Tensorflow 之finetune微调模型方法&&不同层上设置不同的学习率
ConvNets:
AlexNet finetune:
- 自己搭建的网络，加载初始化模型：
def load_with_skip(data_path, session, skip_layer):
data_dict = np.load(data_path).item()
for key in data_dict:
if key not in skip_layer:
with tf.variable_scope(key, reuse=True):
for subkey, data in zip(('weights', 'biases'), data_dict[key]):
session.run(tf.get_variable(subkey).assign(data))
print('Load pre-trained model: {}'.format(weight_file))
load_with_skip(weight_file, sess, ['fc8']) # Skip weights from fc8- https://github.com/joelthchao/tensorflow-finetune-flickr-style
- https://github.com/kratzert/finetune_alexnet_with_tensorflow
VGG模型finetune:
- 自定义网络；加载参数，很详细教程：
- Tensorflow学习笔记：CNN篇（9）——Finetuning，复用在ImageNet已训练好的VGGNet进行图像识别
- Tensorflow学习笔记：CNN篇（10）——Finetuning，猫狗大战，VGGNet的重新针对训练
- https://www.cs.toronto.edu/~frossard/post/vgg16/
- 另外：这是个基于tensorflow-vgg16和Caffe to TensorFlow的VGG16和VGG19的一个TensorFlow的实现。