# dataset train_data = gluon.data.DataLoader(dataset=IndianDataset(train=True), batch_size=batch_size, shuffle=True, last_batch='rollover') val_data = gluon.data.DataLoader(dataset=IndianDataset(train=False), batch_size=batch_size, shuffle=False) # g(x) : stochastic input augmentation function #def g(x): # return x + nd.random.normal(0,stochastic_ratio,shape=x.shape) # model basemodel_zoo = 'simple2' net = symbols.get_model(basemodel_zoo) #net_t = symbols.get_model(basemodel_zoo) net.initialize(mx.init.Xavier(magnitude=2.24)) #net.initialize(mx.init.MSRAPrelu()) #net_t.initialize(mx.init.MSRAPrelu()) #net.initialize(mx.init.Normal(0.5) ,ctx=ctx) #net.load_parameters(para_filepath) #net_t.load_parameters(para_filepath) net.collect_params().reset_ctx(ctx) #net_t.collect_params().reset_ctx(ctx) # solve loss = gloss.SoftmaxCrossEntropyLoss() metric = mx.metric.Accuracy()
transform = lambda data, label: (data.reshape(784, ).astype(np.float32) / 255, label) train_data = gluon.data.DataLoader(dataset=gluon.data.vision.MNIST( train=True, transform=transform), batch_size=100, shuffle=True, last_batch='discard') val_data = gluon.data.DataLoader(dataset=gluon.data.vision.MNIST( train=False, transform=transform), batch_size=100, shuffle=False) # network modelname = 'semi_pi_simple2' basemodel_zoo = 'simple2' net = symbols.get_model('simple2') net.initialize(mx.init.Xavier(magnitude=2.24)) #net.load_parameters(os.path.join('symbols','para','%s.params'%(modelname))) # g(x) : stochastic input augmentation function def g(x): return x + nd.random.normal(0, stochastic_ratio, shape=x.shape) # loss function l_logistic = gloss.SoftmaxCrossEntropyLoss() l_l2loss = gloss.L2Loss() metric = mx.metric.Accuracy()
batch_size=batch_size, shuffle=True, last_batch='rollover') val_data = gluon.data.DataLoader(dataset=IndianDataset(train=False), batch_size=batch_size, shuffle=False) # g(x) : stochastic input augmentation function def g(x): return x #+ nd.random.normal(0,stochastic_ratio,shape=x.shape) # model basemodel_zoo = 'simple2' net = symbols.get_model(basemodel_zoo, pretrained=True) net_t = symbols.get_model(basemodel_zoo, pretrained=True) #net.initialize(mx.init.Xavier(magnitude=2.24)) #net.initialize(mx.init.MSRAPrelu()) #net_t.initialize(mx.init.MSRAPrelu()) #net.initialize(mx.init.Normal(0.5) ,ctx=ctx) #net.load_parameters(para_filepath) #net_t.load_parameters(para_filepath) net.collect_params().reset_ctx(ctx) net_t.collect_params().reset_ctx(ctx) # solve l_logistic = gloss.SoftmaxCrossEntropyLoss() l_l2loss = gloss.L2Loss() metric = mx.metric.Accuracy()