colin
/
witnn
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix as 4 kernels in first layer.

This commit is contained in:
c 2020-01-02 15:51:24 +08:00
parent 346159068c
commit d3046d1131
5 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
FilterEvaluator/EvaluatorUnsuper.py
View File

@ -232,7 +232,7 @@ def UnsuperLearnSearchBestWeight(netmodel, layer, dataloader, databatchs=128, st
if hasdata > 0: if hasdata > 0:
entropys = [] entropys = []
for i in range(len(indexs)): for i in range(len(indexs)):
histced = bitted[:,i].histc(256,0,255).type(torch.float32) histced = bitted[:,i].histc(15,0,15).type(torch.float32)
histced = histced[histced>0] histced = histced[histced>0]
histced = histced/histced.sum() histced = histced/histced.sum()
entropy = (histced.log2()*histced).sum() entropy = (histced.log2()*histced).sum()

4
FilterEvaluator/TrainNetwork.py
View File

@ -40,7 +40,7 @@ batchsize = 128
# traindata, testdata = Loader.MNIST(batchsize, num_workers=4, trainsize=5000) # traindata, testdata = Loader.MNIST(batchsize, num_workers=4, trainsize=5000)
traindata, testdata = Loader.MNIST(batchsize, resize=7, trainsize=50000) traindata, testdata = Loader.MNIST(batchsize, resize=7, trainsize=500)
# traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="Vertical") # traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="Vertical")
# traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="Horizontal") # traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="Horizontal")
# traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="VerticalOneLine") # traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="VerticalOneLine")
@ -97,7 +97,7 @@ Train.TrainEpochs(model,traindata,optimizer,testdata,3000,10,lineNoPre)
# optimizer = optim.SGD(model.parameters(), lr=0.1) # optimizer = optim.SGD(model.parameters(), lr=0.1)
# Train.TrainEpochs(model,traindata,optimizer,testdata,3000,10,linePretrainSearch) # Train.TrainEpochs(model,traindata,optimizer,testdata,3000,10,linePretrainSearch)
model = utils.SetDevice(Model.Net3Grad33()) model = utils.SetDevice(Model.Net333())
model = utils.LoadModel(model, CurrentPath+"/checkpointEntropySearch.pkl") model = utils.LoadModel(model, CurrentPath+"/checkpointEntropySearch.pkl")
optimizer = optim.SGD(model.parameters(), lr=0.1) optimizer = optim.SGD(model.parameters(), lr=0.1)
Train.TrainEpochs(model,traindata,optimizer,testdata,3000,10,linePreAllGrad) Train.TrainEpochs(model,traindata,optimizer,testdata,3000,10,linePreAllGrad)

BIN
FilterEvaluator/bestweightEntropySearch.npy
View File

Binary file not shown.

BIN
FilterEvaluator/bestweightEntropySearch_entropy=-3.975601.npy Normal file
View File

Binary file not shown.

BIN
FilterEvaluator/checkpointEntropySearch.pkl
View File

Binary file not shown.