使用tensorflow2.0版本，使用卷积神经网络对CIFAR-100数据集进行各种数据处理来提高模型准确率后给出训练后的模型的最终得分

好的，以下是使用TensorFlow 2.0版本，使用卷积神经网络对CIFAR-100数据集进行各种数据处理来提高模型准确率后给出训练后的模型的最终得分的步骤：

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

# 数据增强
data_augmentation = keras.Sequential(
    [
        layers.experimental.preprocessing.RandomFlip("horizontal"),
        layers.experimental.preprocessing.RandomRotation(0.1),
        layers.experimental.preprocessing.RandomZoom(0.1),
    ]
)

4. 定义模型

model = keras.Sequential(
    [
        keras.Input(shape=(32, 32, 3)),
        data_augmentation,
        layers.Conv2D(32, 3, activation="relu", padding="same"),
        layers.BatchNormalization(),
        layers.Conv2D(32, 3, activation="relu", padding="same"),
        layers.BatchNormalization(),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Dropout(0.2),
        layers.Conv2D(64, 3, activation="relu", padding="same"),
        layers.BatchNormalization(),
        layers.Conv2D(64, 3, activation="relu", padding="same"),
        layers.BatchNormalization(),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Dropout(0.3),
        layers.Conv2D(128, 3, activation="relu", padding="same"),
        layers.BatchNormalization(),
        layers.Conv2D(128, 3, activation="relu", padding="same"),
        layers.BatchNormalization(),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Dropout(0.4),
        layers.Flatten(),
        layers.Dense(256, activation="relu"),
        layers.BatchNormalization(),
        layers.Dropout(0.5),
        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=100, 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])

本次数据处理采用了数据增强、批量标准化和dropout来提高模型的准确率。最终的模型得分将在测试集上显示。