def test_knn_stat():
print "\nNow tesing with KNN-ROI.....\n"
global test_hit, test_count
for idx in xrange(5):
test_knn(("dataset/ROI_TEST/%d.bin") % idx, "dataset/ROI/mean.pkl",
idx)
print "total error rate: %f%%\n" % (float(test_count - test_hit) /
test_count * 100)
# clear
test_hit = test_count = 0
if __name__ == '__main__':
Test_Single = Model(batch_size=1, lr=0.01, dataSet=None)
meta_num = 100
neure = [meta_num, meta_num, meta_num, meta_num]
batch_size = 1
x = T.matrix('x')
index = T.lscalar()
Test_Single.add(DataLayer(batch_size, (32, 32, 1)))
Test_Single.add(
ConvolutionLayer((batch_size, 1, 32, 32), (neure[0], 1, 3, 3), 'relu',
'Gaussian', 0.0001))
Test_Single.add(PoolingLayer())
Test_Single.add(
ConvolutionLayer((batch_size, neure[0], 15, 15),
(neure[1], neure[0], 4, 4), 'relu', 'Gaussian', 0.01))
Test_Single.add(PoolingLayer())
Test_Single.add(
from layer.core import *
from algorithm.SGD import Mini_Batch
from data.process import loadTrainData
from layer.model import Model
if __name__ == '__main__':
# load ROI+ROTATION dataset
#dataSet=loadTrainData("dataset/ROT/data.pkl","dataset/ROT/mean.pkl",scale=128.0);
# load ROI dataset
dataSet=loadTrainData("dataset/ROI/data.pkl","dataset/ROI/mean.pkl",scale=128.0);
# load normal dataset
#dataSet=loadTrainData("dataset/NORMAL/data.pkl","dataset/NORMAL/mean.pkl",scale=128.0);
cifar=Model(batch_size=100,lr=0.001,dataSet=dataSet,weight_decay=0.0)
#neure=[2000]
neure=[1000,1000,1000]
#neure=[2000,2000,2000];
batch_size=100
cifar.add(DataLayer(batch_size,32*32))
cifar.add(FullyConnectedLayer(32*32,neure[0],'relu','Gaussian',0.1))
cifar.add(DropoutLayer(0.2))
cifar.add(FullyConnectedLayer(neure[0],neure[1],'relu','Gaussian',0.1))
cifar.add(DropoutLayer(0.2))
cifar.add(FullyConnectedLayer(neure[1],neure[2],'relu','Gaussian',0.1))
cifar.add(DropoutLayer(0.2))
cifar.add(SoftmaxLayer(neure[2],5))
cifar.build_train_fn()
cifar.build_vaild_fn()
algorithm=Mini_Batch(model=cifar,n_epochs=200,load_param='mlp_params.pkl',save_param='mlp_params.pkl')
algorithm.run()
