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 ...
model_synthetic=ElasticNet() model_synthetic.fit(X_synthetic, y_synthetic) # 在真实测试集上评估性能 synthetic_score=model_synthetic.score(X_test, y_test) print(f"Performance of model trained on synthetic data: {synthetic_score:.4f}") 同时,我们训练一个基于原始数据的对照模型: # 基于原始数据训...
Learn about synthetic data generation using Python in this hands-on guide. Explore techniques, tools, and code examples to enhance AI and machine learning models.
x.grad.data.zero_() y.backward(torch.ones_like(x),retain_graph=True) d2l.plot(x.detach(), x.grad, 'x', 'grad of sigmoid', figsize=(5, 2.5)) 1. 2. 3. 4. ③tanh函数又称挤压函数(squashing function) 公式: 该激活函数强制将范围**(-∞,+∞)的任意输入压缩为(0,1)**的某一值...
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) ...
For all of these reasons, making use of synthetic data is a good alternative, since it can fulfill the same needs with little effort. In this article, we will introduce you to ten Python libraries that enable you to produce synthetic data for specific business contexts. But first we need ...
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...
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_...
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...