def main():
start = time.time()
query = sys.argv[1]
glove = utils.load_glove()
quest = utils.init_babi_deploy(
os.path.join(
os.path.join(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
'data'), 'corpus'), 'babi.txt'), query)
dmn = dmn_basic.DMN_basic(babi_train_raw=quest,
babi_test_raw=[],
word2vec=glove,
word_vector_size=50,
dim=40,
mode='deploy',
answer_module='feedforward',
input_mask_mode="sentence",
memory_hops=5,
l2=0,
normalize_attention=False,
answer_vec='index',
debug=False)
dmn.load_state(
'states/dmn_basic.mh5.n40.bs10.babi1.epoch2.test1.20454.state')
prediction = dmn.step_deploy()
prediction = prediction[0][0]
for ind in prediction.argsort()[::-1]:
if ind < dmn.answer_size:
print(dmn.ivocab[ind])
break
print('Time taken:', time.time() - start)
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
args_dict['babi_train_raw'] = babi_train_raw
args_dict['babi_test_raw'] = babi_test_raw
args_dict['word2vec'] = word2vec
# init class
if args.network == 'dmn_batch':
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)
args_dict['test_raw'] = test_raw
args_dict['word2vec'] = word2vec
# init class
if args.network == 'dmn_batch':
import dmn_batch
dmn = dmn_batch.DMN_batch(**args_dict)
# The basic module is implemented for document similarity
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
) # Initialize the dmn basic with all the arguments available. This also initializes theano functions and parameters.
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)
input_mask_mode=args.input_mask_mode,
memory_hops=args.memory_hops,
batch_size=args.batch_size,
l2=args.l2,
normalize_attention=args.normalize_attention)
elif args.network == 'dmn_basic':
import dmn_basic
if (args.batch_size != 1):
raise Exception("no minibatch training, set batch_size=1")
dmn = dmn_basic.DMN_basic(babi_train_raw=babi_train_raw,
babi_test_raw=babi_test_raw,
word2vec=word2vec,
word_vector_size=args.word_vector_size,
dim=args.dim,
mode=args.mode,
answer_module=args.answer_module,
input_mask_mode=args.input_mask_mode,
memory_hops=args.memory_hops,
l2=args.l2,
normalize_attention=args.normalize_attention)
elif args.network == 'dmn_qa':
import dmn_qa
if (args.batch_size != 1):
raise Exception("no minibatch training, set batch_size=1")
dmn = dmn_qa.DMN_qa(babi_train_raw=babi_train_raw,
babi_test_raw=babi_test_raw,
word2vec=word2vec,
word_vector_size=args.word_vector_size,
dim=args.dim,
