class GRUClassifier(torch.nn.Module): def __init__(self, input_size, hidden_size, output_size, n_layers=1, bidirectional=True): super(GRUClassifier, self).__init__() self.hidden_size = hidden_size self.n_layers = n_layers self.n_directions = 2 if bidirectional else 1 #Embedding层...
classifier(x) # print(x.shape) return x torch.manual_seed(42) model_2 = FashionMNISTModelV2(input_shape=1, hidden_units=10, output_shape=len(class_names)).to(device) model_2 out: FashionMNISTModelV2( (block_1): Sequential( (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1,...
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...
cifar - 10中的图像大小为3x32x32,即3 - channel彩色图像,大小为32x32像素。 二、Training an image classifier 我们将按顺序进行以下步骤: 1使用torchvision对CIFAR10训练和测试数据集进行加载和规范化 2.定义一个卷积神经网络 3.定义一个损失函数 4.在训练数据上训练神经网络 5.在测试数据上测试神经网络 1加载...
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...
(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) ...
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) ...
(classifier): Sequential( ... ) (Linear): Linear(in_features=1000, out_features=10, bias=True) ) 也可以使用: 代码语言:javascript 复制 vgg_pretrained.classifier.add_module('Linear', torch.nn.Linear(1000, 10)) vgg_pretrained Out[12]: VGG( (features): Sequential( ... ) (avgpool): ...
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...
ResNet, Inception: input_size = model.fc.in_features VGG: input_size = model.classifier[0].in_features DenseNet: input_size = model.classifier.in_features SqueezeNet: input_size = model.classifier[1].in_channels AlexNet: alexnet.classifier[1].in_features ...