使用tensorflow2.0版本，使用卷积神经网络对CIFAR-100数据集进行数据预处理并给出训练后的模型得分

好的，以下是使用TensorFlow 2.0版本，使用卷积神经网络对CIFAR-100数据集进行数据预处理并给出训练后的模型得分的步骤：

1. 导入必要的库

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import cifar100

2. 加载数据集

(x_train, y_train), (x_test, y_test) = cifar100.load_data()

3. 数据预处理

x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

y_train = keras.utils.to_categorical(y_train, 100)
y_test = keras.utils.to_categorical(y_test, 100)

4. 定义模型

model = keras.Sequential(
    [
        keras.Input(shape=(32, 32, 3)),
        layers.Conv2D(32, 3, activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, 3, activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(128, 3, activation="relu"),
        layers.Flatten(),
        layers.Dense(256, activation="relu"),
        layers.Dense(100, activation="softmax"),
    ]
)

5. 编译模型并训练

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=64, epochs=10, validation_split=0.2)

6. 评估模型

score = model.evaluate(x_test, y_test, batch_size=64, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

最终输出的模型得分将在测试集上显示。