If you set the number of target layer to finetune.num_active_layers in config.yml as below, only layers whose number is not greater than the number of the specified layer will be train.
finetune:
models:
- imagenet1k-nin
optimizers:
- sgd
num_active_layers: 6
The default for finetune.num_active_layers is 0, in which case all layers are trained.
If you set 1 to finetune.num_active_layers, only the last fully-connected layers are trained.
You can check the layer numbers of various pretrained models with num_layers command.
$ docker-compose run finetuner num_layers <pretrained model name>
An example of checking the layer number of imagenet1k-caffenet is as follows.
$ docker-compose run finetuner num_layers imagenet1k-caffenet
(...snip...)
Number of the layer of imagenet1k-caffenet
27: data
26: conv1_weight
25: conv1_bias
24: conv1_output
23: relu1_output
22: pool1_output
21: norm1_output
20: conv2_weight
19: conv2_bias
18: conv2_output
17: relu2_output
16: pool2_output
15: norm2_output
14: conv3_weight
13: conv3_bias
12: conv3_output
11: relu3_output
10: conv4_weight
9: conv4_bias
8: conv4_output
7: relu4_output
6: conv5_weight
5: conv5_bias
4: conv5_output
3: relu5_output
2: pool5_output
1: flatten_0_output
If you set the number of a layer displayed above to num_active_layers in config.yml,
only layers whose number is not greater than the number of the specified layer will be train.