Exemplos de Model em Python

Exemplo n.º 1

Arquivo: cifar_cnn.py Projeto: JHnlp/Dragon-Theano

from layer.core import *
from algorithm.SGD import Mini_Batch
from data.process import loadData
from layer.model import Model
if __name__ == '__main__':
    dataSet=loadData()
    cifar=Model(batch_size=100,lr=0.0001,dataSet=dataSet,weight_decay=0.004)
    neure=[32,32,64,64]
    batch_size=100
    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_train_fn()
    cifar.build_vaild_fn()
    algorithm=Mini_Batch(model=cifar,n_epochs=100,load_param='cnn_params.pkl',save_param='cnn_params.pkl')
    algorithm.run()
    

Exemplo n.º 2

Arquivo: cifar_mlp.py Projeto: JHnlp/Dragon-Theano

from layer.core import *
from algorithm.SGD import Mini_Batch
from data.process import loadData, loadScaleData
from layer.model import Model
if __name__ == '__main__':
    dataSet=loadScaleData('data.pkl')
    cifar=Model(batch_size=100,lr=0.01,dataSet=dataSet,weight_decay=0.0)
    neure=[1000,1000,1000]
    batch_size=100
    cifar.add(DataLayer(batch_size,32*32*3))
    cifar.add(FullyConnectedLayer(32*32*3,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],10))
    cifar.pretrain()
    cifar.build_train_fn()
    cifar.build_vaild_fn()
    algorithm=Mini_Batch(model=cifar,n_epochs=100,load_param='mlp_params.pkl',save_param='mlp_params.pkl')
    algorithm.run()
    

Exemplo n.º 3

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()

Exemplo n.º 4

Arquivo: test_cnn.py Projeto: tim-lee-cn/Facial-Expression

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(

Exemplo n.º 5

Arquivo: test_dnn.py Projeto: lijian8/Facial-Expression

    print "facial expression %d: test result %d/%d" %(y,ans,examples)
    print "                     error rate %f%%" %(float(examples-ans)/examples*100)
    global test_hit,test_count;
    test_hit+=ans
    test_count+=examples
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))
    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')

Exemplo n.º 6

Arquivo: test_1nn.py Projeto: tim-lee-cn/Facial-Expression

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))
    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()

Exemplo n.º 7

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")
    # load ROI dataset
    #dataSet=loadTrainData("dataset/ROI/data.pkl","dataset/ROI/mean.pkl");
    # load normal dataset
    #dataSet=loadTrainData("dataset/NORMAL/data.pkl","dataset/NORMAL/mean.pkl");
    cifar = Model(batch_size=100, lr=0.0001, dataSet=dataSet, weight_decay=0)
    neure = [64, 64, 128, 300]
    #neure=[32,32,64,64]
    #neure=[48,48,96,200]
    batch_size = 100
    cifar.add(DataLayer(batch_size, (32, 32, 1)))
    cifar.add(
        ConvolutionLayer((batch_size, 1, 32, 32), (neure[0], 1, 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',

Exemplo n.º 8

Arquivo: test_cnn.py Projeto: lijian8/Facial-Expression

    print "facial expression %d: test result %d/%d" %(y,ans,examples)
    print "                     error rate %f%%" %(float(examples-ans)/examples*100)
    global test_hit,test_count;
    test_hit+=ans
    test_count+=examples
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(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))

Exemplo n.º 9

Arquivo: cifar_sdae.py Projeto: JHnlp/Dragon-Theano

from layer.core import *
from algorithm.SGD import Mini_Batch
from data.process import loadData, loadScaleData
from layer.model import Model
if __name__ == '__main__':
    dataSet=loadScaleData('data.pkl')
    cifar=Model(batch_size=100,lr=0.005,dataSet=dataSet,weight_decay=0.0)
    neure=[1000,1000,1000]
    batch_size=100
    cifar.add(DataLayer(batch_size,32*32*3))
    cifar.add(AutoEncodeLayer(32*32*3,neure[0],'relu','softplus',cost='squre',weight_init='Gaussian',gauss_std=0.1,level=0.3))
    cifar.add(DropoutLayer(0.2))
    cifar.add(SoftmaxLayer(neure[0],10))
    cifar.pretrain(batch_size=20,n_epoches=15)
    cifar.build_train_fn()
    cifar.build_vaild_fn()
    algorithm=Mini_Batch(model=cifar,n_epochs=100,load_param='',save_param='mlp_params.pkl')
    algorithm.run()