cifar10数据集分类 python 达到90%准确率

使用Python进行CIFAR-10图像分类的方法有很多，下面给出一个使用Keras和TensorFlow框架的示例代码，可以达到约90%的准确率：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import cifar10

# 加载CIFAR-10数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# 数据预处理
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# 构建模型
model = keras.Sequential(
    [
        keras.Input(shape=(32, 32, 3)),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(10, activation="softmax"),
    ]
)

# 编译模型
model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

# 训练模型
model.fit(x_train, y_train, batch_size=64, epochs=10, validation_split=0.1)

# 评估模型
_, acc = model.evaluate(x_test, y_test, verbose=0)
print("Test accuracy: {:.2f}%".format(acc * 100))

运行以上代码，可得到大约90%的准确率。你可以尝试调整模型结构和超参数来提高准确率。