### 常见的Python机器学习库 #### Scikit-learn Scikit-learn是一个简单易用的机器学习库，提供广泛的算法和模型，包括分类、回归、聚类等。应用场景广泛，适用于通用机器学习任务、数据挖掘、学术研究以及教育等领域[^4]。 ```python from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier iris = datasets.load_iris() X, y = iris.data, iris.target X_train, X_test, y_train, y_test = train_test_split(X, y) knn = KNeighborsClassifier(n_neighbors=3) knn.fit(X_train, y_train) print(knn.score(X_test, y_test)) ``` #### TensorFlow TensorFlow是由Google开发的一个开源软件库，主要用于数值计算和大规模机器学习。支持多种类型的神经网络和其他复杂的机器学习模型构建与训练。因其强大的功能，在工业界和学术界都得到了广泛应用。 ```python import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10) ]) predictions = model(x_train[:1]).numpy() loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy']) model.fit(x_train, y_train, epochs=5) ``` #### PyTorch PyTorch由Facebook开发，具备良好的Python集成性和动态计算图特性，易于上手且灵活性高。近年来在深度学习社区内迅速崛起并获得大量开发者青睐[^2]。 ```python import torch import torchvision import torchvision.transforms as transforms transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') import matplotlib.pyplot as plt import numpy as np def imshow(img): img = img / 2 + 0.5 # unnormalize npimg = img.numpy() plt.imshow(np.transpose(npimg, (1, 2, 0))) plt.show() dataiter = iter(trainloader) images, labels = dataiter.next() imshow(torchvision.utils.make_grid(images)) print(' '.join('%5s' % classes[labels[j]] for j in range(4))) ```