如何在不同形状的数据集上实现VGG-net？

import tensorflow as tf
from tensorflow.keras import layers, models

def create_vgg(input_shape):
    model = models.Sequential([
        layers.Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_shape),
        layers.Conv2D(64, (3, 3), activation='relu', padding='same'),
        layers.MaxPooling2D((2, 2), strides=(2, 2)),
        
        layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
        layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
        layers.MaxPooling2D((2, 2), strides=(2, 2)),
        
        layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
        layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
        layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
        layers.MaxPooling2D((2, 2), strides=(2, 2)),
        
        layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
        layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
        layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
        layers.MaxPooling2D((2, 2), strides=(2, 2)),
        
        layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
        layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
        layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
        layers.MaxPooling2D((2, 2), strides=(2, 2)),
        
        layers.Flatten(),
        layers.Dense(4096, activation='relu'),
        layers.Dropout(0.5),
        layers.Dense(4096, activation='relu'),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation='softmax')
    ])
    
    return model

# Example usage
input_shape = (224, 224, 3)  # Adjust based on your dataset
num_classes = 10  # Number of classes in your dataset
model = create_vgg(input_shape)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])