Bibliothèque de développement d'intelligence artificielle recommandée : l'outil privilégié pour améliorer l'efficacité du développement de l'IA

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)