推荐的人工智能开发库：提高AI开发效率的首选工具

import tensorflow as tffrom 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 torchimport torch.nn as nnimport 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 datasetsfrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.metrics import accuracy_score# 导入数据集iris = datasets.load_iris()X = iris.datay = 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)