importtorch.nnasnn importtorch.optimasoptim importtorchvision.datasetsasdatasets importtorchvision.transformsastransforms #定义Transformer模型 classTransformerModel(nn.Module): def__init__(self,num_classes=10,d_model=512,nhead=8,num_encoder_layers=6,dim_feedforward=2048,dropout=0.1): super(TransformerMo...
在PyTorch中,神经网络模型由继承自nn.Module的类表示,因此您需要定义一个类来创建判别器。 判别别器是一个具有二维输入和一维输出的模型。它将接收来自真实数据或生成器的样本,并提供样本属于真实训练数据的概率。下面的代码展示了如何创建判别器: class Discriminator(nn.Module): def __init__(self): nn.Linear(...
class DKTModel(nn.Module): def __init__(self, topic_size): super(DKTModel, self).__init__() self.topic_size = topic_size # 使用GRU,输入就是【知识点数量*2】,就是前面介绍过的one-hot self.rnn = nn.GRU(topic_size * 2, topic_size, 1) # 得分预测层 self.score = nn.Linear(topi...
nn.functional as F from torch_geometric.nn import GCNConv class GAE(nn.Module): def __init__(self, in_channels, out_channels): super(GAE, self).__init__() self.conv1 = GCNConv(in_channels, 2 * out_channels) self.conv2 = GCNConv(2 * out_channels, out_channels) def encode(...
class SentimentLSTM(nn.Module): def __init__( self, n_vocab, n_embed, n_hidden, n_output, n_layers, drop_p = 0.8 ): super().__init__() self.n_vocab = n_vocab self.n_layers = n_layers self.n_hidden = n_hidden 然后,我们定义网络的每个层。 首先,我们定义嵌入层,该层的词汇...
class EnsembleModel(nn.Module): def __init__(self, modelA, modelB, modelC): super().__init__() self.modelA = modelA self.modelB = modelB self.modelC = modelC self.classifier = nn.Linear(200 * 3, 200) def forward(self, x): ...
class LeNet(nn.Module): def __init__(self, classes): super(LeNet, self).__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16*5*5, 120) self.fc2 = nn.Linear(120, 84) ...
classInvalidDatasetException(Exception): def__init__(self, len_of_paths, len_of_labels): super().__init__( f"Number of paths ({len_of_paths}) is not compatible with number of labels ({len_of_labels})" ) transform = transforms.Compose([transforms.ToTensor()]) ...
classSimpleRNN(nn.Module):def__init__(self, input_size, hidden_size, output_size):super(SimpleRNN,self).__init__()self.hidden_size = hidden_sizeself.rnn = nn.RNN(input_size, hidden_size, batch_first=True)self.fc = nn.Linear(hidden_size, output_size)defforward(self, x): ...
我们已经对数据进行了预处理,现在是时候训练我们的模型了。我们将定义一个LSTM类,该类继承自 PyTorch 库的 nn.Module 类。 classLSTM(nn.Module):def__init__(self, input_size=1, hidden_layer_size=100, output_size=1): super().__init__() ...