output_size,n_layers=1,bidirectional=True):super(GRUClassifier,self).__init__()self.hidden_size=hidden_sizeself.n_layers=n_layersself.n_directions=2ifbidirectionalelse1#Embedding层:输入(seqLen,batch_size),输出(seqLen
先用softmax 的公式算出来 y(预测值)。 然后用上面的损失函数算出来y的损失值。在Pytorch中使用: 这条函数包括了上面的softmax算预测值和算损失值的全部过程。 在使用CrossEntropyLossr的时候,最后一层线性层不要做非线性变换,就是乘以那个α 或 sigmoid激活函数。这条函数(交叉熵)会自动帮你激活。 关于上面的...
) self.classifier = nn.Linear(2048, num_classes) if init_weights: self._initialize_weights() @autocast() def forward(self, x): x =self.layers(x) x = self.fc(x) x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m,nn.Conv2d...
运行后首先进行数据的下载: (deeplearning2) userdeMBP:classifier cifar user$ python cifar10_tutorial.py Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz100.0%Files already downloaded and verified 下面展示一些训练图像: import matplotlib.pyplotas...
import torch.nn as nnclass classifier(nn.Module): #定义所有层 def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout): super().__init__() #embedding 层 self.embedding = nn.Embedding(vocab_size, embedding_dim) #lstm...
定义优化器和损失函数: optimizer = torch.optim.Adam(classifier.parameters(), lr=1e-5) # Adjust learning rate and optimizer as per your requirements. criterion = torch.nn.CrossEntropyLoss() # Use appropriate loss function based on your task (e.g., binary cross entropy for binary classification...
class Classifier(torch.nn.Module): def __init__(self): super().__init__() self.backbone = torchvision.models.resnet18(pretrained=True) def forward(self, x): feature = self.backbone(x) probability = torch.softmax(feature, dim=1) ...
VGG19有两个部分,分别是VGG19.features和VGG19.classifier。 vgg19.features有卷积层和池化层 vgg19.features有三个线性层,最后是softmax分类器 下面将使用 torchvision.models 加载 VGG19,并将预训练权重设置为 True之后,将冻结层,使这些层不可训练。
(kernel_size=2, stride=2), ) self.classifier = nn.Sequential( nn.Linear(2048, 512), nn.ReLU(), nn.Dropout(0.1), nn.Linear(512, num_classes) ) def forward(self, x): x = self.features(x) flattened_conv_output = torch.flatten(x, 1) x = self.classifier(flattened_conv_output) ...
nn.ReLU(),# 激活函数 nn.AvgPool2d( kernel_size = 2,# 平均值池化,2*2 stride = 2,# 池化步长2 ), ) self.conv2 = nn.Sequential( nn.Conv2d(16, 32, 3, 1, 0, dilation = 2), nn.ReLU(), nn.AvgPool2d(2, 2), ) self.classifier = nn.Sequential( ...