我正在尝试创建自己的自定义python层来计算网络准确性(用于阶段:TEST)。
我的问题:它是否仍应具有所有这四个功能:
设置-使用从图层变量获得的参数初始化图层
前进-图层的输入和输出
向后-给定下一层的预测和渐变,计算上一层的渐变
重塑-根据需要重塑斑点
如果是,为什么?我只想在测试阶段和计算准确性时使用它,而不是在学习中使用(前进和后退似乎是为了训练)。
感谢大家!
最佳答案
尽管我不确定如果没有定义所有这四个方法,Caffe可能会输出错误,但是您肯定需要安装和转发:
设置:正是您所说的。例如,在我的“准确性”层中,我通常会为整个测试集和每个样本的softmax概率保存一些度量标准(真假肯定/假肯定/否定,f分数),以防万一我想合并/融合不同的网络/方法后来。这是我打开文件的地方,我将在其中写入这些信息。
转发:在这里,您将自己计算准确度,将批次中每个样品的预测结果与标签进行比较。通常,该层将有两个输入,标签(可能由数据/输入层提供的地面真相)和一个输出每个类的批次中每个样本的预测/得分/概率的层(我通常使用SoftMax层);
重塑和后退:不用担心这些。您无需担心后退传球,也无需重新塑造斑点。
这是一个精度层的示例:
# Remark: This class is designed for a binary problem with classes '0' and '1'
# Saving this file as accuracyLayer.py
import caffe
TRAIN = 0
TEST = 1
class Accuracy_Layer(caffe.Layer):
#Setup method
def setup(self, bottom, top):
#We want two bottom blobs, the labels and the predictions
if len(bottom) != 2:
raise Exception("Wrong number of bottom blobs (prediction and label)")
#Initialize some attributes
self.correctPredictions = 0.0
self.totalImgs = 0
#Forward method
def forward(self, bottom, top):
#The order of these depends on the prototxt definition
predictions = bottom[0].data
labels = bottom[1].data
self.totalImgs += len(labels)
for i in range(len(labels)): #len(labels) is equal to the batch size
pred = predictions[i] #pred is a tuple with the normalized probability
#of a sample i.r.t. two classes
lab = labels[i]
if pred[0] > pred[1]: #this means it was predicted as class 0
if lab == 0.0:
self.correctPredictions += 1.0
else: #else, predicted as class 1
if lab == 1.0:
self.correctPredictions += 1.0
acc = correctPredictions / self.totalImgs
#output data to top blob
top[0].data = acc
def reshape(self, bottom, top):
"""
We don't need to reshape or instantiate anything that is input-size sensitive
"""
pass
def backward(self, bottom, top):
"""
This layer does not back propagate
"""
pass
以及如何在原型中定义它。这是您对Caffe所说的,该层仅在TEST阶段出现:
layer {
name: "metrics"
type: "Python"
top: "Acc"
top: "FPR"
top: "FNR"
bottom: "prediction" #let's suppose we have these two bottom blobs
bottom: "label"
python_param {
module: "accuracyLayer"
layer: "Accuracy_Layer"
}
include {
phase: TEST. #This will ensure it will only be executed in TEST phase
}
}
顺便说一句,I've written a gist,它可能是您正在寻找的精度Python层的更复杂示例。