Tensorflow 之finetune微调模型方法&&不同层上设置不同的学习率...

来自 : 开源中国 发布时间：2021-03-25

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

2019年1月7日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()...github,amp,tensorflow,slim,layer,osc_set2m6wk,,Tensorflow 之finetune微调模型方法&&不同层上设置不同的学习率 - osc_set2m6wk的个人空间,OSCHINA博客在不同层上设置不同的学习率，fine-tuning https://github.com/dgurkaynak/tensorflow-cnn-finetune ConvNets: AlexNet VGGNet ResNet 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_di...Tensorflow 之finetune微调模型方法&&不同层上设置不同的学习率