self.layers.append(Convolution(self.params['W1'], self.params['b1'], conv_param_1['stride'], conv_param_1['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['W2'], self.params['b2'], conv_param_2['stride'], conv_param_2['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Convolution(self.params['W3'], self.params['b3'], conv_param_3['stride'], conv_param_3['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['W4'], self.params['b4'], conv_param_4['stride'], conv_param_4['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Convolution(self.params['W5'], self.params['b5'], conv_param_5['stride'], conv_param_5['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['W6'], self.params['b6'], conv_param_6['stride'], conv_param_6['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Affine(self.params['W7'], self.params['b7'])) self.layers.append(Relu()) self.layers.append(Dropout(0.5)) self.layers.append(Affine(self.params['W8'], self.params['b8'])) self.layers.append(Dropout(0.5)) self.last_layer = SoftmaxWithLoss()

class TemporalBlock(nn.Module): """ Temporal block with the following layers: - 2x3x3, 1x3x3, spatio-temporal pyramid pooling - dropout - skip connection. """ def init(self, in_channels, out_channels=None, use_pyramid_pooling=False, pool_sizes=None): super().init() self.in_channels = in_channels self.half_channels = in_channels // 2 self.out_channels = out_channels or self.in_channels self.kernels = [(2, 3, 3), (1, 3, 3)] # Flag for spatio-temporal pyramid pooling self.use_pyramid_pooling = use_pyramid_pooling # 3 convolution paths: 2x3x3, 1x3x3, 1x1x1 self.convolution_paths = [] for kernel_size in self.kernels: self.convolution_paths.append( nn.Sequential( conv_1x1x1_norm_activated(self.in_channels, self.half_channels), CausalConv3d(self.half_channels, self.half_channels, kernel_size=kernel_size), ) ) self.convolution_paths.append(conv_1x1x1_norm_activated(self.in_channels, self.half_channels)) self.convolution_paths = nn.ModuleList(self.convolution_paths) agg_in_channels = len(self.convolution_paths) * self.half_channels if self.use_pyramid_pooling: assert pool_sizes is not None, "setting must contain the list of kernel_size, but is None." reduction_channels = self.in_channels // 3 self.pyramid_pooling = PyramidSpatioTemporalPooling(self.in_channels, reduction_channels, pool_sizes) agg_in_channels += len(pool_sizes) * reduction_channels # Feature aggregation self.aggregation = nn.Sequential( conv_1x1x1_norm_activated(agg_in_channels, self.out_channels),) if self.out_channels != self.in_channels: self.projection = nn.Sequential( nn.Conv3d(self.in_channels, self.out_channels, kernel_size=1, bias=False), nn.BatchNorm3d(self.out_channels), ) else: self.projection = None网络结构是什么？

- 3 个卷积路径: 2x3x3 卷积、1x3x3 卷积和 1x1x1 卷积 - dropout 层 - skip 连接 - 可选的 spatio-temporal pyramid pooling 层 - 最后是特征聚合和投影层（如果输入和输出通道数不同）其中，卷积路径通过 nn....

请详细解析一下python代码: import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def init(self): super(Net, self).init() self.conv1 = nn.Conv2d(3, 128, 5, padding=2) self.conv2 = nn.Conv2d(128, 128, 5, padding=2) self.conv3 = nn.Conv2d(128, 256, 3, padding=1) self.conv4 = nn.Conv2d(256, 256, 3, padding=1) self.pool = nn.MaxPool2d(2, 2) self.bn_conv1 = nn.BatchNorm2d(128) self.bn_conv2 = nn.BatchNorm2d(128) self.bn_conv3 = nn.BatchNorm2d(256) self.bn_conv4 = nn.BatchNorm2d(256) self.bn_dense1 = nn.BatchNorm1d(1024) self.bn_dense2 = nn.BatchNorm1d(512) self.dropout_conv = nn.Dropout2d(p=0.25) self.dropout = nn.Dropout(p=0.5) self.fc1 = nn.Linear(256 * 8 * 8, 1024) self.fc2 = nn.Linear(1024, 512) self.fc3 = nn.Linear(512, 10) def conv_layers(self, x): out = F.relu(self.bn_conv1(self.conv1(x))) out = F.relu(self.bn_conv2(self.conv2(out))) out = self.pool(out) out = self.dropout_conv(out) out = F.relu(self.bn_conv3(self.conv3(out))) out = F.relu(self.bn_conv4(self.conv4(out))) out = self.pool(out) out = self.dropout_conv(out) return out def dense_layers(self, x): out = F.relu(self.bn_dense1(self.fc1(x))) out = self.dropout(out) out = F.relu(self.bn_dense2(self.fc2(out))) out = self.dropout(out) out = self.fc3(out) return out def forward(self, x): out = self.conv_layers(x) out = out.view(-1, 256 * 8 * 8) out = self.dense_layers(out) return out net = Net() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print('Device:', device) net.to(device) num_params = sum(p.numel() for p in net.parameters() if p.requires_grad) print("Number of trainable parameters:", num_params)

在该网络中，conv_layers函数用于定义卷积层和Batch Normalization层，并使用了ReLU激活函数和最大池化层。dense_layers函数用于定义全连接层和dropout层，并同样使用ReLU激活函数。最后，forward函数将卷积层和全...

# New module: utils.pyimport torchfrom torch import nnclass ConvBlock(nn.Module): """A convolutional block consisting of a convolution layer, batch normalization layer, and ReLU activation.""" def init(self, in_chans, out_chans, drop_prob): super().init() self.conv = nn.Conv2d(in_chans, out_chans, kernel_size=3, padding=1) self.bn = nn.BatchNorm2d(out_chans) self.relu = nn.ReLU(inplace=True) self.dropout = nn.Dropout2d(p=drop_prob) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) x = self.dropout(x) return x# Refactored U-Net modelfrom torch import nnfrom utils import ConvBlockclass UnetModel(nn.Module): """PyTorch implementation of a U-Net model.""" def init(self, in_chans, out_chans, chans, num_pool_layers, drop_prob, pu_args=None): super().init() PUPS.init(self, pu_args) self.in_chans = in_chans self.out_chans = out_chans self.chans = chans self.num_pool_layers = num_pool_layers self.drop_prob = drop_prob # Calculate input and output channels for each ConvBlock ch_list = [chans] + [chans 2 ** i for i in range(num_pool_layers - 1)] in_chans_list = [in_chans] + [ch_list[i] for i in range(num_pool_layers - 1)] out_chans_list = ch_list[::-1] # Create down-sampling layers self.down_sample_layers = nn.ModuleList() for i in range(num_pool_layers): self.down_sample_layers.append(ConvBlock(in_chans_list[i], out_chans_list[i], drop_prob)) # Create up-sampling layers self.up_sample_layers = nn.ModuleList() for i in range(num_pool_layers - 1): self.up_sample_layers.append(ConvBlock(out_chans_list[i], out_chans_list[i + 1] // 2, drop_prob)) self.up_sample_layers.append(ConvBlock(out_chans_list[-1], out_chans_list[-1], drop_prob)) # Create final convolution layer self.conv2 = nn.Sequential( nn.Conv2d(out_chans_list[-1], out_chans_list[-1] // 2, kernel_size=1), nn.Conv2d(out_chans_list[-1] // 2, out_chans, kernel_size=1), nn.Conv2d(out_chans, out_chans, kernel_size=1), ) def forward(self, x): # Down-sampling path encoder_outs = [] for layer in self.down_sample_layers: x = layer(x) encoder_outs.append(x) x = nn.MaxPool2d(kernel_size=2)(x) # Bottom layer x = self.conv(x) # Up-sampling path for i, layer in enumerate(self.up_sample_layers): x = nn.functional.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) x = torch.cat([x, encoder_outs[-(i + 1)]], dim=1) x = layer(x) # Final convolution layer x = self.conv2(x) return x

1. 将 ConvBlock 提取到了 utils.py 中，以便在 U-Net 中复用。 2. 在 U-Net 模型的 forward 方法中，使用了 ModuleList 对 down-sampling 和 up-sampling 层进行了封装，使得代码更加简洁和易于扩展。 3. 使用了 ...

def attention_sublayers(self, feats, embedding_layers, latent): feats = feats.view((feats.size(0), self.k, -1)) feats = feats.transpose(dim0=1, dim1=2) feats = feats + latent.unsqueeze(1) feats = feats.transpose(dim0=1, dim1=2) feats = embedding_layers(feats).squeeze(-1) p = F.softmax(feats, dim=1) return p def forward(self, x): conv2_2 = self.conv2_2(x) conv3_4 = self.conv3_4(conv2_2) conv4_4 = self.conv4_4(conv3_4) conv5_4 = self.conv5_4(conv4_4) x = F.relu(self.fc4(self.fc_layers(self.tail_layer(conv5_4).view(-1, 25088)))) attr = self.bn1(x[:, :self.k]) latent = self.bn2(x[:, self.k:]) feats_0 = self.extract_0(conv2_2) feats_1 = self.extract_1(conv3_4) feats_2 = self.extract_2(conv4_4) feats_3 = self.extract_3(conv5_4) # N x k x 14 x 14 p_0 = self.attention_sublayers(feats_0, self.fc0, latent) p_1 = self.attention_sublayers(feats_1, self.fc1, latent) p_2 = self.attention_sublayers(feats_2, self.fc2, latent) p_3 = self.attention_sublayers(feats_3, self.fc3, latent) # N x k p = p_0 + p_1 + p_2 + p_3

这里的 attention_sublayers 函数接受一个特征张量 feats，一个嵌入层列表 embedding_layers 和一个潜在特征向量 latent。它将特征张量 feats 调整为一个三维张量，然后加上潜在特征向量 latent，再把它调整回原来的...

import tensorflow as tf class Residual(tf.keras.layers.Layer): def init(self, in_c, out_c): super(Residual, self).init() self.conv = tf.keras.Sequential([ tf.keras.layers.Conv2D(out_c, kernel_size=3, padding='same'), tf.keras.layers.BatchNormalization(), tf.keras.layers.ReLU(), tf.keras.layers.Conv2D(out_c, kernel_size=3, padding='same'), tf.keras.layers.BatchNormalization(), tf.keras.layers.ReLU() ]) self.botneck = tf.keras.layers.Conv2D(out_c, kernel_size=1) self.pool = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)) def call(self, x): x_prim = x x = self.conv(x) x = self.botneck(x_prim) + x x = self.pool(x) return x

在__init__方法中，定义了一个由多个卷积、批归一化和ReLU激活函数组成的序列模型self.conv，以及一个$1\times 1$的卷积层self.botneck和一个最大池化层self.pool。在call方法中，将输入张量x保存在x...

def init(self, input_size, hidden_size, output_size, weight_init_std = 0.01): # 重みの初期化 self.params = {} self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size) self.params['b1'] = np.zeros(hidden_size) self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size) self.params['b2'] = np.zeros(output_size) # レイヤの生成 self.layers = OrderedDict() self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1']) self.layers['Relu1'] = Relu() self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2']) self.lastLayer = SoftmaxWithLoss()

首先是第一层Affine层，其权重矩阵W1和偏置向量b1是之前定义的self.params['W1']和self.params['b1']。然后是激活函数层Relu，使用了ReLU激活函数。最后是第二层Affine层，其权重矩阵W2和偏置向量b2是之前定义的self...

class BasicBlock2D(nn.Module): expansion = 1 def init(self, in_channels, out_channels, stride=1): super(BasicBlock2D, self).init() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * out_channels) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out # 定义二维ResNet-18模型 class ResNet18_2D(nn.Module): def init(self, num_classes=1000): super(ResNet18_2D, self).init() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) # print(out.shape) out = out.view(out.size(0), -1) out = self.fc(out) return out改为用稀疏表示替换全连接层

layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) ...

if self.conv_layers is not None: for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers): # 遍历注意力架构层 x, attn = attn_layer(x, attn_mask=attn_mask) # 对x做maxpool1d操作，将512-->256 # 也就是结构中的金字塔，为了加速模型训练提出 x = conv_layer(x) attns.append(attn) # # 遍历注意力架构层 x, attn = self.attn_layers[-1](x, attn_mask=attn_mask) attns.append(attn) else: for attn_layer in self.attn_layers: x, attn = attn_layer(x, attn_mask=attn_mask) attns.append(attn)

这段代码是一个条件语句，用于根据self.conv_layers是否为None来选择不同的分支执行。如果self.conv_layers不为None，则会进入第一个分支。在这个分支中，代码首先通过zip函数将self.attn_layers和...

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

- conv1：第一个卷积层，输入 3 通道，输出 self.in_channel 个通道，卷积核大小为 7，步长为 2，padding 为 3。 - bn1：卷积层后面紧跟的批归一化层。 - relu：ReLU 激活函数。 - maxpool：最大池化层，尺寸为 3x3...

class ResNet18_2D(nn.Module): def init(self, num_classes=1000): super(ResNet18_2D, self).init() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.LeakyReLU = nn.LeakyReLU(negative_slope=0.1) self.relu = nn.ReLU(inplace=True) self.elu = nn.ELU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): # out = F.ReLU(self.bn1(self.conv1(x))) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) out = out.view(out.size(0), -1) return out 在self.layer4(out)和 self.avgpool(out)之间加CBAM

要在 self.layer4(out) 和 self.avgpool(out) 之间CBAM模块，可以按照以下步骤进行修改：首先，导入CBAM模块的相关库： python from cbam import CBAM 然后，在ResNet18_2D类中添加CBAM模块： ...

deck.gl框架入门与操作指南

1. **图层（Layers）**：在deck.gl中，所有的可视化都是通过图层来实现的。开发者可以将图层堆叠在一起，来创建复杂的可视化效果。例如，可以通过添加一个ScatterplotLayer来展示地理空间数据的散点图，通过...

探索ArcGIS JavaScript 4.x系列API版本差异

- 图层(Layers): 包括支持多种数据源的图层类型，如切片图层、特征图层、热力图层等，使得数据展示更加灵活多样。 - 地理处理工具(Geoprocessing): 提供了强大的地理处理工具集，允许开发者在客户端执行复杂的地理...

基于 C++构建 Qt 实现的 GDAL 与 PROJ4 的遥感图像处理软件课程设计

【作品名称】：基于 C++构建 Qt 实现的 GDAL 与 PROJ4 的遥感图像处理软件【课程设计】【适用人群】：适用于希望学习不同技术领域的小白或进阶学习者。可作为毕设项目、课程设计、大作业、工程实训或初期项目立项。【项目介绍】：用于处理包括*.img、*.tif、*.jpg、*.bmp、*.png等格式，不同位深的遥感图像。旨在提供简洁的用户界面与清晰的操作逻辑。软件囊括了基本的遥感图像处理功能，例如增强、边缘检测等，并提供了一种变化检测方法。引用库本软件基于 GDAL 与 Qt 在 C++ 环境下构建，地理信息处理部分使用了开源库 Proj.4 库，部分功能引用了 OpenCV 计算机视觉库【资源声明】：本资源作为“参考资料”而不是“定制需求”，代码只能作为参考，不能完全复制照搬。需要有一定的基础看懂代码，自行调试代码并解决报错，能自行添加功能修改代码。

【java毕业设计】娜娜服装企业物流管理系统源码（完整前后端+说明文档+LW）.zip

本系统包括普通员工、管理员两种角色，各个角色的具体功能如下：（1）普通员工具有的权限功能如下： ① 员工信息管理：对员工信息进行增删改查。 ② 服装信息管理：对服装公司的衣服信息进行增删改查。 ③ 库存信息管理：可以查看仓库的服装数量。 ④ 车辆信息管理：员工可以查询车辆信息：车牌号，编号，司机等。 ⑤ 服装配送管理：可以查看服装配送情况，车辆等。（2）管理员具有的权限功能如下： ① 账号信息管理：对员工的账号进行管理。 ② 用户信息管理：当前企业的用户信息管理。 ③ 员工信息管理：对员工信息进行增删改查。 ④ 服装信息管理：对服装公司的衣服信息进行增删改查。 ⑤ 库存信息管理：可以查看仓库的服装数量。 ⑥ 车辆信息管理：员工可以查询车辆信息：车牌号，编号，司机等。 ⑦ 服装配送管理：可以查看服装配送情况，车辆等。环境说明：开发语言：Java，jsp JDK版本：JDK1.8 数据库：mysql 5.7 数据库工具：Navicat11 开发软件：eclipse/idea 部署容器：tomcat

2009年国际大学生数学建模竞赛成果公告

内容概要：报告了第25届数学建模大赛(MCM)的官方结果。此次比赛来自14个国家超过1600支队伍参赛，在规定的时间完成指定题目。本次大赛主要在线上进行，参与者可以在规定时间注册，下载所需资料以及问题。比赛设置了‘最佳设计奖’，有9支团队从不同院校脱颖而出被评为优秀获奖者，其中包括两所中国大学——清华大学和北京理工大学（北航大学）。适用人群：数学、运筹学等相关领域的学者和学生使用场景及目标：为从事相关研究工作的个人提供了重要的参考资料；帮助了解当前学术界在解决复杂实际问题方面的方法与趋势。其他说明：竞赛旨在提升青年对于运用数学模型解决问题的兴趣和能力，促进了跨文化交流和技术水平的发展。

self.layers.append(Affine(self.params['W7'], self.params['b7'])) self.layers.append(Relu()) self.layers.append(Dropout(0.5)) self.layers.append(Affine(self.params['W8'], self.params['b8'])) self.layers.append(Dropout(0.5))

相关推荐

tensorflow中tf.nn.conv1d与layers.conv1d的运动学建模详解

Python库useful_layers-0.0a0.dev2详细安装指南

TensorFlow.js：探索迁移学习与AI产品创新的实践指南

deck.gl框架入门与操作指南

探索ArcGIS JavaScript 4.x系列API版本差异

基于 C++构建 Qt 实现的 GDAL 与 PROJ4 的遥感图像处理软件课程设计

【java毕业设计】娜娜服装企业物流管理系统源码（完整前后端+说明文档+LW）.zip

2009年国际大学生数学建模竞赛成果公告

最新推荐

对tensorflow中tf.nn.conv1d和layers.conv1d的区别详解

关于keras.layers.Conv1D的kernel_size参数使用介绍

解决Tensorflow2.0 tf.keras.Model.load_weights() 报错处理问题

基于 C++构建 Qt 实现的 GDAL 与 PROJ4 的遥感图像处理软件课程设计

【java毕业设计】娜娜服装企业物流管理系统源码（完整前后端+说明文档+LW）.zip

C语言数组操作：高度检查器编程实践

管理建模和仿真的文件

【KUKA系统变量进阶】：揭秘从理论到实践的5大关键技巧

如何使用Python编程语言创建一个具有动态爱心图案作为背景并添加文字'天天开心（高级版）'的图形界面？

基于Swift开发的嘉定单车LBS iOS应用项目解析