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(
ConvolutionLayer((batch_size, neure[1], 6, 6),
(neure[2], neure[1], 5, 5), 'relu', 'Gaussian', 0.01))
Test_Single.add(PoolingLayer())
Test_Single.add(
FullyConnectedLayer(neure[2] * 1 * 1, neure[3], 'relu', 'Gaussian',
0.1))
Test_Single.add(DropoutLayer(0.5))
Test_Single.add(SoftmaxLayer(neure[3], 5, 'Gaussian', 0.1))
Test_Single.build_test_fn()
Test_Single.load_params('params/CNN128_ROI.pkl')
test_pred = Test_Single.test_pred
test_belief = Test_Single.test_belief
test_single_stat()
batch_size = 9
x = T.matrix('x')
Test_KNN = Model(batch_size=9, lr=0.01, dataSet=None)
Test_KNN.add(DataLayer(batch_size, (32, 32, 1)))
Test_KNN.add(
ConvolutionLayer((batch_size, 1, 32, 32), (neure[0], 1, 3, 3), 'relu',
'Gaussian', 0.0001))
Test_KNN.add(PoolingLayer())
Test_KNN.add(
ConvolutionLayer((batch_size, neure[0], 15, 15),
(neure[1], neure[0], 4, 4), 'relu', 'Gaussian', 0.01))
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))
Test_Single.add(FullyConnectedLayer(32*32,neure[0],'relu','Gaussian',0.1))
Test_Single.add(DropoutLayer(0.2))
Test_Single.add(FullyConnectedLayer(neure[0],neure[1],'relu','Gaussian',0.1))
Test_Single.add(DropoutLayer(0.2))
Test_Single.add(FullyConnectedLayer(neure[1],neure[2],'relu','Gaussian',0.1))
Test_Single.add(DropoutLayer(0.2))
Test_Single.add(SoftmaxLayer(neure[2],5))
Test_Single.build_test_fn()
Test_Single.load_params('params/DNN2000_ROI.pkl')
test_pred=Test_Single.test_pred
test_belief=Test_Single.test_belief
test_single_stat()
batch_size=9
x=T.matrix('x')
Test_KNN=Model(batch_size=9,lr=0.01,dataSet=None)
Test_KNN.add(DataLayer(batch_size,(32,32,1)))
Test_KNN.add(FullyConnectedLayer(32*32,neure[0],'relu','Gaussian',0.1))
Test_KNN.add(DropoutLayer(0.2))
Test_KNN.add(FullyConnectedLayer(neure[0],neure[1],'relu','Gaussian',0.1))
Test_KNN.add(DropoutLayer(0.2))
Test_KNN.add(FullyConnectedLayer(neure[1],neure[2],'relu','Gaussian',0.1))
Test_KNN.add(DropoutLayer(0.2))
Test_KNN.add(SoftmaxLayer(neure[3],5,'Gaussian',0.1))
for i in xrange(size):
ans+=[test_fn(i)[0]]
transfer(ans,filename)
if __name__ == '__main__':
cifar=Model(batch_size=1,lr=0.01,dataSet=None)
neure=[32,32,64,64]
batch_size=1
x=T.matrix('x')
index=T.lscalar()
cifar.add(DataLayer(batch_size,(32,32,3)))
cifar.add(ConvolutionLayer((batch_size,3,32,32),(neure[0],3,3,3),'relu','Gaussian',0.0001))
cifar.add(PoolingLayer())
cifar.add(ConvolutionLayer((batch_size,neure[0],15,15),(neure[1],neure[0],4,4),'relu','Gaussian',0.01))
cifar.add(PoolingLayer())
cifar.add(ConvolutionLayer((batch_size,neure[1],6,6),(neure[2],neure[1],5,5),'relu','Gaussian',0.01))
cifar.add(PoolingLayer())
cifar.add(FullyConnectedLayer(neure[2]*1*1,neure[3],'relu','Gaussian',0.1))
cifar.add(DropoutLayer(0.5))
cifar.add(SoftmaxLayer(neure[3],5,'Gaussian',0.1))
cifar.build_test_fn()
cifar.load_params('cnn_params.pkl')
test_pred=cifar.test_pred
#### Muti-Thread Sevrer ####
host = "localhost"
port = 2335
addr = (host, port)
server = ThreadingTCPServer(addr, MyStreamRequestHandlerr)
print("now listening")
server.serve_forever()
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))
Test_Single.add(
FullyConnectedLayer(32 * 32, neure[0], 'relu', 'Gaussian', 0.1))
Test_Single.add(DropoutLayer(0.2))
Test_Single.add(SoftmaxLayer(neure[0], 5))
Test_Single.build_test_fn()
Test_Single.load_params('params/1NN2000_ROI.pkl')
test_pred = Test_Single.test_pred
test_belief = Test_Single.test_belief
test_single_stat()
batch_size = 9
x = T.matrix('x')
Test_KNN = Model(batch_size=9, lr=0.01, dataSet=None)
Test_KNN.add(DataLayer(batch_size, (32, 32, 1)))
Test_KNN.add(
FullyConnectedLayer(32 * 32, neure[0], 'relu', 'Gaussian', 0.1))
Test_KNN.add(DropoutLayer(0.2))
Test_KNN.add(SoftmaxLayer(neure[0], 5, 'Gaussian', 0.1))
Test_KNN.build_test_fn()
Test_KNN.load_params('params/1NN2000_ROI.pkl')
test_pred = Test_KNN.test_pred
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(ConvolutionLayer((batch_size,neure[1],6,6),(neure[2],neure[1],5,5),'relu','Gaussian',0.01))
Test_Single.add(PoolingLayer())
Test_Single.add(FullyConnectedLayer(neure[2]*1*1,neure[3],'relu','Gaussian',0.1))
Test_Single.add(DropoutLayer(0.5))
Test_Single.add(SoftmaxLayer(neure[3],5,'Gaussian',0.1))
Test_Single.build_test_fn()
Test_Single.load_params('params/CNN128_ROI.pkl')
test_pred=Test_Single.test_pred
test_belief=Test_Single.test_belief
test_single_stat()
batch_size=9
x=T.matrix('x')
Test_KNN=Model(batch_size=9,lr=0.01,dataSet=None)
Test_KNN.add(DataLayer(batch_size,(32,32,1)))
Test_KNN.add(ConvolutionLayer((batch_size,1,32,32),(neure[0],1,3,3),'relu','Gaussian',0.0001))
Test_KNN.add(PoolingLayer())
Test_KNN.add(ConvolutionLayer((batch_size,neure[0],15,15),(neure[1],neure[0],4,4),'relu','Gaussian',0.01))
Test_KNN.add(PoolingLayer())
Test_KNN.add(ConvolutionLayer((batch_size,neure[1],6,6),(neure[2],neure[1],5,5),'relu','Gaussian',0.01))
Test_KNN.add(PoolingLayer())
Test_KNN.add(FullyConnectedLayer(neure[2]*1*1,neure[3],'relu','Gaussian',0.1))
