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model_params:dict

时间: 2023-10-30 20:47:27 浏览: 59

model-0000.params

insightface-models-genderage_v1模型文件model-0000.params

I am sorry, as an AI language model, I do not have access to specific information about your project or context. Can you please provide more information or context about what you are looking for?

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openpose_tennis

params["model_folder"] = "/path/to/openpose/models/" params["output_height"], params["output_width"] = 432, 368 params["net_resolution"] = "-1x-1" params["disable_blending"] = True opWrapper = op....

根据以下代码，利用shap库写出绘制bar plot图的代码“def five_fold_train(x: pd.DataFrame, y: pd.DataFrame, model_class: type, super_parameters: dict = None, return_model=False): """ 5折交叉验证训练器 :param x: :param y: :param model_class: 学习方法类别，传入一个类型 :param super_parameters: 超参数 :param return_model: 是否返回每个模型 :return: list of [pred_y,val_y,auc,precision,recall] """ res = [] models = [] k_fold = KFold(5, random_state=456, shuffle=True) for train_index, val_index in k_fold.split(x, y): #即对数据进行位置索引，从而在数据表中提取出相应的数据 train_x, train_y, val_x, val_y = x.iloc[train_index], y.iloc[train_index], x.iloc[val_index], y.iloc[val_index] if super_parameters is None: super_parameters = {} model = model_class(**super_parameters).fit(train_x, train_y) pred_y = model.predict(val_x) auc = metrics.roc_auc_score(val_y, pred_y) precision = metrics.precision_score(val_y, (pred_y > 0.5) * 1) recall = metrics.recall_score(val_y, (pred_y > 0.5) * 1) res.append([pred_y, val_y, auc, precision, recall]) models.append(model) # print(f"fold: auc{auc} precision{precision} recall{recall}") if return_model: return res, models else: return res best_params = { "n_estimators": 500, "learning_rate": 0.05, "max_depth": 6, "colsample_bytree": 0.6, "min_child_weight": 1, "gamma": 0.7, "subsample": 0.6, "random_state": 456 } res, models = five_fold_train(x, y, XGBRegressor, super_parameters=best_params, return_model=True)”

model_index = 0 # 获取特征重要性信息 explainer = shap.TreeExplainer(models[model_index]) shap_values = explainer.shap_values(x) # 绘制bar plot shap.summary_plot(shap_values, x, plot_type="bar") # ...

if args.start_epoch > 0: pre_model_dir = model_dir checkpoint = torch.load("%s/net_params_%d.pth" % (pre_model_dir, args.start_epoch)) model.load_state_dict(checkpoint['net']) optimizer.load_state_dict(checkpoint['optimizer']) start_epoch = checkpoint["epoch"] + 1 for i in range(0, start_epoch): scheduler.step() else: start_epoch = args.start_epoch + 1 scheduler.step() print("Model: %s , Sensing Rate: %.2f , Epoch: %d , Initial LR: %f\n" % ( args.model, args.sensing_rate, args.epochs, args.lr))

是的，这段代码是用于训练神经网络模型的。首先，代码检查是否指定了一个起始的epoch，如果指定了，则从checkpoint中加载之前的训练状态，包括模型的参数、优化器的参数和学习率调度器的状态，并从上一个epoch开始...

pretrain_model = resnet34(pretrained=False) # 94.6% num_ftrs = pretrain_model.fc.in_features # 获取全连接层的输入。新加进去的层，训练单独训练最后一层 pretrain_model.fc = nn.AdaptiveAvgPool2d(4) # 全连接层改为不同的输出，自己需要的输出 print(pretrain_model) pretrained_dict = torch.load('./resnet34_pretrain.pth') pretrained_dict.pop('fc.weight') pretrained_dict.pop('fc.bias') print(pretrained_dict) model_dict = pretrain_model.state_dict() print(model_dict) pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} model_dict.update(pretrained_dict) print(model_dict) pretrain_model.load_state_dict(model_dict) print(pretrain_model) for name, value in pretrain_model.named_parameters(): if (name != 'fc.weight') and (name != 'fc.bias'): value.requires_grad = False params_conv = filter(lambda p: p.requires_grad, pretrain_model.parameters()) # 要更新的参数在parms_conv当中 model = pretrain_model.to(device) loss_fn = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(params_conv, lr=1e-3) # 初始学习率为什么会出现"ValueError: optimizer got an empty parameter list"错误

你在代码中使用了pretrained_dict.pop('fc.weight')和pretrained_dict.pop('fc.bias')来移除全连接层的权重和偏置项，确认这部分代码正确执行，并且移除了正确的键。如果以上步骤都没有问题，那么可能是其他...

from sklearn.model_selection import GridSearchCV print("参数最优化：") xgb_model = xgb.XGBClassifier() param_dict = {'n_estimators': [10,50,100,150], 'learning_rate': [0.01,0.1,0.3]} clf = GridSearchCV(xgb_model, param_dict, verbose=1) clf.fit(X,Y) print(clf.best_score_) print(clf.best_params_)如何把这个代码可视化为热力图

clf = GridSearchCV(xgb_model, param_dict, verbose=1) clf.fit(X,Y) results = pd.DataFrame(clf.cv_results_) heatmap_data = pd.pivot_table(results, values='mean_test_score', index='param_n_estimators', ...

model.load_state_dict(torch.load('./%s/net_params_%d.pkl' %

model.load_state_dict(torch.load('./%s/net_params_%d.pkl' % (filename, epoch))) 这行代码的作用是从指定路径加载模型的参数。其中，'./%s/net_params_%d.pkl'是一个格式化字符串，用于指定加载参数文件的路径...

cfg_dict = hydra_params_to_dotdict(cfg) model = hydra.utils.instantiate(cfg.task_model, cfg_dict)

这段代码是使用 Hydra 库来实例化一个模型。...其中，cfg.task_model 表示配置文件中指定的模型类型，cfg_dict 包含了其他配置参数。通过这种方式，我们可以方便地使用 Hydra 管理和配置复杂的深度学习模型。

from sklearn.model_selection import train_test_split, GridSearchCV X_train, X_test, y_train, y_test = train_test_split(aac_all,label_all,test_size=0.2) from sklearn.linear_model import LogisticRegression from sklearn import metrics #First, an example for logistics regression cs = [1,3,5,7,10] param_grid = dict(C = cs) cls = LogisticRegression() grid = GridSearchCV(estimator=cls, param_grid=param_grid,cv = 5,scoring ='roc_auc') grid.fit(X_train, y_train) print("grid.best_params_") print(grid.best_params_) print("Best auc_roc on train set:{:.2f}".format(grid.best_score_)) print("Test set auc_roc:{:.2f}".format(grid.score(X_test,y_test))) y_predict = grid.predict(X_test) TN,FP,FN,TP = metrics.confusion_matrix(y_test, y_predict).ravel() recall = TP/(TP+FP) y_prob = grid.predict_proba(X_test) auroc = metrics.roc_auc_score(y_test, y_prob)

这段代码是一个使用逻辑回归进行分类任务的示例。首先，它导入了需要的库和函数：train_test_...请注意，代码中的print(grid.best_params_)和其他打印语句是为了展示结果，在实际使用时可以根据需要进行修改或删除。

{"method":"/algo/result","request":"{"user_input_params":{"cur_hour":"'1000'","limit_offset":"0","limit_size":"500","cur_day":"'20230605'"},"version":"main","tid":"generate_direct_purchase_task_v2","sid":"OMS"}","dsl":"cluster:bigdata GET store_product_auto_purchase_hourly/_search { "size" : 0, "query" : { "bool" : { "filter" : [ { "bool" : { "must" : [ { "term" : { "cur_day" : { "value" : "20230605", "boost" : 1.0 } } }, { "term" : { "cur_hour" : { "value" : "1000", "boost" : 1.0 } } } ], "adjust_pure_negative" : true, "boost" : 1.0 } } ], "adjust_pure_negative" : true, "boost" : 1.0 } }, "_source" : { "includes" : [ ], "excludes" : [ ] }, "aggregations" : { "result" : { "composite" : { "size" : 10000, "sources" : [ { "supplier_id" : { "terms" : { "field" : "supplier_id", "missing_bucket" : false, "order" : "asc" } } }, { "city_zip" : { "terms" : { "field" : "city_zip", "missing_bucket" : false, "order" : "asc" } } }, { "city_order_create_type" : { "terms" : { "field" : "city_order_create_type", "missing_bucket" : false, "order" : "asc" } } }, { "city_order_create" : { "terms" : { "field" : "city_order_create", "missing_bucket" : false, "order" : "asc" } } }, { "city_order_confirm_end" : { "terms" : { "field" : "city_order_confirm_end", "missing_bucket" : false, "order" : "asc" } } }, { "supply_model" : { "terms" : { "field" : "supply_model", "missing_bucket" : false, "order" : "asc" } } }, { "dc_store_delivery_start_time" : { "terms" : { "field" : "dc_store_delivery_start_time", "missing_bucket" : false, "order" : "asc" } } }, { "plan_sale_base_start" : { "terms" : { "field" : "plan_sale_base_start", "missing_bucket" : false, "order" : "asc" } } }, { "rule_detail_type" : { "terms" : { "field" : "rule_detail_type", "missing_bucket" : false, "order" : "asc" } } }, { "delivery_waves" : { "terms" : { "field" : "delivery_waves", "missing_bucket" : false, "order" : "asc" } } } ] }, "aggregations" : { "r_bucket_sort" : { "bucket_sort" : { "sort" : [ ], "from" : 0, "size" : 500, "gap_policy" : "SKIP" } } } } }}","total":0,"result":"[]"} 将上面的json转化为python字典

json_str = '{"method":"/algo/result","request":"{\\"user_input_params\\":{\\"cur_hour\\":\\"\'1000\'\\",\\"limit_offset\\":\\"0\\",\\"limit_size\\":\\"500\\",\\"cur_day\\":\\"\'20230605\'\\"},\\...

if epoch > 50 and epoch % 5 == 0: checkpoint = { 'epoch': epoch, 'net': model.state_dict(), 'optimizer': optimizer.state_dict(), } torch.save(checkpoint, "%s/net_params_%d.pth" % (model_dir, epoch))

具体来说，如果当前epoch大于50且epoch能够被5整除，就会将当前的模型参数、epoch数以及优化器参数保存到一个checkpoint中，并将其命名为 "net_params_{epoch}.pth"，其中{epoch}会被替换为当前的epoch数。...

【Code Practice】: Implementing GAN with TensorFlow_Keras: Beginners Can Also Get Started Easily

Generative Adversarial Networks (GAN) are a type of deep learning model comprised of two networks: a generator and a discriminator. They are trained through an adversarial process where the generator ...

解释这行代码best_models.append(Model(**dict(best_params_)))

dict(best_params_)将best_params_转换为一个字典对象，而Model(**dict(best_params_))则将该字典解包为单独的参数，创建一个新的Model对象。最后，该新对象将被添加到best_models列表中。因此，该行...

用代码实现：def nn_forward_pass(params: Dict[str, torch.Tensor], X: torch.Tensor): """ The first stage of our neural network implementation: Run the forward pass of the network to compute the hidden layer features and classification scores. The network architecture should be: FC layer -> ReLU (hidden) -> FC layer (scores) As a practice, we will NOT allow to use torch.relu and torch.nn ops just for this time (you can use it from A3). Inputs: - params: a dictionary of PyTorch Tensor that store the weights of a model. It should have following keys with shape W1: First layer weights; has shape (D, H) b1: First layer biases; has shape (H,) W2: Second layer weights; has shape (H, C) b2: Second layer biases; has shape (C,) - X: Input data of shape (N, D). Each X[i] is a training sample. Returns a tuple of: - scores: Tensor of shape (N, C) giving the classification scores for X - hidden: Tensor of shape (N, H) giving the hidden layer representation for each input value (after the ReLU). """

def nn_forward_pass(params: Dict[str, torch.Tensor], X: torch.Tensor): # extract the weights and biases from the dictionary W1 = params['W1'] b1 = params['b1'] W2 = params['W2'] b2 = params['b2']...

tuned_params, perfs = dict(), dict()

As an AI language model, I cannot provide a proper response without any context or information regarding the purpose of these variables. Could you please provide more details or code snippets so that ...

代码解读： cnn = model.CNN(img_size=config.img_size, num_class=num_class).to(device) cnn.load_state_dict( torch.load(os.path.join(config.model_path, config.model_name)) ) finalconv_name = 'conv' # hook feature_blobs = [] def hook_feature(module, input, output): feature_blobs.append(output.cpu().data.numpy()) cnn._modules.get(finalconv_name).register_forward_hook(hook_feature) params = list(cnn.parameters()) # get weight only from the last layer(linear) weight_softmax = np.squeeze(params[-2].cpu().data.numpy())

首先，代码通过调用 model.CNN 函数创建一个名为 cnn 的卷积神经网络模型，并将模型移动到指定的设备上。其输入图像的尺寸是 config.img_size，输出类别数目为 num_class。接下来，代码通过调用 torch....

for name, params in server.global_model.state_dict().items(): weight_accumulator[name] = torch.zeros_like(params)含义

for name, params in server.global_model.state_dict().items(): weight_accumulator[name] = torch.zeros_like(params) 的含义是对 server.global_model 模型中的每个参数，创建一个与其 size 相同的值都为 0 ...

ef train(args, model, train_loader, test_loader, boardio, textio, checkpoint): #learnable_params = filter(lambda p: p.requires_grad, model.parameters()) #optimizer = torch.optim.Adam(learnable_params) optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 100, 150], gamma=0.1) if checkpoint is not None: min_loss = checkpoint['min_loss'] optimizer.load_state_dict(checkpoint['optimizer']) best_test_loss = np.inf best_test_mse_ab = np.inf best_test_rmse_ab = np.inf best_test_mae_ab = np.inf best_test_r_mse_ab = np.inf best_test_r_rmse_ab = np.inf best_test_r_mae_ab = np.inf best_test_t_mse_ab = np.inf best_test_t_rmse_ab = np.inf best_test_t_mae_ab = np.inf for epoch in range(args.epochs): train_loss, train_mse_ab, train_mae_ab, train_rotations_ab, train_translations_ab, train_rotations_ab_pred, \ train_translations_ab_pred, train_eulers_ab, = train_one_epoch(args.device, model, train_loader, optimizer) test_loss, test_mse_ab, test_mae_ab, test_rotations_ab, test_translations_ab, test_rotations_ab_pred, \ test_translations_ab_pred, test_eulers_ab = test_one_epoch(args.device, model, test_loader)设置动态学习率

这段代码是用来设置动态学习率的。它使用了PyTorch中的lr_scheduler模块，具体来说，使用了MultiStepLR策略。这个策略会在训练过程中根据指定的milestones（里程碑）来调整学习率，每次乘以gamma（衰减因子）。...

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1.版本：matlab2014/2019a/2024a 2.附赠案例数据可直接运行matlab程序。 3.代码特点：参数化编程、参数可方便更改、代码编程思路清晰、注释明细。 4.适用对象：计算机，电子信息工程、数学等专业的大学生课程设计、期末大作业和毕业设计。替换数据可以直接使用，注释清楚，适合新手

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model_params:dict

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openpose_tennis

model.load_state_dict(torch.load('./%s/net_params_%d.pkl' %

cfg_dict = hydra_params_to_dotdict(cfg) model = hydra.utils.instantiate(cfg.task_model, cfg_dict)

if epoch > 50 and epoch % 5 == 0: checkpoint = { 'epoch': epoch, 'net': model.state_dict(), 'optimizer': optimizer.state_dict(), } torch.save(checkpoint, "%s/net_params_%d.pth" % (model_dir, epoch))

【Code Practice】: Implementing GAN with TensorFlow_Keras: Beginners Can Also Get Started Easily

解释这行代码best_models.append(Model(**dict(best_params_)))

tuned_params, perfs = dict(), dict()

for name, params in server.global_model.state_dict().items(): weight_accumulator[name] = torch.zeros_like(params)含义

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