KFoldfromsklearn.metricsimportmean_squared_error, r2_scorefromsklearn.preprocessingimportStandardScalerfromtorchimport_dynamoastorchdynamofromtypingimportList# Generate synthetic datasetnp.random.seed(42)torch.manual_seed(42)# Feature engineering
style_loss_list=[]#风格损失 model=nn.Sequential()#创建一个model,按顺序放入layer model=model.to(device)gram=loss.Gram().to(device)'''把vgg19中的layer、content_loss以及style_loss按顺序加入到model中:'''i=1forlayerincnn:ifisinstance(layer,nn.Conv2d):name='conv_'+str(i)model.add_module(...
params = list(model.named_parameters())(name, param) = params[28]print(name)print(param.grad)print('---')(name2, param2) = params[29]print(name2)print(param2.grad)print('---')(name1, param1) = params[30]print(name1)print(param1.grad) 模型可视...
如果我们向下滚动,我们会看到很多Bottleneck模块一个接一个地重复(共 101 个!),包含卷积和其他模块。这就是典型的用于计算机视觉的深度神经网络的解剖学:一个或多或少顺序级联的滤波器和非线性函数,最终以一个层(fc)产生每个 1,000 个输出类别(out_features)的分数。 resnet变量可以像函数一样调用,输入一个或多...
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,seed) # 模型输入层(也是DNN输入层) dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list) # DNN输出层 ...
model_weights = [] # append模型的权重 conv_layers = [] # append模型的卷积层本身 # get all the model children as list model_children = list(model.children()) # counter to keep count of the conv layers counter = 0 # 统计模型里共有多少个卷积层 model.children() 直接print出来,结果是:<...
import torch from torch.utils.data import DataLoader, TensorDataset # 创建一个 TensorDataset features = torch.randn(10, 5) # 10 个样本,每个样本有 5 个特征 labels = torch.randint(0, 2, (10,)) # 10 个标签,0 或 1 dataset = TensorDataset(features, labels) def collate_batch(batch_list):...
):self.loss.backward(retain_graph=retain_graph)return self.lossclass Gram_matrix(nn.Module):def forward(self,input):a,b,c,d=input.size()feature=input.view(a*b,c*d)gram=torch.mm(feature, feature.t())return gram.div(a*b*c*d)#%% 模型搭建vgg=models.vgg19(pretrained=True).features...
# Define model parametersinput_size = list(input.shape)[1]# = 4. The input depends on how many features we initially feed the model. In our case, there are 4 features for every predict valuelearning_rate =0.01output_size = len(labels)# The output is prediction results for three types ...
*To see a full list of public feature submissions clickhere. BETA FEATURES [Beta] torch.compiler.set_stance This feature enables the user to specify different behaviors (“stances”) thattorch.compilecan take between different invocations of compiled functions. One of the stances, for example, is...