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()
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()
