Caffe:Tour:LayerCatalogue

To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).

Vision Layers

Header: include/caffe/vision_layers.hpp

비전층은 일반적으로 획득한 이미지를 입력으로(INPUT), 출력으로(OUTPUT) 다른 이미지를 생성한다. ¹

Convolution
Pooling
Local Response Normalization (LRN)
im2col

Loss Layers

Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.

Softmax
Sum-of-Squares / Euclidean
Hinge / Margin
Sigmoid Cross-Entropy
Infogain
Accuracy and Top-k

Activation / Neuron Layers

In general, activation / Neuron layers are element-wise operators, taking one bottom blob and producing one top blob of the same size.

ReLU / Rectified-Linear and Leaky-ReLU
Sigmoid
TanH / Hyperbolic Tangent
Absolute Value
Power
BNLL

Data Layers

Data enters Caffe through data layers: they lie at the bottom of nets. Data can come from efficient databases (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats.

Database
In-Memory
HDF5 Input
HDF5 Output
Images
Windows
Dummy

Common Layers

Inner Product

num_output: the number of filters.
(아마도) 마지막 레이어에서 이 수치를 사용하여 라벨(Label or Filter)을 분류한다. train_val.prototxt와 deploy.prototxt의 마지막 레이어에 존재하는 num_output값에 주목해야 한다. ²

layer {
  name: "fc8"
  type: "InnerProduct"
  # ...
  inner_product_param {
    num_output: 1000
    # ...
  }
  # ...
}

Splitting

Flattening

Reshape

Concatenation

Slicing

Elementwise Operations

Argmax

Softmax

Mean-Variance Normalization

Favorite site

References

원문: Vision layers usually take images as input and produce other images as output. ↩
what is batch_size mean? #585 ↩