AI生成文章技术在纳米材料表征与信息呈现中的应用实践教程

import numpy as np
import pandas as pd
from skimage import io
from sklearn.preprocessing import StandardScaler

def preprocess_tem_image(image_path):
"""预处理TEM图像"""
img = io.imread(image_path)
归一化
img = img.astype(np.float32) / 255.0
转换为灰度图
gray_img = np.mean(img, axis=2)
return gray_img.flatten()

def preprocess_xrd_data(xrd_path):
"""预处理XRD数据"""
data = pd.read_csv(xrd_path, sep='t', header=None)
标准化
scaler = StandardScaler()
processed_data = scaler.fit_transform(data)
return processed_data

示例预处理
tem_features = preprocess_tem_image("sample_tem_001.jpg")
xrd_features = preprocess_xrd_data("sample_xrd_001.txt")

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

加载模型和分词器
tokenizer = AutoTokenizer.from_pretrained("Text-to-Image-Generation")
model = AutoModelForCausalLM.from_pretrained("Text-to-Image-Generation").to('cuda')

模型配置
config = {
"model_name": "Text-to-Image-Generation",
"batch_size": 16,
"max_length": 1024,
"temperature": 0.7,
"top_p": 0.95,
"device": "cuda"
}

def generate_report(tem_features, xrd_features, template_path="report_template.txt"):
"""生成纳米材料表征报告"""
加载报告模板
with open(template_path, 'r') as f:
template = f.read()

构建输入
input_ids = tokenizer(template, return_tensors="pt").input_ids.to(config['device'])

生成报告
with torch.no_grad():
outputs = model.generate(
input_ids,
max_length=config['max_length'],
temperature=config['temperature'],
top_p=config['top_p'],
do_sample=True
)

解码输出
report = tokenizer.decode(outputs[0], skip_special_tokens=True)

插入表征数据
report = report.replace("{TEM_FEATURES}", str(tem_features))
report = report.replace("{XRD_FEATURES}", str(xrd_features))

return report

生成报告
report_text = generate_report(tem_features, xrd_features)
print(report_text)

from transformers import Trainer, TrainingArguments
from datasets import load_dataset

加载数据集
dataset = load_dataset("nanomaterial_reports")

定义训练参数
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=8,
save_steps=10_000,
save_total_limit=2,
)

创建Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
)

开始训练
trainer.train()