from gluoncv import model_zoo as mzoo
from mxnet import autograd, gluon, init, nd
from mxnet.gluon import nn, loss as gloss
from indian_dataset import IndianDataset
num_epochs = 5
batch_size = 100
out_put_num = 16
dropout_rate = 0.8
ctx = mx.gpu()
#modelname = 'indian_try'
modelname = 'indian_conv_msra'
para_filepath = os.path.join(this_dir, '..', 'symbols', 'para',
'%s.params' % (modelname))
# 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)
# model
net = nn.Sequential()
#net.add(
# nn.Dense(500,activation='relu'),
# nn.Dense(256,activation='relu'),
# nn.Dropout(dropout_rate),
# nn.Dense(out_put_num,activation='sigmoid')
# )
from mxnet import autograd, gluon, init, nd
from mxnet.gluon import nn, loss as gloss
from indian_dataset import IndianDataset
num_epochs = 5
batch_size = 100
out_put_num = 16
dropout_rate=0.8
stochastic_ratio = 0.01
val_acc_bk = 0
ctx = mx.gpu()
#modelname = 'indian_try'
modelname = 'indian_simple_ema'
para_filepath = os.path.join(this_dir,'..','symbols','para','%s.params'%(modelname))
# 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_t.load_parameters(os.path.join(this_dir,'..','symbols','para','%s.params'%("indian_simple20.870362")) )