conv2d.zero_grad()

时间: 2023-06-13 10:02:33 浏览: 62
`conv2d.zero_grad()` 是一个 PyTorch 中的函数,用于将一个卷积层(`conv2d`)的梯度归零。在深度学习中,我们通常使用反向传播算法来计算神经网络中的梯度,然后再使用优化器来更新网络中的参数。在每次反向传播之前,我们需要将梯度清零,以避免梯度累积的影响。`conv2d.zero_grad()` 就是用来实现这个功能的。
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分割模型数据已加载,模型定义完成,请输出训练代码。模型如下model1.segmentation_head[0] = nn.Conv2d(in_channels, 3, kernel_size=3, padding=1)

非常感谢您的问题。以下是训练代码: ``` import torch import torch.nn as nn import torch.optim as optim # 定义模型 class Model1(nn.Module): def __init__(self, in_channels): super(Model1, self).__init__() self.segmentation_head = nn.Sequential( nn.Conv2d(in_channels, 3, kernel_size=3, padding=1) ) def forward(self, x): x = self.segmentation_head(x) return x # 加载数据 train_data = ... train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True) # 定义损失函数和优化器 model = Model1(in_channels=3) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 训练模型 for epoch in range(10): running_loss = 0.0 for i, data in enumerate(train_loader, 0): inputs, labels = data optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(train_loader))) ```

import torchimport torch.nn as nnimport torch.optim as optimimport numpy as np# 定义视频特征提取模型class VideoFeatureExtractor(nn.Module): def __init__(self): super(VideoFeatureExtractor, self).__init__() self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1) self.pool = nn.MaxPool2d(kernel_size=2, stride=2) def forward(self, x): x = self.pool(torch.relu(self.conv1(x))) x = self.pool(torch.relu(self.conv2(x))) x = x.view(-1, 32 * 8 * 8) return x# 定义推荐模型class VideoRecommendationModel(nn.Module): def __init__(self, num_videos, embedding_dim): super(VideoRecommendationModel, self).__init__() self.video_embedding = nn.Embedding(num_videos, embedding_dim) self.user_embedding = nn.Embedding(num_users, embedding_dim) self.fc1 = nn.Linear(2 * embedding_dim, 64) self.fc2 = nn.Linear(64, 1) def forward(self, user_ids, video_ids): user_embed = self.user_embedding(user_ids) video_embed = self.video_embedding(video_ids) x = torch.cat([user_embed, video_embed], dim=1) x = torch.relu(self.fc1(x)) x = self.fc2(x) return torch.sigmoid(x)# 加载数据data = np.load('video_data.npy')num_users, num_videos, embedding_dim = data.shapetrain_data = torch.tensor(data[:int(0.8 * num_users)])test_data = torch.tensor(data[int(0.8 * num_users):])# 定义模型和优化器feature_extractor = VideoFeatureExtractor()recommendation_model = VideoRecommendationModel(num_videos, embedding_dim)optimizer = optim.Adam(recommendation_model.parameters())# 训练模型for epoch in range(10): for user_ids, video_ids, ratings in train_data: optimizer.zero_grad() video_features = feature_extractor(video_ids) ratings_pred = recommendation_model(user_ids, video_ids) loss = nn.BCELoss()(ratings_pred, ratings) loss.backward() optimizer.step() # 计算测试集准确率 test_ratings_pred = recommendation_model(test_data[:, 0], test_data[:, 1]) test_loss = nn.BCELoss()(test_ratings_pred, test_data[:, 2]) test_accuracy = ((test_ratings_pred > 0.5).float() == test_data[:, 2]).float().mean() print('Epoch %d: Test Loss %.4f, Test Accuracy %.4f' % (epoch, test_loss.item(), test_accuracy.item()))解释每一行代码

1. `import torch`: 导入 PyTorch 模块 2. `import torch.nn as nn`: 导入 PyTorch 中的神经网络模块 3. `import torch.optim as optim`: 导入 PyTorch 中的优化器模块 4. `import numpy as np`: 导入 NumPy 模块,并将其重命名为 np 5. `class VideoFeatureExtractor(nn.Module):`:定义视频特征提取模型,继承自 nn.Module 6. `def __init__(self):`:定义初始化函数,初始化视频特征提取模型中的卷积层和池化层 7. `super(VideoFeatureExtractor, self).__init__()`: 调用父类的初始化函数 8. `self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)`: 定义一个 3 x 3 的卷积层,输入通道数为 3 ,输出通道数为 16,卷积核大小为 3,步长为 1,填充为 1 9. `self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)`: 定义一个 3 x 3 的卷积层,输入通道数为 16 ,输出通道数为 32,卷积核大小为 3,步长为 1,填充为 1 10. `self.pool = nn.MaxPool2d(kernel_size=2, stride=2)`: 定义一个大小为 2x2 的最大池化层 11. `def forward(self, x):`: 定义前向传播函数,将输入 x 经过卷积层和池化层后展平输出 12. `x = self.pool(torch.relu(self.conv1(x)))`: 将输入 x 经过第一层卷积层、ReLU 激活函数和最大池化层 13. `x = self.pool(torch.relu(self.conv2(x)))`: 将输入 x 经过第二层卷积层、ReLU 激活函数和最大池化层 14. `x = x.view(-1, 32 * 8 * 8)`: 将输出结果展平为一维向量,大小为 32*8*8 15. `return x`: 返回输出结果 x 16. `class VideoRecommendationModel(nn.Module):`:定义推荐模型,继承自 nn.Module 17. `def __init__(self, num_videos, embedding_dim):`:定义初始化函数,初始化推荐模型中的用户嵌入层、视频嵌入层和全连接层 18. `super(VideoRecommendationModel, self).__init__()`: 调用父类的初始化函数 19. `self.video_embedding = nn.Embedding(num_videos, embedding_dim)`: 定义视频嵌入层,输入维度为 num_videos,输出维度为 embedding_dim 20. `self.user_embedding = nn.Embedding(num_users, embedding_dim)`: 定义用户嵌入层,输入维度为 num_users,输出维度为 embedding_dim 21. `self.fc1 = nn.Linear(2 * embedding_dim, 64)`: 定义一个全连接层,输入维度为 2*embedding_dim,输出维度为 64 22. `self.fc2 = nn.Linear(64, 1)`: 定义一个全连接层,输入维度为 64,输出维度为 1 23. `def forward(self, user_ids, video_ids):`: 定义前向传播函数,将用户和视频 id 经过嵌入层和全连接层计算得到推荐评分 24. `user_embed = self.user_embedding(user_ids)`: 将用户 id 经过用户嵌入层得到用户嵌入 25. `video_embed = self.video_embedding(video_ids)`: 将视频 id 经过视频嵌入层得到视频嵌入 26. `x = torch.cat([user_embed, video_embed], dim=1)`: 将用户嵌入和视频嵌入拼接起来 27. `x = torch.relu(self.fc1(x))`: 将拼接后的结果经过激活函数和全连接层 28. `x = self.fc2(x)`: 将全连接层的输出作为推荐评分 29. `return torch.sigmoid(x)`: 将推荐评分经过 sigmoid 函数转换到 [0,1] 区间内 30. `data = np.load('video_data.npy')`: 从文件中读取数据 31. `num_users, num_videos, embedding_dim = data.shape`: 获取数据的形状,即用户数、视频数和嵌入维度 32. `train_data = torch.tensor(data[:int(0.8 * num_users)])`: 将前 80% 的数据作为训练集,并转换为 PyTorch 的 tensor 格式 33. `test_data = torch.tensor(data[int(0.8 * num_users):])`: 将后 20% 的数据作为测试集,并转换为 PyTorch 的 tensor 格式 34. `feature_extractor = VideoFeatureExtractor()`: 创建视频特征提取模型的实例 35. `recommendation_model = VideoRecommendationModel(num_videos, embedding_dim)`: 创建推荐模型的实例 36. `optimizer = optim.Adam(recommendation_model.parameters())`: 创建优化器,使用 Adam 算法优化推荐模型的参数 37. `for epoch in range(10):`: 开始训练,进行 10 轮迭代 38. `for user_ids, video_ids, ratings in train_data:`: 对训练集中的每个样本进行训练 39. `optimizer.zero_grad()`: 将梯度清零 40. `video_features = feature_extractor(video_ids)`: 提取视频特征 41. `ratings_pred = recommendation_model(user_ids, video_ids)`: 通过推荐模型得到预测评分 42. `loss = nn.BCELoss()(ratings_pred, ratings)`: 计算二分类交叉熵损失 43. `loss.backward()`: 反向传播求梯度 44. `optimizer.step()`: 更新模型参数 45. `test_ratings_pred = recommendation_model(test_data[:, 0], test_data[:, 1])`: 对测试集进行评分预测 46. `test_loss = nn.BCELoss()(test_ratings_pred, test_data[:, 2])`: 计算测试集上的损失 47. `test_accuracy = ((test_ratings_pred > 0.5).float() == test_data[:, 2]).float().mean()`: 计算测试集上的准确率 48. `print('Epoch %d: Test Loss %.4f, Test Accuracy %.4f' % (epoch, test_loss.item(), test_accuracy.item()))`: 输出每轮迭代的测试集损失和准确率

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import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable class Bottleneck(nn.Module): def init(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer): super(Bottleneck, self).init() self.out_planes = out_planes self.dense_depth = dense_depth self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(in_planes) self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False) self.bn2 = nn.BatchNorm2d(in_planes) self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(out_planes+dense_depth) self.shortcut = nn.Sequential() if first_layer: self.shortcut = nn.Sequential( nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_planes+dense_depth) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) x = self.shortcut(x) d = self.out_planes out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1) out = F.relu(out) return out class DPN(nn.Module): def init(self, cfg): super(DPN, self).init() in_planes, out_planes = cfg['in_planes'], cfg['out_planes'] num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth'] self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.last_planes = 64 self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1) self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2) self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2) self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2) self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)dense_depth[3], 10) def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride): strides = [stride] + 1 layers = [] for i,stride in (strides): layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0)) self.last_planes = out_planes + (i+2) * dense_depth return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.linear(out) return out def DPN92(): cfg = { 'in_planes': (96,192,384,768), 'out_planes': (256,512,1024,2048), 'num_blocks': (3,4,20,3), 'dense_depth': (16,32,24,128) } return DPN(cfg)基于这个程序利用pytorch框架修改成图像检测与分类输出坐标、大小和种类

import torch import os import torch.nn as nn import torch.optim as optim import numpy as np import random class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 16, kernel_size=3,stride=1) self.pool = nn.MaxPool2d(kernel_size=2,stride=2) self.conv2 = nn.Conv2d(16, 32, kernel_size=3,stride=1) self.fc1 = nn.Linear(32 * 9 * 9, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 2) def forward(self, x): x = self.pool(nn.functional.relu(self.conv1(x))) x = self.pool(nn.functional.relu(self.conv2(x))) x = x.view(-1, 32 * 9 * 9) x = nn.functional.relu(self.fc1(x)) x = nn.functional.relu(self.fc2(x)) x = self.fc3(x) return x net = Net() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) folder_path1 = 'random_matrices2' # 创建空的tensor x = torch.empty((40, 1, 42, 42)) # 遍历文件夹内的文件,将每个矩阵转化为tensor并存储 for j in range(40): for j in range(40): file_name = 'matrix_{}.npy'.format(i) file_path1 = os.path.join(folder_path1, file_name) matrix1 = np.load(file_path1) x[j] = torch.from_numpy(matrix1).unsqueeze(0) folder_path2 = 'random_label2' y = torch.empty((40, 1)) for k in range(40): for k in range(40): file_name = 'label_{}.npy'.format(i) file_path2 = os.path.join(folder_path2, file_name) matrix2 = np.load(file_path2) y[k] = torch.from_numpy(matrix2).unsqueeze(0) for epoch in range(10): running_loss = 0.0 for i in range(40): inputs, labels = x[i], y[i] optimizer.zero_grad() outputs = net(inputs) optimizer.step() #running_loss += loss.item() #print('[%d] loss: %.3f' % (epoch + 1, running_loss / 40)) print('Finished Training') 加入loss可视化

LDAM损失函数pytorch代码如下:class LDAMLoss(nn.Module): def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).__init__() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下:# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

import torch import os import torch.nn as nn import torch.optim as optim import numpy as np import random import matplotlib.pyplot as plt class Net(nn.Module): def init(self): super(Net, self).init() self.conv1 = nn.Conv2d(1, 16, kernel_size=3,stride=1) self.pool = nn.MaxPool2d(kernel_size=2,stride=2) self.conv2 = nn.Conv2d(16, 32, kernel_size=3,stride=1) self.fc1 = nn.Linear(32 * 9 * 9, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 2) def forward(self, x): x = self.pool(nn.functional.relu(self.conv1(x))) x = self.pool(nn.functional.relu(self.conv2(x))) x = x.view(-1, 32 * 9 * 9) x = nn.functional.relu(self.fc1(x)) x = nn.functional.relu(self.fc2(x)) x = self.fc3(x) return x net = Net() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) folder_path1 = 'random_matrices2' # 创建空的tensor x = torch.empty((40, 1, 42, 42)) # 遍历文件夹内的文件,将每个矩阵转化为tensor并存储 for j in range(40): for j in range(40): file_name = 'matrix_{}.npy'.format(i) file_path1 = os.path.join(folder_path1, file_name) matrix1 = np.load(file_path1) x[j] = torch.from_numpy(matrix1).unsqueeze(0) folder_path2 = 'random_label2' y = torch.empty((40, )) for k in range(40): for k in range(40): file_name = 'label_{}.npy'.format(i) file_path2 = os.path.join(folder_path2, file_name) matrix2 = np.load(file_path2) y[k] = torch.from_numpy(matrix2).unsqueeze(0) losses = [] for epoch in range(10): running_loss = 0.0 for i in range(40): inputs, labels = x[i], y[i] optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels.squeeze(1)) loss.backward() optimizer.step() running_loss += loss.item() losses.append(running_loss / 40) print('[%d] loss: %.3f' % (epoch + 1, running_loss / 40)) print('Finished Training') plt.plot(losses) plt.xlabel('Epoch') plt.ylabel('Loss') plt.show() 报错:IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1) 怎么修改?

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