如何从训练数据和预期结果列表中创建tensorflow数据库，以及如何构建独特的网络形状

import tensorflow as tf

# 示例数据
train_data = [...]  # 你的训练数据列表
train_labels = [...]  # 你的预期结果列表

# 创建数据集
dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))

# 数据预处理（例如，归一化）
def preprocess(x, y):
    x = x / 255.0  # 假设x是图像数据
    return x, y

dataset = dataset.map(preprocess)

# 批处理和打乱数据
batch_size = 32
dataset = dataset.shuffle(buffer_size=len(train_data)).batch(batch_size)

from tensorflow.keras import layers, models

def create_model(input_shape, num_classes):
    model = models.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(128, (3, 3), activation='relu'),
        layers.Flatten(),
        layers.Dense(128, activation='relu'),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation='softmax')
    ])
    return model

# 示例输入形状和类别数
input_shape = (64, 64, 3)  # 假设图像大小为64x64，3个通道（RGB）
num_classes = 10  # 假设有10个类别

model = create_model(input_shape, num_classes)
model.summary()