def train(nn_name='12-net', k=12):
"""
Fucntion for traning 12-net with testing on part of data
using cross validation
"""
suff = str(k)
if nn_name.find('calib') > 0:
X_data_name = 'train_data_icalib_' + suff + '.npy'
y_data_name = 'labels_icalib_' + suff + '.npy'
else:
X_data_name = 'train_data_' + suff + '.npy'
y_data_name = 'labels_' + suff + '.npy'
rates12 = sp.hstack((0.05 * sp.ones(25, dtype=sp.float32),
0.005 * sp.ones(15, dtype=sp.float32),
0.0005 * sp.ones(10, dtype=sp.float32)))
rates24 = sp.hstack((0.01 * sp.ones(25, dtype=sp.float32),
0.0001 * sp.ones(15, dtype=sp.float32)))
rates48 = sp.hstack([
0.05 * sp.ones(15, dtype=sp.float32),
0.005 * sp.ones(10, dtype=sp.float32)
])
if nn_name == '24-net':
nn = Cnnl(
nn_name=nn_name,
l_rates=rates24,
subnet=Cnnl(nn_name='12-net',
l_rates=rates12).load_model('12-net_lasagne_.pickle'))
elif nn_name == '48-net':
nn = Cnnl(nn_name=nn_name,
l_rates=rates48,
subnet=Cnnl(
nn_name='24-net',
l_rates=rates24,
subnet=Cnnl(
nn_name='12-net',
l_rates=rates12).load_model('12-net_lasagne_.pickle')
).load_model('24-net_lasagne_.pickle'))
else:
nn = Cnnl(nn_name=nn_name, l_rates=rates12)
if not os.path.exists(nn_name + '_lasagne_.pickle'):
if nn_name.find('calib') > 0:
ds.get_train_wider_calib_data(k=k)
else:
ds.get_train_data(k=k)
X, y = sp.load(X_data_name), sp.load(y_data_name)
X_train, y_train = X, y
if not os.path.exists(nn_name + '_lasagne_.pickle'):
if nn_name == '24-net':
X_sub_train12 = sp.load('train_data_12.npy')
nn.fit(X=X_train, y=y_train, X12=X_sub_train12)
elif nn_name == '48-net':
X_sub_train12 = sp.load('train_data_12.npy')
X_sub_train24 = sp.load('train_data_24.npy')
nn.fit(X=X_train, y=y_train, X12=X_sub_train12, X24=X_sub_train24)
else:
nn.fit(X=X_train, y=y_train)
nn.save_model(nn_name + '_lasagne_.pickle')
def train(nn_name = '12-net',k = 12):
"""
Fucntion for traning 12-net with testing on part of data
using cross validation
"""
suff = str(k)
if nn_name.find('calib') > 0:
X_data_name = 'train_data_icalib_'+ suff + '.npy'
y_data_name = 'labels_icalib_'+ suff + '.npy'
else:
X_data_name = 'train_data_'+ suff + '.npy'
y_data_name = 'labels_'+ suff + '.npy'
rates12 = sp.hstack((0.05 * sp.ones(25,dtype=sp.float32),0.005*sp.ones(15,dtype=sp.float32),0.0005*sp.ones(10,dtype=sp.float32)))
rates24 = sp.hstack((0.01 * sp.ones(25,dtype=sp.float32),0.0001*sp.ones(15,dtype=sp.float32)))
rates48 = sp.hstack ([0.05 * sp.ones(15,dtype=sp.float32),0.005*sp.ones(10,dtype=sp.float32) ])
if nn_name == '24-net':
nn = Cnnl(nn_name = nn_name,l_rates=rates24,subnet=Cnnl(nn_name = '12-net',l_rates=rates12).load_model(
'12-net_lasagne_.pickle'))
elif nn_name == '48-net':
nn = Cnnl(nn_name = nn_name,l_rates=rates48,subnet=Cnnl(nn_name = '24-net',l_rates=rates24,subnet=Cnnl(nn_name = '12-net',l_rates=rates12).load_model(
'12-net_lasagne_.pickle')).load_model('24-net_lasagne_.pickle'))
else:
nn = Cnnl(nn_name = nn_name,l_rates=rates12)
if not os.path.exists(nn_name + '_lasagne_.pickle'):
if nn_name.find('calib') > 0:
ds.get_train_wider_calib_data(k=k)
else:
ds.get_train_data(k=k)
X,y = sp.load(X_data_name),sp.load(y_data_name)
X_train,y_train = X,y
if not os.path.exists(nn_name + '_lasagne_.pickle'):
if nn_name == '24-net':
X_sub_train12 = sp.load('train_data_12.npy')
nn.fit(X = X_train,y = y_train,X12 = X_sub_train12)
elif nn_name == '48-net':
X_sub_train12 = sp.load('train_data_12.npy')
X_sub_train24 = sp.load('train_data_24.npy')
nn.fit(X = X_train,y = y_train,X12 = X_sub_train12,X24 = X_sub_train24)
else:
nn.fit(X = X_train,y = y_train)
nn.save_model(nn_name + '_lasagne_.pickle')
