代码为import torch import torch.nn as nn class STAE(nn.Module): def __init__(self): super(STAE, self).__init__() self.c1 = nn.Sequential( nn.Conv1d(1, 64, kernel_size=3, stride=1, padding=1), nn.BatchNorm1d(64), nn.MaxPool1d(kernel_size=2, stride=2), nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1), nn.BatchNorm1d(128), nn.MaxPool1d(kernel_size=2, stride=2), nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1), nn.BatchNorm1d(128), nn.MaxPool1d(kernel_size=2, stride=2), nn.Conv1d(128, 256, kernel_size=3, stride=1, padding=0), nn.BatchNorm1d(256), nn.MaxPool1d(kernel_size=2, stride=2), nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1), nn.BatchNorm1d(256), nn.MaxPool1d(kernel_size=2, stride=2), )#31*256 长*通道数（原先1000*1） self.r1 = nn.Sequential( nn.LSTM(input_size=30, hidden_size=64, batch_first=True), ) self.l1 = nn.Linear(64, 1) def forward(self,x): x=self.c1(x) x=self.r1(x) return x if __name__ == '__main__': a=torch.ones(1,1,1000) net=STAE() output=net(a) print(torch.Size(a))。代码出错Traceback (most recent call last): File "D:\Program Files\JetBrains\PyCharm 2023.1\PycharmProject\test\STAE.py", line 38, in <module> print(torch.Size(a)) TypeError: torch.Size() takes an iterable of 'int' (item 0 is 'Tensor')。请问如何修改

import torch from torch import nn class MLP(nn.Module): def init(self, input_dim, num_class, hidden_dim) -> None: super().init() self.hidden_dim = hidden_dim self.mlp = nn.Sequential(*[ nn.Linear(input_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, num_class) ]) def forward(self, x): return self.mlp(x)

其输入特征维度为 input_dim，输出类别数为 num_class，隐藏层维度为 hidden_dim。其中，nn.Linear 表示全连接层，nn.ReLU 表示激活函数 ReLU。forward() 方法定义了模型的前向传播过程，即输入特征经过多个全连接层...

更改import torch import torchvision.models as models import torch.nn as nn import torch.nn.functional as F class eca_Resnet50(nn.Module): def init(self): super().init() self.model = models.resnet50(pretrained=True) self.model.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.model.fc = nn.Linear(2048, 1000) self.eca = ECA_Module(2048, 8) def forward(self, x): x = self.model.conv1(x) x = self.model.bn1(x) x = self.model.relu(x) x = self.model.maxpool(x) x = self.model.layer1(x) x = self.model.layer2(x) x = self.model.layer3(x) x = self.model.layer4(x) x = self.eca(x) x = self.model.avgpool(x) x = torch.flatten(x, 1) x = self.model.fc(x) return x class ECA_Module(nn.Module): def init(self, channel, k_size=3): super(ECA_Module, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x) y = self.conv(y.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1) y = self.sigmoid(y) return x * y.expand_as(x) class ImageDenoising(nn.Module): def init(self): super().init() self.model = eca_Resnet50() self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = self.conv3(x) x = F.relu(x) return x输出为[16,1,50,50]

很好，你已经成功地定义了一个带有 ECA 模块的 ResNet50，以及一个图像去噪模型 ImageDenoising，输出大小为 [16, 1, 50, 50]。在 ImageDenoising 的 forward 函数中，你首先将输入 x 传递给 conv1，然后对其进行 ...

优化这段代码：import torch.nn as nnclass MyLinear(nn.Module): def init(self, in_features, out_features): super(MyLinear, self).init() self.linears = nn.ModuleList([nn.Linear(in_features, out_features) for i in range(5)]) def forward(self, x): outputs = [] for i in range(5): outputs.append(self.linears[i](x[:,i,:])) return torch.stack(outputs, dim=1)

import torch.nn as nn class MyLinear(nn.Module): def __init__(self, in_features, out_features): super(MyLinear, self).__init__() self.linears = nn.ModuleList([nn.Linear(in_features, out_features) ...

import torch import torch.nn as nn from torch.nn import functional as F class RestNetBasicBlock(nn.Module): def init(self, in_channels, out_channels, stride): super(RestNetBasicBlock, self).init() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1) self.bn2 = nn.BatchNorm2d(out_channels) def forward(self, x): output = self.conv1(x) output = F.relu(self.bn1(output)) output = self.conv2(output) output = self.bn2(output) return F.relu(x + output) ———————————————— 逐行解释以上代码

import torch.nn as nn from torch.nn import functional as F 首先导入了PyTorch库及其相关模块。 python class RestNetBasicBlock(nn.Module): def __init__(self, in_channels, out_channels, stride): ...

优化这段代码来提高运行速度：import torch.nn as nn class MyLinear(nn.Module): def init(self, in_features, out_features): super(MyLinear, self).init() self.linears = nn.ModuleList([nn.Linear(in_features, out_features) for i in range(5)]) def forward(self, x): chunks = torch.chunk(x, 5, dim=1) outputs = [linear(chunk) for linear, chunk in zip(self.linears, chunks)] return torch.cat(outputs, dim=1)

import torch.nn as nn class MyLinear(nn.Module): def __init__(self, in_features, out_features): super(MyLinear, self).__init__() self.linears = nn.ModuleList([nn.Linear(in_features, out_features) ...

r"""Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def init(self): super(Model, self).init() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

这是关于PyTorch中神经网络模块的基类nn.Module的说明文档。所有的神经网络模型都应该继承这个类。nn.Module还可以包含其他的nn.Module，从而允许将它们嵌套在树形结构中。可以将子模块分配为常规属性，这些子...

import torch import torch.nn as nn import torch.nn.functional as F import torchvision.models as models import os class FCNTransformerNet(nn.Module): def init(self, num_classes): super(FCNTransformerNet, self).init() self.fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True).backbone self.fcn_backbone.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.transformer_layers = nn.TransformerEncoderLayer(d_model=2048, nhead=8) self.transformer_encoder = nn.TransformerEncoder(self.transformer_layers, num_layers=6) self.classification_head = nn.Sequential( nn.Linear(2048, 512), nn.ReLU(), nn.Linear(512, num_classes) ) def forward(self, x): fcn_output = self.fcn_backbone(x)['out'] fcn_output = fcn_output.view(fcn_output.size(0), fcn_output.size(1), -1) fcn_output = fcn_output.permute(2, 0, 1) transformer_output = self.transformer_encoder(fcn_output) transformer_output = transformer_output.permute(1, 2, 0) transformer_output = transformer_output.contiguous().view(transformer_output.size(0), -1, 1, 1) output = self.classification_head(transformer_output) return output FCNTransformerNet net = FCNTransformerNet(num_classes=2) input_batch = torch.randn(4, 3, 512, 512) output_batch = net(input_batch) print(output_batch.size()) # Should print: torch.Size([4, 2, 512, 512]) 运行这段代码，并改错

import torch.nn as nn import torchvision.models as models class FCNTransformerNet(nn.Module): def __init__(self, num_classes): super(FCNTransformerNet, self).__init__() self.fcn_backbone = models...

import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim class Net(nn.Module): def init(self, input_size, output_size): super().init() #任务1定义网络计算单元# #任务1定义网络计算单元# def forward(self, x): #任务2网络结构# #任务2网络结构# return x

可以看出，它包括了一个名为Net的类，该类继承自nn.Module类。在该类的构造函数中，我们可以自定义网络计算单元，即神经网络的各层结构以及参数。在forward函数中，我们定义了网络的前向传播过程，即输入数据如何...

运行以下Python代码：import torchimport torch.nn as nnimport torch.optim as optimfrom torchvision import datasets, transformsfrom torch.utils.data import DataLoaderfrom torch.autograd import Variableclass Generator(nn.Module): def init(self, input_dim, output_dim, num_filters): super(Generator, self).init() self.input_dim = input_dim self.output_dim = output_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters), nn.ReLU(), nn.Linear(num_filters, num_filters2), nn.ReLU(), nn.Linear(num_filters2, num_filters4), nn.ReLU(), nn.Linear(num_filters4, output_dim), nn.Tanh() ) def forward(self, x): x = self.net(x) return xclass Discriminator(nn.Module): def init(self, input_dim, num_filters): super(Discriminator, self).init() self.input_dim = input_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters4), nn.LeakyReLU(0.2), nn.Linear(num_filters4, num_filters2), nn.LeakyReLU(0.2), nn.Linear(num_filters2, num_filters), nn.LeakyReLU(0.2), nn.Linear(num_filters, 1), nn.Sigmoid() ) def forward(self, x): x = self.net(x) return xclass ConditionalGAN(object): def init(self, input_dim, output_dim, num_filters, learning_rate): self.generator = Generator(input_dim, output_dim, num_filters) self.discriminator = Discriminator(input_dim+1, num_filters) self.optimizer_G = optim.Adam(self.generator.parameters(), lr=learning_rate) self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=learning_rate) def train(self, data_loader, num_epochs): for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(data_loader): # Train discriminator with real data real_inputs = Variable(inputs) real_labels = Variable(labels) real_labels = real_labels.view(real_labels.size(0), 1) real_inputs = torch.cat((real_inputs, real_labels), 1) real_outputs = self.discriminator(real_inputs) real_loss = nn.BCELoss()(real_outputs, torch.ones(real_outputs.size())) # Train discriminator with fake data noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0, 10)) fake_labels = fake_labels.view(fake_labels.size(0), 1) fake_inputs = self.generator(torch.cat((noise, fake_labels.float()), 1)) fake_inputs = torch.cat((fake_inputs, fake_labels), 1) fake_outputs = self.discriminator(fake_inputs) fake_loss = nn.BCELoss()(fake_outputs, torch.zeros(fake_outputs.size())) # Backpropagate and update weights for discriminator discriminator_loss = real_loss + fake_loss self.discriminator.zero_grad() discriminator_loss.backward() self.optimizer_D.step() # Train generator noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0,

import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms from torch.utils.data import DataLoader from torch.autograd import Variable 接下来定义生成器...

如何改进下面这段代码使其性能更好import torch.nn as nn class fcNN(nn.Module): def init(self, in_features): super(fcNN, self).init() self.Linear1 = nn.Sequential( nn.Linear(in_features, 8, bias=True), nn.ReLU(), nn.Dropout(0.1) ) self.Linear2 = nn.Sequential( nn.Linear(8, 4, bias=True), nn.ReLU(), nn.Dropout(0.1) ) self.Linear3 = nn.Sequential( nn.Linear(4, 1, bias=True), nn.ReLU(), nn.Dropout(0.1) ) def forward(self, x): x = x.view(1, 1, 1, -1) out = self.Linear3(self.Linear2(self.Linear1(x))) return out

import torch.nn as nn class fcNN(nn.Module): def __init__(self, in_features): super(fcNN, self).__init__() self.Linear1 = nn.Sequential( nn.Linear(in_features, 8, bias=True), nn.LeakyReLU() ) ...

import torch import torch.nn as nn from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import torch.optim as optim import matplotlib.pyplot as plt # 划分特征值和预测值并转换为numpy数据 X = df.iloc[:,:-1].values y = df.iloc[:,-1].values # 划分训练集和测试集 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0) ## 开始进行数据回归分析 # 将数据转换为张量 X_train = torch.from_numpy(X_train).float() X_test = torch.from_numpy(X_test).float() y_train = torch.from_numpy(y_train).float() y_test = torch.from_numpy(y_test).float() torch.manual_seed(0) # 定义模型 class Net(nn.Module): def init(self): super(Net,self).init() self.fc1=nn.Linear(3,10) self.fc2=nn.Linear(10,1) def forward(self, x): x=self.fc1(x) x=torch.relu(x) x=self.fc2(x) return x net = Net() # 定义损失函数和优化器 criterion = nn.MSELoss() optimizer = optim.Adam(net.parameters(),lr=0.07)这里面用的是什么神经网络模型

隐藏层的神经元数量为 10，可以通过修改代码中 self.fc1=nn.Linear(3,10) 中的参数来调整；输出层的神经元数量为 1，因为预测值只有一个。使用的激活函数是 ReLU 函数，在隐藏层中使用。损失函数使用的是均方误差...

优化这段代码让其在gpu上可以更快运行：import torch.nn as nnclass MyLinear(nn.Module): def init(self, in_features, out_features): super(MyLinear, self).init() self.linears = nn.ModuleList([nn.Linear(in_features, out_features) for i in range(5)]) def forward(self, x): chunks = torch.chunk(x, 5, dim=1) chunks = torch.stack(chunks, dim=0) weights = torch.stack([linear.weight for linear in self.linears], dim=0) outputs = torch.matmul(chunks, weights.transpose(1, 2)).reshape(x.shape[0], -1) return outputs

import torch.nn as nn class MyLinear(nn.Module): def __init__(self, in_features, out_features): super(MyLinear, self).__init__() self.linears = nn.ModuleList([nn.Linear(in_features, out_features)....

下面的代码定义了VQTFNet模型，其中的model是由class tf_net定义的，使用pytorch_lightning修改下面的代码，要求VQTFNet模型在训练集上训练时tf_net的参数"t_timesteps"=303，在验证集上验证时"t_timesteps"=803 class VQTFNet(pl.LightningModule): def init(self, hparam): super(VQTFNet, self).init() self.hparam = hparam self.model = tf_net() class tf_net(nn.Module): def init( self, ): super(tf_net, self).init() cfg = { "t_timesteps": 303, "fc_dim": 128 }

import torch.nn as nn import pytorch_lightning as pl class VQTFNet(pl.LightningModule): def __init__(self, hparam): super(VQTFNet, self).__init__() self.hparam = hparam self.model = tf_net() ...

更改import torch import torchvision.models as models import torch.nn as nn import torch.nn.functional as F class eca_Resnet50(nn.Module): def init(self): super().init() self.model = models.resnet50(pretrained=True) self.model.avgpool = nn.AdaptiveAvgPool2d((1,1)) self.model.fc = nn.Linear(2048, 1000) self.eca = ECA_Module(2048, 8) def forward(self, x): x = self.model.conv1(x) x = self.model.bn1(x) x = self.model.relu(x) x = self.model.maxpool(x) x = self.model.layer1(x) x = self.model.layer2(x) x = self.model.layer3(x) x = self.model.layer4(x) x = self.eca(x) x = self.model.avgpool(x) x = torch.flatten(x, 1) x = self.model.fc(x) return x class ECA_Module(nn.Module): def init(self, channel, k_size=3): super(ECA_Module, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x) y = self.conv(y.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1) y = self.sigmoid(y) return x * y.expand_as(x) class ImageDenoising(nn.Module): def init(self): super().init() self.model = eca_Resnet50() self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = self.conv3(x) x = F.relu(x) return x，使最后输出为[16,1,50,50,]。

import torch.nn as nn import torch.nn.functional as F class eca_Resnet50(nn.Module): def __init__(self): super().__init__() self.model = models.resnet50(pretrained=True) self.model.avgpool = nn....

import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import Dataset, DataLoader from sklearn.metrics import accuracy_score import jieba from CLDNN2 import CLDNN from CLDNNtest import CLDNNtest # 定义超参数 MAX_LENGTH = 100 # 输入序列的最大长度 VOCAB_SIZE = 35091 # 词汇表大小 EMBEDDING_SIZE = 128 # 词向量的维度 NUM_FILTERS = 100 # 卷积核数量 FILTER_SIZES = [2, 3, 4] # 卷积核尺寸 class SentimentDataset(Dataset): def init(self, texts, labels): self.texts = texts self.labels = labels def len(self): return len(self.texts) def getitem(self, index): text = self.texts[index] label = self.labels[index] return text, label class CNNClassifier(nn.Module): def init(self, vocab_size, embedding_size, num_filters, filter_sizes, output_size, dropout): super().init() self.embedding = nn.Embedding(vocab_size, embedding_size) # self.convs = nn.ModuleList([ # nn.Conv2d(1, num_filters, (fs, embedding_size)) for fs in filter_sizes # ]) self.convs = nn.Sequential( nn.Conv2d(1, num_filters, (2, 2)), # nn.MaxPool2d(2), nn.ReLU(inplace=True), nn.Conv2d(num_filters, num_filters, (3, 3)), nn.ReLU(inplace=True), nn.Conv2d(num_filters, num_filters, (4, 4)), nn.MaxPool2d(2), nn.ReLU(inplace=True), nn.Dropout(dropout) ) self.fc = nn.Sequential( nn.Linear(286700, 300), nn.Linear(300, output_size) ) # self.dropout = nn.Dropout(dropout) def forward(self, text): # text: batch_size * seq_len embedded = self.embedding(text) # batch_size * seq_len * embedding_size # print(embedded.shape) embedded = embedded.unsqueeze(1) # batch_size * 1 * seq_len * embedding_size x = self.convs(embedded) print(x.shape) # print(embedded.shape) # conved = [F.relu(conv(embedded)).squeeze(3)

模型的定义使用了 PyTorch 中的 nn.Module 类，包括了词嵌入层、卷积层和全连接层。前向传播的实现包括了将输入文本进行词嵌入、进行卷积操作、进行池化操作和进行最终的分类。需要注意的是，这段代码中使用了中文...

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset class LSTM(nn.Module): def init(self, inputDim, hiddenDim, layerNum, batchSize): super(LSTM, self).init()

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