機械学習モデルの過学習問題

from keras.preprocessing.image import ImageDataGenerator

# 定义数据扩充器
datagen = ImageDataGenerator(
    rotation_range=20,  # 随机旋转角度范围
    width_shift_range=0.1,  # 水平平移范围
    height_shift_range=0.1,  # 垂直平移范围
    shear_range=0.2,  # 剪切变换范围
    zoom_range=0.2,  # 缩放范围
    horizontal_flip=True,  # 随机水平翻转
    fill_mode='nearest'  # 填充模式
)

# 加载图像数据集
train_data = datagen.flow_from_directory("train/", target_size=(224, 224), batch_size=32, class_mode='binary')
test_data = datagen.flow_from_directory("test/", target_size=(224, 224), batch_size=32, class_mode='binary')

# 训练模型
model.fit_generator(train_data, steps_per_epoch=len(train_data), epochs=10, validation_data=test_data, validation_steps=len(test_data))

import torch
import torch.nn as nn

# 定义模型
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(10, 10)
        self.fc2 = nn.Linear(10, 1)
    
    def forward(self, x):
        x = self.fc1(x)
        x = nn.ReLU()(x)
        x = self.fc2(x)
        return x

model = MyModel()

# 定义损失函数
criterion = nn.MSELoss()

# 定义优化器
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, weight_decay=0.001)  # 注意weight_decay参数即为正则化项的系数

# 训练模型
for epoch in range(100):
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

import tensorflow as tf

# 定义模型
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(10,)),
    tf.keras.layers.Dropout(0.5),  # dropout率为0.5
    tf.keras.layers.Dense(1)
])

# 编译模型
model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True))

# 训练模型
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))