From 18126824ee3d8a5afc4039cae80aa7f6216e40f1 Mon Sep 17 00:00:00 2001
From: c <lianghongkey@gmail.com>
Date: Fri, 27 Dec 2019 18:04:51 +0800
Subject: [PATCH] Add more train to test entropy resualt.

---
 FilterEvaluator/Evaluator.py        |  2 +-
 FilterEvaluator/EvaluatorUnsuper.py |  9 +--------
 FilterEvaluator/TrainNetwork.py     | 15 ++++++++++-----
 train                               |  0
 4 files changed, 12 insertions(+), 14 deletions(-)
 delete mode 100644 train

diff --git a/FilterEvaluator/Evaluator.py b/FilterEvaluator/Evaluator.py
index 937b9bd..825be44 100644
--- a/FilterEvaluator/Evaluator.py
+++ b/FilterEvaluator/Evaluator.py
@@ -74,7 +74,7 @@ traindata, testdata = Loader.MNIST(batchsize, shuffle=True, resize=7)
 
 
 
-bestweight = EvaluatorUnsuper.UnsuperLearnSearchBestWeight(model,layer,traindata,32,20,250000)
+bestweight = EvaluatorUnsuper.UnsuperLearnSearchBestWeight(model,layer,traindata,8,20,250000)
 np.save(CurrentPath+"bestweightEntropySearch.npy", bestweight)
 utils.NumpyToImage(bestweight, CurrentPath+"image",title="EntropySearchWerightBest")
 EvaluatorUnsuper.SetModelConvWeight(model,layer,bestweight)
diff --git a/FilterEvaluator/EvaluatorUnsuper.py b/FilterEvaluator/EvaluatorUnsuper.py
index 608a878..aa93f33 100644
--- a/FilterEvaluator/EvaluatorUnsuper.py
+++ b/FilterEvaluator/EvaluatorUnsuper.py
@@ -137,7 +137,7 @@ def UnsuperLearnTrainWeight(model, layer, dataloader, NumTrain=500, TrainChannel
 
 # search best weight from weight input
 def UnsuperLearnFindBestWeight(netmodel, layer, weight, dataloader, databatchs=128, interation=10000):
-    interationbar = tqdm(total=interation)    
+    interationbar = tqdm(total=interation)
     weight = weight.astype("float32")
     netmodel.eval()
     tl = netmodel.features[layer]
@@ -179,10 +179,6 @@ def UnsuperLearnFindBestWeight(netmodel, layer, weight, dataloader, databatchs=1
     interationbar.close()
     return bestweight,indexs[sortindex]
 
-
-
-
-
 # search best weight from random data
 def UnsuperLearnSearchBestWeight(netmodel, layer, dataloader, databatchs=128, stepsize=1000, interation=1000):
     interationbar = tqdm(total=interation)    
@@ -295,9 +291,6 @@ def UnsuperLearnSearchBestWeight(netmodel, layer, dataloader, databatchs=128, st
     interationbar.close()
     return bestweight
 
-
-
-
 def SetModelConvWeight(model, layer, weight):
     w = utils.SetDevice(torch.from_numpy(weight))
     model.features[layer].weight.data = w
diff --git a/FilterEvaluator/TrainNetwork.py b/FilterEvaluator/TrainNetwork.py
index f8b42f9..d4b51f9 100644
--- a/FilterEvaluator/TrainNetwork.py
+++ b/FilterEvaluator/TrainNetwork.py
@@ -40,7 +40,7 @@ batchsize = 128
 
 
 # traindata, testdata = Loader.MNIST(batchsize, num_workers=4, trainsize=5000)
-traindata, testdata = Loader.MNIST(batchsize, resize=7, trainsize=200)
+traindata, testdata = Loader.MNIST(batchsize, resize=7, trainsize=50000)
 # 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="VerticalOneLine")
@@ -54,7 +54,8 @@ window = WebVisual.LineWin()
 lineNoPre = WebVisual.Line(window, "NoPre")
 lineNoPreBN = WebVisual.Line(window, "NoPreBN")
 linePretrainSearch = WebVisual.Line(window, "PretrainSearch")
-linePretrainTrain = WebVisual.Line(window, "PretrainTrain")
+linePreSubGrad = WebVisual.Line(window, "linePreSubGrad")
+linePreAllGrad = WebVisual.Line(window, "linePreAllGrad")
 
 
 
@@ -87,8 +88,6 @@ linePretrainTrain = WebVisual.Line(window, "PretrainTrain")
 
 
 model = utils.SetDevice(Model.Net333())
-# bestweight = np.ones((model.features[0].weight.data.shape),dtype="float32")
-# EvaluatorUnsuper.SetModelConvWeight(model,0,bestweight)
 optimizer = optim.SGD(model.parameters(), lr=0.1)
 Train.TrainEpochs(model,traindata,optimizer,testdata,3000,10,lineNoPre)
 
@@ -101,7 +100,13 @@ Train.TrainEpochs(model,traindata,optimizer,testdata,3000,10,lineNoPre)
 model = utils.SetDevice(Model.Net3Grad33())
 model = utils.LoadModel(model, CurrentPath+"/checkpointEntropySearch.pkl")
 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,linePreAllGrad)
+
+
+model = utils.SetDevice(Model.Net3Grad33())
+model = utils.LoadModel(model, CurrentPath+"/checkpointEntropySearch.pkl")
+optimizer = optim.SGD(model.parameters(), lr=0.1)
+Train.TrainEpochs(model,traindata,optimizer,testdata,3000,10,linePreSubGrad)
 
 
 
diff --git a/train b/train
deleted file mode 100644
index e69de29..0000000