caffe 速览笔记

Caffe Tutorial

Caffe 模型的最基本要素： Blob, Layers, Nets

Blob 传送数据的基本单元

Blob与tensorflow中的tensor类似，都是用来在节点之间传递数据所用，接口也类似，常规的dimension是 数据量N x 通道K x 高度H x 宽度W

Blob内部存有data和diff两大块。前者是传递的数据，后者是计算的梯度。

由于blob同时存在CPU和GPU，所以有两种方式来访问他们：

const (don`t change the values)

mutable (changes the values)

const Dtype* cpu_data() const;
Dtype* mutable_cpu_data();

要使用合适的方式来访问blob变量，主要是因为blob具有cpu和gpu同步功能，会进行cpu和gpu的通信。所以如果不想更改值的话，最好使用const来调用，以减小通信开销。并且blob的指针不要存在自己的对象中，而使用函数来获取指针，因为syncedmem需要函数的调用来确定什么时候复制数据。

cpu 和 gpu 之间的转换？

Layer 计算与连接的单元

Layer是模型的本质与计算的基本单元。包括卷积、池化、内积、sigmoid等。

每个Layer包括了三个重要的计算：设置、前向传输、后向传输(setup, forward, backward)

Setup: 初始化layer与连接

Forward: 根据来自底部的数据进行计算，传到顶部

Backward：根据顶部传来的梯度进行梯度计算，然后传到底部。具有参数的layer会将梯度进行存储应用

Net 定义和操作

Net是一个layer连接的组合，组成了一个有向无环图。



这样进行定义：

name: "LogReg"
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
data_param {
source: "input_leveldb"
batch_size: 64
}
}
layer {
name: "ip"
type: "InnerProduct"
bottom: "data"
top: "ip"
inner_product_param {
num_output: 2
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip"
bottom: "label"
top: "loss"
}

Forward 与 Backward

Forward 与 Backward 的传输是Net的重要计算。



Forward计算:



Backward计算：

Loss

普通：

layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "pred"
bottom: "label"
top: "loss"
}

loss weight:

layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "pred"
bottom: "label"
top: "loss"
loss_weight: 1
}

最后可根据所有loss进行加权相加计算：

loss := 0
for layer in layers:
for top, loss_weight in layer.tops, layer.loss_weights:
loss += loss_weight * sum(top)

Slover

各种用于计算梯度与loss的方法。

//todo

Layers

数据层

可利用设置TransformationParameter来进行输入的预处理(mean subtraction, scaling, random cropping, and mirroring)

当TransformationParameter失效时，可利用 bias, scale, and crop层进行处理。

有以下几种data layers:

Image Data - read raw images.

Database - read data from LEVELDB or LMDB.

HDF5 Input - read HDF5 data, allows data of arbitrary dimensions.

HDF5 Output - write data as HDF5.

Input - typically used for networks that are being deployed.

Window Data - read window data file.

Memory Data - read data directly from memory.

Dummy Data - for static data and debugging.

Vison Layers

以images为输入，以images为输出。

有以下几种Vision Layers：

卷积层

池化层

Spatial Pyramid Pooling (SPP)

Crop 裁剪操作

Deconvolution Layer 逆卷积层

Recurrent Layers

Recurrent

RNN

Long-short Term Memory

Common Layers

Inner Product

Dropout

Embed

Normalization Layers

Local Response Normalization

Mean Variance Normalization

Batch Normalization

Activation / Neuron Layers

ReLU / Rectified-Linear and Leaky-ReLU - ReLU and Leaky-ReLU rectification.

PReLU - parametric ReLU.

ELU - exponential linear rectification.

Sigmoid

TanH

Absolute Value

Power - f(x) = (shift + scale * x) ^ power.

Exp - f(x) = base ^ (shift + scale * x).

Log - f(x) = log(x).

BNLL - f(x) = log(1 + exp(x)).

Threshold - performs step function at user defined threshold.

Bias - adds a bias to a blob that can either be learned or fixed.

Scale - scales a blob by an amount that can either be learned or fixed.

Utility Layers

Flatten

Reshape

Batch Reindex

Split

Concat

Slicing

Eltwise - element-wise operations such as product or sum between two blobs.

Filter / Mask - mask or select output using last blob.

Parameter - enable parameters to be shared between layers.

Reduction - reduce input blob to scalar blob using operations such as sum or mean.

Silence - prevent top-level blobs from being printed during training.

ArgMax

Softmax

Python - allows custom Python layers.

Loss Layers

Multinomial Logistic Loss

Infogain Loss - a generalization of MultinomialLogisticLossLayer.

Softmax with Loss - computes the multinomial logistic loss of the sof
4000
tmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.

Sum-of-Squares / Euclidean - computes the sum of squares of differences of its two inputs

Hinge / Margin - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).

Sigmoid Cross-Entropy Loss - computes the cross-entropy (logistic) loss, often used for predicting targets interpreted as probabilities.

Accuracy / Top-k layer - scores the output as an accuracy with respect to target – it is not actually a loss and has no backward step.

Contrastive Loss

Data inputs and outputs

in:

layer {
name: "mnist"
# Data layer loads leveldb or lmdb storage DBs for high-throughput.
type: "Data"
# the 1st top is the data itself: the name is only convention
top: "data"
# the 2nd top is the ground truth: the name is only convention
top: "label"
# the Data layer configuration
data_param {
# path to the DB
source: "examples/mnist/mnist_train_lmdb"
# type of DB: LEVELDB or LMDB (LMDB supports concurrent reads)
backend: LMDB
# batch processing improves efficiency.
batch_size: 64
}
# common data transformations
transform_param {
# feature scaling coefficient: this maps the [0, 255] MNIST data to [0, 1]
scale: 0.00390625
}
}

out:

layer {
name: "data"
type: "Data"
[...]
transform_param {
scale: 0.1
mean_file_size: mean.binaryproto
# for images in particular horizontal mirroring and random cropping
# can be done as simple data augmentations.
mirror: 1  # 1 = on, 0 = off
# crop a `crop_size` x `crop_size` patch:
# - at random during training
# - from the center during testing
crop_size: 227
}
}