def random_plan(request):
while True:
conf = get_random_conf()
cnn = CNN(conf)
cnn.train_model(1)
if 0 in cnn.con_mat_val[-1]:
continue
else:
print 'we find normal model'
cnn.train_model(10)
def good_plan(request):
try:
# cnn = CNN({'model_name': 'good_plan_50', 'img_rows': 50, 'img_cols': 50})
cnn = CNN({'model_name': 'good_plan_50'}, True)
cnn.train_model(40)
except Exception as e:
print e
return Response({'msg': e.message}, status=status.HTTP_400_BAD_REQUEST)
print 'finish good plan'
return Response({}, status=status.HTTP_200_OK)
def test_train_model(self):
# cnn = CNN({'model_name': 'split_cases_50_50_2_layer', 'img_rows': 200, 'img_cols': 200})
# cnn = CNN({'model_name': 'model_with_gabor2'}, True)
# cnn = CNN({'model_name': 'split_cases_dropout_50', 'img_rows': 200, 'img_cols': 200})
# cnn = CNN({'model_name': 'test_split_case'}, True)
# cnn = CNN({'model_name': 'test_split_case', 'img_rows': 200, 'img_cols': 200})
# cnn = CNN({'model_name': 'third_model', 'img_rows': 200, 'img_cols': 200})
cnn = CNN({'model_name': 'kernal_5X5'}, True)
# cnn = CNN({'model_name': 'second_model'}, True)
# cnn = CNN({'model_name': 'naor_first_model'}, True)
# cnn._calculate_confusion_matrix()
# self.assertEqual(cnn.tp, 396)
# self.assertEqual(cnn.tn, 970)
# self.assertEqual(cnn.fp, 6)
# self.assertEqual(cnn.fn, 61)
cnn.train_model(n_epoch=1)
def test_train_model(self):
_con_mat = [[25, 25, 25, 25], [30, 20, 30, 20], [50, 0, 0, 50]]
model_name = 'kernal_6X6'
cnn = CNN({
'model_name': model_name,
'img_rows': 75,
'img_cols': 75,
'kernel_size': (8, 8)
})
cnn.con_mat_train = _con_mat
cnn.con_mat_val = _con_mat
cnn._save_only_best()
self.assertTrue(
os.path.exists(os.path.join(cnn.model_path + '.h5(weights)')))
self.assertTrue(os.path.exists(os.path.join(cnn.model_path + '.json')))
cnn.train_model(1)
del cnn
cnn = CNN({'model_name': model_name}, True)
self.assertEqual(_con_mat, cnn.con_mat_train)
self.assertEqual(_con_mat, cnn.con_mat_val)
def full_plan(request):
try:
item = 1
for split_cases in ['True', 'False']:
for dropout in [0.25, 0.5]:
for activation_function in ['softmax', 'sigmoid']:
for img_size in [(75, 75), (50, 50)]:
for nb_filters in [32, 64]:
for kernel_size in [5, 6, 7, 8, 9, 10]:
for pool_size in [2, 4, 6, 8]:
for batch_size in [32, 64, 128]:
for sigma in [180, 90, 30]:
for theta in [45, 90, 135]:
for lammbd in [45, 90, 135]:
for gamma in [
0.3, 0.5, 0.7, 0.9
]:
for psi in [
0.2, 0.5, 0.8
]:
try:
item_path = os.path.join(
ROOT_DIR,
'cnn_models',
'item%s.json'
% item)
if os.path.exists(
item_path
):
item += 1
break
params = {}
params[
'model_name'] = "item%s" % item
item += 1
params[
'split_cases'] = split_cases
params[
'img_rows'] = img_size[
0]
params[
'img_cols'] = img_size[
1]
params[
'batch_size'] = batch_size
params[
'nb_filters'] = nb_filters
params[
'dropout'] = dropout
params[
'activation_function'] = activation_function
params[
'pool_size'] = pool_size
params[
'kernel_size'] = kernel_size
params[
'sigma'] = sigma
params[
'theta'] = theta
params[
'lammbd'] = lammbd
params[
'gamma'] = gamma
params[
'psi'] = psi
cnn = CNN(
params)
cnn.train_model(
150)
except Exception as e:
print e
except Exception as e:
print e
return Response({'msg': e.message}, status=status.HTTP_400_BAD_REQUEST)
print 'finish all plan'
return Response({}, status=status.HTTP_200_OK)
