# lab5

**Repository Path**: johnny1128/lab5

## Basic Information

- **Project Name**: lab5
- **Description**: test5test5
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-06-09
- **Last Updated**: 2022-06-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# # 使用TensorFlow Lite Model Maker生成图像分类器模型
1. 打开pycharm，安装程序运行所需的一些库
	> pip install tflite-model-maker

    ![图片](./image/1.jpg)

2. 模型训练代码

    import os  
  
	import numpy as np  
	import matplotlib.pyplot as plt  
	import tensorflow as tf  
	assert tf.__version__.startswith('2')  
  
	from tflite_model_maker import model_spec  
	from tflite_model_maker import image_classifier  
	from tflite_model_maker.config import ExportFormat  
	from tflite_model_maker.config import 			QuantizationConfig  
	from tflite_model_maker.image_classifier import DataLoader  
  
  
  
	image_path = tf.keras.utils.get_file(  
    'flower_photos.tgz',  
  'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',  
  extract=True)  
image_path = os.path.join(os.path.dirname(image_path), 'flower_photos')  
  
	data = DataLoader.from_folder(image_path)  
train_data, test_data = data.split(0.9)  
  
	inception_v3_spec = image_classifier.ModelSpec(  
    uri='https://storage.googleapis.com/tfhub-modules/tensorflow/efficientnet/lite0/feature-vector/2.tar.gz')  
inception_v3_spec.input_image_shape = [240, 240]  
model = image_classifier.create(train_data, model_spec=inception_v3_spec)  
  
	loss, accuracy = model.evaluate(test_data)  
  
	model.export(export_dir='.')

3. 运行代码
![图片](./image/2.jpg)
生成model.tflite文件
![图片](./image/3.jpg)

4. 将model.tflite文件在实验3中运行
![图片](./image/4.jpg)

![输入图片说明](images/29.png)