권장 인공 지능 개발 라이브러리: AI 개발 효율성을 향상시키기 위해 선호되는 도구

import tensorflow as tf from tensorflow import keras # 导入数据集 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() # 构建模型 model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10, activation='softmax') ]) # 编译和训练模型 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5) # 评估模型 test_loss, test_acc = model.evaluate(x_test, y_test) print('Test accuracy:', test_acc)

import torch import torch.nn as nn import torch.optim as optim # 定义模型 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size): super(LSTM, self).__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) out, _ = self.lstm(x, (h0, c0)) out = self.fc(out[:, -1, :]) return out # 导入数据集 train_dataset = ... train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True) # 构建模型 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = LSTM(input_size, hidden_size, num_layers, output_size).to(device) # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 训练模型 total_step = len(train_loader) for epoch in range(num_epochs): for i, (sequences, labels) in enumerate(train_loader): sequences = sequences.to(device) labels = labels.to(device) # 前向传播和反向传播 outputs = model(sequences) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() if (i + 1) % 100 == 0: print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))

from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score # 导入数据集 iris = datasets.load_iris() X = iris.data y = iris.target # 数据集划分 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 构建模型 knn = KNeighborsClassifier(n_neighbors=3) # 模型训练 knn.fit(X_train, y_train) # 模型预测 y_pred = knn.predict(X_test) # 模型评估 accuracy = accuracy_score(y_test, y_pred) print('Accuracy:', accuracy)