Garbage first
时间: 2023-09-05 10:11:20 浏览: 41
Garbage First (G1) 是一种Java虚拟机的垃圾回收器,它被设计用于替代传统的分代垃圾回收器。G1的主要目标是在有限的时间内实现可控制的低延迟垃圾回收,并尽量避免发生长时间的停顿。它使用了一种基于区域的内存管理方式,将堆内存分割成许多大小相等的区域,这些区域可以是Eden区、幸存者区或者老年代区。G1采用了增量并发收集算法,能够与应用程序线程并发执行,以减少停顿时间。同时,G1还具备自适应的调整能力,根据应用程序的运行情况动态调整垃圾回收策略。
相关问题
use python to programing a garbage classification program
As an AI language model, I don't have the capability to write a full-fledged program in Python. However, I can provide some guidance on how you can program a garbage classification program in Python.
1. Collect Data: The first step is to collect and label the images that you will use to train your model. You can use online datasets or create your own.
2. Preprocess the Data: You need to preprocess the data by resizing the images, normalizing the pixel values, and splitting the data into training and testing sets.
3. Build the Model: You can use different deep learning frameworks such as TensorFlow or PyTorch to build your garbage classification model. You can use pre-trained models such as ResNet or VGGNet as a starting point.
4. Train the Model: You need to train the model on the training data by feeding the images into the model and adjusting the weights to minimize the loss.
5. Evaluate the Model: You need to evaluate the model on the testing data to see how well it performs. You can calculate metrics such as accuracy, precision, recall, and F1 score.
6. Use the Model: Once you have trained and evaluated the model, you can use it to classify new images of garbage.
Here is some sample code to get you started:
```
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# define data directories
train_dir = 'path/to/training/data'
test_dir = 'path/to/testing/data'
# define data generators
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
# define batch size and image size
batch_size = 32
img_size = (224, 224)
# define training and testing data generators
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=img_size,
batch_size=batch_size,
class_mode='categorical')
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=img_size,
batch_size=batch_size,
class_mode='categorical')
# define the model
model = tf.keras.applications.ResNet50(
include_top=True,
weights=None,
input_shape=(224, 224, 3),
classes=6)
# compile the model
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# train the model
model.fit(train_generator,
epochs=10,
validation_data=test_generator)
# evaluate the model
model.evaluate(test_generator)
```
This is just a basic example of how you can build a garbage classification program in Python. You can modify and improve the model according to your specific requirements.
JDK 10 11 有什么新特性
JDK 10和11的新特性如下:
1. JDK 10新增了局部变量类型推断(var)。通过用var关键字代替类型来声明局部变量,可以使代码更简洁易读。
2. JDK 10还加入了一种新的垃圾回收器,名为G1 GC(Garbage First Garbage Collector)。它运行时能够根据堆的内存使用情况自动调整垃圾回收的行为,能够更快捷、更高效地进行垃圾回收。
3. JDK 11新增了Http Client API。这是一个全新的HTTP客户端API,支持HTTP/1.1和HTTP/2协议,并支持async/await异步编程模型,具有更好的性能、扩展性和安全性。
4. JDK 11加入了一种基于内存的JVM元数据存储模式。这种新模式取代了以前的基于文件的存储方式,可以为应用程序提供更加快速、可靠的元数据存储。
5. JDK 11还增强了ZGC(Z Garbage Collector)垃圾回收器,支持更多的操作系统平台,性能更高,具有更好的可伸缩性,能够更好地满足企业级应用的需求。