associated with toxicity outnumbered neutral comments regarding the same identity ---- Dataset dataset labeled...[train_labeled_df['target'] >= .5][identities] non_toxic_df = train_labeled_df[train_labeled_df['target...== 0, other = 1).sum() non_toxic_count = non_toxic_df.where(train_labeled_df == 0, other = 1).sum()...[:, 1:].multiply(train_labeled_df.iloc[:, 0], axis="index").sum() # changing the value of identity to...1 or 0 only and get comment count per identity group identity_label_count = train_labeled_df[identities
= 20 X_labeled = torch.tensor(X[:n_labeled], dtype=torch.float32) y_labeled = torch.tensor(y[:n_labeled..., y_labeled, X_unlabeled, adj_matrix, lambda_reg): labeled_loss = criterion(model(X_labeled), y_labeled...# 扩展标记数据集 X_labeled = torch.cat((X_labeled, X_pseudo_labeled)) y_labeled = torch.cat((y_labeled..., y_pseudo_labeled)) # 重新训练模型 dataset = TensorDataset(X_labeled, y_labeled) dataloader =..., y_labeled, X_unlabeled, lambda_reg): labeled_loss = criterion(model(X_labeled), y_labeled)
from sklearn.semi_supervised import LabelPropagation # 构建有标签数据和无标签数据 X_labeled, X_unlabeled, y_labeled..., X_unlabeled)) y_combined = np.concatenate((y_labeled, y_unlabeled)) # 训练标签传播模型 label_propagation =...y_pred_unlabeled = np.argmax(model.predict(x_unlabeled), axis=1) x_labeled = np.vstack((x_labeled..., x_unlabeled)) y_labeled = np.concatenate((y_labeled, y_pred_unlabeled)) model.fit(x_labeled..., y_labeled, epochs=5, validation_data=(x_test, y_test), verbose=2) model2.fit(x_labeled, y_labeled,
OJ’s undirected graph serialization: Nodes are labeled uniquely....First node is labeled as 0. Connect node 0 to both nodes 1 and 2. Second node is labeled as 1....Third node is labeled as 2. Connect node 2 to node 2 (itself), thus forming a self-cycle.
用标注数据和 pseudo-labeled 数据一起来更新 CNN 模型。...我们通过逐步增大每次加入训练的 pseudo-labeled 数据量，从而逐渐去利用更难识别的，包含更多信息多样性的视频片段。...这里有两个值得注意的点： （1）如何决定每次选取多少 pseudo-labeled 数据做训练 我们用一种动态测量，逐渐增加选取的样本。...在开始的循环中，只有一小部分 pseudo-labeled 数据被选中，之后会有越来越多样本被加进来。...下面我们展示一下算法选出来的 pseudo-labeled 样本。 ?
(num_line); for(int i=0;i<num_line;i++) { dist[i]=BIG; labeled[i]=false; } dist[0]=0;...labeled[0]=true; int current_line=0; for(int i=1;i<num_line;i++) { for(int d=current_line+1...labeled[d]) { if (dist[current_line]+isPalindrome[current_line*num_line+d]<dist[d]) {...labeled[i]) { if(mindist>dist[i]) { minindex=i; mindist=dist[i]; }...} } current_line=minindex; labeled[minindex]=true; } return dist[num_line-1]-1; } }; 参考推荐
OJ's undirected graph serialization: Nodes are labeled uniquely....First node is labeled as 0. Connect node 0 to both nodes 1 and 2. Second node is labeled as 1....Third node is labeled as 2. Connect node 2 to node 2 (itself), thus forming a self-cycle.
= les[l].transform(dedups[l]) dedups.loc[:, l + '_feat'] = pd.Series(tr, index=dedups.index) labeled...['name_feat'].unique()) / len(labeled['name_feat']) #输出：0.6224184813880769 #name列的标签占总数的62%。...labeled.drop(['name_feat'], axis='columns', inplace=True) 05 关联性分析 让我们看看功能如何相互关联，更重要的是，与价格。...代码： #所有属性间的关联 plot_correlation_map(labeled) labeled.corr() labeled.corr().loc[:,'price'].abs().sort_values...06 准备模型 代码： Y = labeled['price'] X = labeled.drop(['price'], axis='columns', inplace=False) matplotlib.rcParams
在这种情况下使用Labeled BGP(带有标签分发能力的BGPV4)而不是VxLAN也是个很好的选择。...Labeled BGP标签映射信息被携带在多协议扩展属性的NLRI中。AFI标识关联的路由条目，SAFI值为4表示NLRI包含标签。...服务提供商通过支持MPLS和Labeled BGP的路由器，可以使用MPLS在数据中心之间建立隧道，BGP标签则作为在不同BGP之间交换MPLS标签的信令机制。...三、白盒交换机的支持 随着SDN技术的发展以及相应带动的白盒化进程，基于商用交换芯片的普通白牌交换机也可以支持丰富的路由功能包括Labeled BGP。...VxLAN对于企业实现大二层是个扩展性很强的新技术，但是对于现有设备已经支持MPLS的服务商来讲，Labeled BGP等老技术仍然可以开新花。
, 1-labeled_portion]) labeled_data = data[mask, :] unlabeled_data = data[~mask, :] labeled_data_labels..., labeled_data_labels, epochs=epochs, batch_size=batch_size, verbose=0) acc_model1.append(sum(argmax..., 1-labeled_portion]) labeled_data = data[mask, :] unlabeled_data = data[~mask, :] labeled_data_labels..., labeled_data_labels, epochs=epochs, batch_size=batch_size, shuffle=False, verbose=0) acc_model2.append..., 1-labeled_portion]) unlabeled_data_labels = labels[~mask] # Randomize the labels of unlabeled
\(\*SubscriptBox[\(y\), \(0\)]\)"}, 0.001, 8, Appearance -> "Labeled"}, {{r, 0.15, "growth rate...r"}, .01, 2, Appearance -> "Labeled"}, {{M, 4, "carrying capacity M"}, .01, 10, Appearance -> "Labeled
System 1, labeled as “Reflexive,” is depicted in a yellow cloud shape....An arrow labeled “Evolution” points towards it, indicating its development over time....System 2, also enclosed in a yellow cloud shape and labeled as “Deliberative,” represents reasoning from...It’s connected to an icon representing the internet and labeled “Pretraining” and another icon representing...task-specific data labeled “Finetuning.”
list(parent_dir.iterdir()) def labeler(example, index): return example, tf.cast(index, tf.int64) labeled_data_sets...file_name in enumerate(FILE_NAMES): lines_dataset = tf.data.TextLineDataset(str(parent_dir/file_name)) labeled_dataset...= lines_dataset.map(lambda ex: labeler(ex, i)) labeled_data_sets.append(labeled_dataset) 如上图所示，我们可以看到...lower_case = tf_text.case_fold_utf8(text) return tokenizer.tokenize(lower_case) tokenized_ds = all_labeled_data.map...上图是关于把 raw text 转化成 tokens 的展示结果 接下来，我们需要对数据集进行划分，然后再创建模型，最后就可以开始训练了，代码如下所示 all_encoded_data = all_labeled_data.map
= ctrl_adata.copy().copy() labeled_ctrl_adata.obs['is_ct'] = labeled_ctrl_adata.obs['ct'].str.contains...(ct).astype(int) memento.create_groups(labeled_ctrl_adata, label_columns=['is_ct', 'ind', 'ct'...(tf_list) & set(labeled_ctrl_adata.var.index) available_genes = set(labeled_ctrl_adata.var.index)...print(len(available_tfs),labeled_ctrl_adata.shape) memento.compute_2d_moments(labeled_ctrl_adata,..._1d_moments(labeled_ctrl_adata, min_perc_group=.7)memento.compute_2d_moments(labeled_ctrl_adata, list
n = 800 # 样本数 n_labeled = 10 # 有标签样本数 X, Y = make_moons(n, shuffle=True, noise=0.1, random_state=1000...{save_dir}/bi_classification.pdf", format='pdf') plt.show() Y_input = np.concatenate((one_hot(Y[:n_labeled...], 2), np.zeros((n-n_labeled, 2)))) ?...y_true = Y[numLabels:].astype(y_pred.dtype) return y_true, y_pred Y_input = np.concatenate((Y[:n_labeled...], -np.ones(n-n_labeled))) y_true, y_pred = pred_lgc(X, Y, Y_input, n_labeled) print(metrics.classification_report
as bear classified by model as bear: 2 times Examples labeled as bear classified by model as deer: 1...times Examples labeled as deer classified by model as deer: 1 times Examples labeled as deer classified...by model as duck: 1 times Examples labeled as deer classified by model as turtle: 1 times Examples labeled...as duck classified by model as duck: 3 times Examples labeled as duck classified by model as turtle:...1 times Examples labeled as turtle classified by model as deer: 1 times Examples labeled as turtle classified
Their puzzles consist of a 2 × 2 grid and three tiles labeled ‘A’, ‘B’, and ‘C’....Since the tiles are labeled with letters, rotations and reflections are not allowed....The positions of the tiles are labeled ‘A’, ‘B’, and ‘C’, while the empty cell is labeled ‘X’.
logits= D_real_logit, labels= extended_label) # Labeled_mask...softmax cross_entropy calculated over whole batch D_L_Supervised= tf.reduce_sum(tf.multiply(temp,labeled_mask...))/ tf.reduce_sum(labeled_mask) # Multiplying temp with labeled_mask gives supervised loss on labeled_mask...# data only, calculating mean by dividing by no of labeled samples # Unsupervised loss -> R/F...label : batch[1], labeled_mask : mask, dropout_rate :0.7, is_training
transfer learning的setting，是假设我们能够接触到足够多的目标数据集的labeled data的，但在实际应用时，往往目标数据集的labeled data是不足的。...其实，这个在transfer learning目标域labeled data不足的setting，就是咱常说的few-shot image classification，或者也可以叫做few-shot...，但没有降低类别数目，而这些数据集上类别数目都很大，后来我自己做了实验，发现当类别数目变小时两种方法差异更大，这表示finetune效果与labeled data数据总量正相关。...这里再举另外一个例子，由于 目标域labeled data少 目标域类别在训练时没见过 因此backbone网络会不知道在纷繁复杂的图片应该关注什么信息。...可以看到，训练学得一个good representation，和测试时从有限labeled data建立一个好的分类器在一般的任务中是可以统一起来的。
领取专属 10元无门槛券
手把手带您无忧上云
云服务器
ICP备案
对象存储
即时通信 IM
实时音视频
