and ModelInspector) for generating synthetic data. It alsohas a GUI(a Web app based on Django) that enables you to test it directly without coding. In addition, it has three different ways to generate data: ran
X_train, y_train = generate_synthetic_data(num_samples, input_shape, num_classes) X_val, y_val = generate_synthetic_data(num_samples // 4, input_shape, num_classes) # 模型训练 model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, batch_size=32) 通过上述例子,...
features, labels = synthetic_data(true_w, true_b, 100) def load_array(data_arrays, batch_size, is_Train=True): '''构造一个PyTorch数据迭代器''' dataset = data.TensorDataset(*data_arrays) ## 生成一个datasets的向量 return data.DataLoader(dataset, batch_size, shuffle=is_Train) batch_size ...
features,labels = synthetic_data(true_w,true_b,1000) print('features:',features[0],'\nlabel:',labels[0]) d2l.set_figsize() d2l.plt.scatter(features[:,1].detach().numpy(),labels.detach().numpy(),1) '''展示你的画图''' # d2l.plt.show() '''定义一个data_iter函数,该函数接收批量...
The Role of Generative AI in Synthetic Data Getting Started with Synthetic Data Generation Conclusion FAQs In the post-GPT world, the demand for high-quality datasets has never been greater. Generative AI and large language models all hinge on the availability of robust, high-quality, and high-...
用下面的函数来生成训练样本 def synthetic_data(w, b, num_examples): """生成y=w1*x1+w2*x2+b训练样本""" X = np.random.normal(0, 1, (num_examples, len(w))) y = np.dot(X, w) + b y += np.random.normal(0, 0.01, y.shape) ...
print(f'weight:{self.net[0].weight.data}') print(f'bias:{self.net[0].bias.data}') def main(): true_w = torch.tensor([2, -3.4]) true_b = 4.2 features, labels = d2l.synthetic_data(true_w, true_b, 1000) linearR = linearReg(features, labels) linearR.fit() linearR.print_...
compare_2d(df, synthetic) 技术要点总结 Copula框架提供了边际分布与依赖结构的解耦机制,使得统计建模具有高度的可定制性和适应性。Copula在金融领域(如资产相关性建模)、气象学等需要处理复杂依赖结构的领域有着广泛应用。现代统计计算库(如 copulas )为实现基于copula的模型提供了高效的工具支持,便于在实际应用中进行...
创建合成数据（Creating synthetic data）：通过特定方法生成新的样本，以增加数据集中的稀少类别样本。欠采样和过采样的组合（Combination of under and over sampling）：结合上述两种方法，灵活调整数据集的平衡。接下来，让我们探讨如何评估聚类模型的性能。在无监督学习中，评估聚类效果通常是一个挑战，因为聚类模型没...
2. 合成控制法 (Synthetic Control) # 2. 合成控制法 (Synthetic Control)# 导入所需的库fromscipy.optimizeimportminimizeimportnumpyasnpimportpandasaspd# 生成模拟数据(1个处理单元,5个控制单元,10年)np.random.seed(42)years=10n_control=5# 生成处理单元的数据treated_data=np.random.normal(0,1,years)+np...