def load_network(code_path, babi_train_raw, babi_test_raw, word2vec,
word_vector_size, model_json):
if model_json in loaded_models:
print "!> model %s is already loaded" % model_json
return loaded_models[model_json]
print "!> loading model %s..." % model_json
model_file = open(model_json)
args_dict = json.load(model_file)
assert word_vector_size == args_dict['word_vector_size']
args_dict['babi_train_raw'] = babi_train_raw
args_dict['babi_test_raw'] = babi_test_raw
args_dict['word2vec'] = word2vec
# init class
if args_dict['network'] == 'dmn_batch':
raise Exception("dmn_batch did not implement predict()")
sys.path.insert(0, code_path)
import dmn_batch
dmn = dmn_batch.DMN_batch(**args_dict)
sys.path.insert(0, current_dir)
elif args_dict['network'] == 'dmn_basic':
raise Exception("dmn_batch did not implement predict()")
sys.path.insert(0, code_path)
import dmn_basic
dmn = dmn_basic.DMN_basic(**args_dict)
sys.path.insert(0, current_dir)
elif args_dict['network'] == 'dmn_smooth':
sys.path.insert(0, code_path)
import dmn_smooth
dmn = dmn_smooth.DMN_smooth(**args_dict)
sys.path.insert(0, current_dir)
elif args_dict['network'] == 'dmn_qa':
raise Exception("dmn_batch did not implement predict()")
sys.path.insert(0, code_path)
import dmn_qa_draft
dmn = dmn_qa_draft.DMN_qa(**args_dict)
sys.path.insert(0, current_dir)
else:
raise Exception("No such network known: " + args_dict['network'])
print "!> loading state %s..." % args_dict['load_state']
dmn.load_state(args_dict['load_state'])
loaded_models[model_json] = dmn
return dmn
def get_dmn(network, batch_size, args_dict):
# init class
if network == 'dmn_batch':
import dmn_batch
dmn = dmn_batch.DMN_batch(**args_dict)
else:
if (batch_size != 1):
print "==> no minibatch training, argument batch_size is useless"
batch_size = 1
if network == 'dmn_basic':
import dmn_basic
dmn = dmn_basic.DMN_basic(**args_dict)
elif network == 'dmn_smooth':
import dmn_smooth
dmn = dmn_smooth.DMN_smooth(**args_dict)
elif network == 'dmn_spv':
import dmn_spv
dmn = dmn_spv.DMN(**args_dict)
else:
raise Exception("No such network known: " + network)
return dmn, batch_size
import dmn_batch
dmn = dmn_batch.DMN_batch(**args_dict)
elif args.network == 'dmn_basic':
import dmn_basic
if (args.batch_size != 1):
print "==> no minibatch training, argument batch_size is useless"
args.batch_size = 1
dmn = dmn_basic.DMN_basic(**args_dict)
elif args.network == 'dmn_smooth':
import dmn_smooth
if (args.batch_size != 1):
print "==> no minibatch training, argument batch_size is useless"
args.batch_size = 1
dmn = dmn_smooth.DMN_smooth(**args_dict)
elif args.network == 'dmn_qa':
import dmn_qa_draft
if (args.batch_size != 1):
print "==> no minibatch training, argument batch_size is useless"
args.batch_size = 1
dmn = dmn_qa_draft.DMN_qa(**args_dict)
else:
raise Exception("No such network known: " + args.network)
if args.load_state != "":
dmn.load_state(args.load_state)