_sizes=pool_sizes, ) spatial = [ Bottleneck3D(block_out_channels, block_out_channels, kernel_size=(1, 3, 3)) for _ in range(n_spatial_layers_between_temporal_layers) ] temporal_spatial_layers = nn.Sequential(temporal, spatial) modules.extend(temporal_spatial_layers) block_in_channels = block_out_channels block_out_channels += extra_in_channels self.out_channels = block_in_channels self.model = nn.Sequential(modules) def forward(self, x): # Reshape input tensor to (batch, C, time, H, W) x = x.permute(0, 2, 1, 3, 4) x = self.model(x) x = x.permute(0, 2, 1, 3, 4).contiguous() return x[:, (self.receptive_field - 1):]是如何一步步前向传播的？

首先，输入张量x的形状为(batch_size, in_channels, sequence_length, height, width)。然后，我们将x的维度从(sequence_length, batch_size, in_channels, height, width)改变为(batch_size, sequence_length, in...

为以下每句代码做注释：class Bottleneck(nn.Module): expansion = 4 def init(self, in_channel, out_channel, stride=1, downsample=None): super(Bottleneck, self).init() self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=1, stride=1, bias=False) # squeeze channels self.bn1 = nn.BatchNorm2d(out_channel) self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=stride, bias=False, padding=1) self.bn2 = nn.BatchNorm2d(out_channel) self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel * self.expansion, kernel_size=1, stride=1, bias=False) # unsqueeze channels self.bn3 = nn.BatchNorm2d(out_channel * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += identity out = self.relu(out) return out

这段代码定义了一个名为Bottleneck的类，继承自nn.Module类。该类的作用是搭建一个ResNet中的Bottleneck块，用于深度卷积神经网络。 expansion = 4 表示Bottleneck块中输出通道数是输入通道数的4倍。 init函数是...

请分析这段代码class GhostBottleneck(nn.Module): """ Ghost bottleneck w/ optional SE""" def init(self, in_chs, mid_chs, out_chs, dw_kernel_size=3, stride=1, act_layer=nn.ReLU, se_ratio=0.): super(GhostBottleneck, self).init() has_se = se_ratio is not None and se_ratio > 0. self.stride = stride # Point-wise expansion self.ghost1 = GhostModule(in_chs, mid_chs, relu=True) # Depth-wise convolution if self.stride > 1: self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride, padding=(dw_kernel_size - 1) // 2, groups=mid_chs, bias=False) self.bn_dw = nn.BatchNorm2d(mid_chs) # Squeeze-and-excitation if has_se: self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio) else: self.se = None # Point-wise linear projection self.ghost2 = GhostModule(mid_chs, out_chs, relu=False) # shortcut if (in_chs == out_chs and self.stride == 1): self.shortcut = nn.Sequential() else: self.shortcut = nn.Sequential( nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride, padding=(dw_kernel_size - 1) // 2, groups=in_chs, bias=False), nn.BatchNorm2d(in_chs), nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False), nn.BatchNorm2d(out_chs), ) def forward(self, x): residual = x # 1st ghost bottleneck x = self.ghost1(x) # Depth-wise convolution if self.stride > 1: x = self.conv_dw(x) x = self.bn_dw(x) # Squeeze-and-excitation if self.se is not None: x = self.se(x) # 2nd ghost bottleneck x = self.ghost2(x) x += self.shortcut(residual) return x

这段代码定义了一个名为GhostBottleneck的类，继承自nn.Module。该类实现了一个带有可选Squeeze-and-excitation (SE)的Ghost bottleneck。在初始化方法中，它接受一些参数，包括输入通道数（in_chs）、中间通道数...

class BasicBlock(nn.Module): expansion = 1 def init(self, in_channels, out_channels, stride=1, downsample=None): super(BasicBlock, self).init() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels , kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU() self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.downsample = downsample

这是一个PyTorch中定义ResNet的BasicBlock的代码。BasicBlock是ResNet中的基本残差块，包含两个卷积层和一个跳跃连接。参数in_channels和out_channels分别表示输入通道数和输出通道数。stride表示卷积核的步长，down...

class Bottleneck(nn.Module): expansion = 4 def init(self, inplanes, planes, stride=1, downsample=None, rate=1): super(Bottleneck, self).init() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=rate, dilation=rate, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride解释

这段代码定义了一个 ResNet 中的 Bottleneck 模块，它是由三个卷积层组成的。第一个卷积层使用 1x1 的卷积核将输入通道数 inplanes 转换为 planes，第二个卷积层使用 3x3 的卷积核进行卷积操作，并且可能会进行下...

叙述下列代码的实现流程class Bottlrneck(torch.nn.Module): def init(self,In_channel,Med_channel,Out_channel,downsample=False): super(Bottlrneck, self).init() self.stride = 1 if downsample == True: self.stride = 2 self.layer = torch.nn.Sequential( torch.nn.Conv1d(In_channel, Med_channel, 1, self.stride), torch.nn.BatchNorm1d(Med_channel), torch.nn.ReLU(), torch.nn.Conv1d(Med_channel, Med_channel, 3, padding=1), torch.nn.BatchNorm1d(Med_channel), torch.nn.ReLU(), torch.nn.Conv1d(Med_channel, Out_channel, 1), torch.nn.BatchNorm1d(Out_channel), torch.nn.ReLU(), ) if In_channel != Out_channel: self.res_layer = torch.nn.Conv1d(In_channel, Out_channel,1,self.stride) else: self.res_layer = None def forward(self,x): if self.res_layer is not None: residual = self.res_layer(x) else: residual = x return self.layer(x)+residual

这段代码实现了一个 Bottleneck 模块，用于卷积神经网络中的特征提取。它包含了三个 1x1、3x3、1x1 的卷积层，以及对应的 Batch Normalization 和 ReLU 激活函数。在初始化时，输入参数包括输入通道数 In_channel...

解释class Bottlrneck(torch.nn.Module): def init(self,In_channel,Med_channel,Out_channel,downsample=False): super(Bottlrneck, self).init() self.stride = 1 if downsample == True: self.stride = 2 self.layer = torch.nn.Sequential( torch.nn.Conv1d(In_channel, Med_channel, 1, self.stride), torch.nn.BatchNorm1d(Med_channel), torch.nn.ReLU(), torch.nn.Conv1d(Med_channel, Med_channel, 3, padding=1), torch.nn.BatchNorm1d(Med_channel), torch.nn.ReLU(), torch.nn.Conv1d(Med_channel, Out_channel, 1), torch.nn.BatchNorm1d(Out_channel), torch.nn.ReLU(), ) if In_channel != Out_channel: self.res_layer = torch.nn.Conv1d(In_channel, Out_channel,1,self.stride) else: self.res_layer = None def forward(self,x): if self.res_layer is not None: residual = self.res_layer(x) else: residual = x return self.layer(x)+residual

这是一个使用 1x1、3x3、1x1 卷积核的瓶颈块(Bottleneck Block)。它的作用是减少参数数量并增加网络深度，同时减少梯度消失问题。具体来说，它的结构如下： - 输入 In_channel 经过一个 1x1 的卷积核，输出通道数...

def init(self, in_channel, out_channel, stride=1, downsample=None, groups=1, width_per_group=64): super(Bottleneck, self).init() width = int(out_channel * (width_per_group / 64.)) * groups self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, kernel_size=1, stride=1, bias=False) # squeeze channels self.bn1 = nn.BatchNorm2d(width) self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups, kernel_size=3, stride=stride, bias=False, padding=1) self.bn2 = nn.BatchNorm2d(width) self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channelself.expansion, kernel_size=1, stride=1, bias=False) # unsqueeze channels self.bn3 = nn.BatchNorm2d(out_channelself.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample

这段代码是一个 Bottleneck 模块的初始化函数。Bottleneck 模块是 ResNet 中的一种基本模块，在卷积神经网络中用于增加网络的深度和宽度。该模块包含三个卷积层，其中第一个和第三个卷积层是 1x1 卷积层，用于减少和...

解释一下 with tf.name_scope('final_training_ops'): weights = tf.Variable( tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, n_classes], stddev=0.1))

这段代码使用了 TensorFlow 的名字作用域（name scope）机制，将变量 weights 和后续的操作都放在了名为 "final_training_ops" 的名字作用域中。其中，weights 是一个形状为 [BOTTLENECK_TENSOR_SIZE, n_classes] ...

class ResNetRGBD(nn.Module): def init(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None): super(ResNetRGBD, self).init() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1

这是一个 PyTorch 中的 ResNetRGBD 模型类的定义，它继承自 nn.Module 类。在初始化函数 __init__ 中，它接受以下参数： - block：ResNet 中残差块的类（比如 BasicBlock 或 Bottleneck）。 - layers：一个...

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解释一下 with tf.name_scope('final_training_ops'): weights = tf.Variable( tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, n_classes], stddev=0.1))

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