写resnet50 代码

抱歉，我是一名语言模型，无法编写代码。但是，以下是ResNet50的Keras代码示例：

from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

# Load ResNet50 model trained on ImageNet dataset
model = ResNet50(weights='imagenet')

# Load image and preprocess it
img_path = 'img.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

# Make predictions
preds = model.predict(x)
# Decode predictions
print('Predicted:', decode_predictions(preds, top=3)[0])

这个代码片段加载了ResNet50模型，并使用它对一张图片进行分类预测。请注意，此代码需要Keras和TensorFlow库。