keras深度学习训练结果可视化

runner668

于 2018-01-28 10:44:00 发布

阅读量9.3k

点赞数

分类专栏： python

本文链接：https://blog.youkuaiyun.com/runner668/article/details/79185137

版权

该博客展示了如何利用Keras对预训练的VGG16模型进行微调，以对猫狗分类任务进行训练，并通过ImageDataGenerator进行数据增强。博主创建了一个包含2000张训练样本和800张验证样本的数据集，模型最终被保存为'fine_tune_model.h5'和'fine_tune_model_weight'。博客还包含了训练过程中的损失和准确率的可视化图表。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
- put the cat pictures index 1000-1400 in data/validation/cats
- put the dogs pictures index 12500-13499 in data/train/dogs
- put the dog pictures index 13500-13900 in data/validation/dogs
So that we have 1000 training examples for each class, and 400 validation examples for each class.
In summary, this is our directory structure:
```
data/
    train/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg

最低0.47元/天解锁文章