def evaluation():
_, test_data, _, _ = preprocessor.load_data()
models = ['random', 'frequent']
for model in models:
_, test_data, _, _ = preprocessor.load_data()
perform, fname = eval_model(test_data, model)
print('MODEL:', model, ', accuracy:',perform)
print('Result is save at', fname)
print('')
def test():
import targetid
import etri
_, test_data, _, _ = preprocessor.load_data()
test = test_data
testing = [ test[1] ]
fid_result = frame_identifier(testing, 'random')
print(fid_result)
_, test_data, _, _ = preprocessor.load_data()
test = test_data
testing = [ test[1] ]
fid_result = frame_identifier(testing, 'frequent')
print(fid_result)
self.scores = {}
self.score(val_data, user_ls)
vbf_dict = self.scores
results = {u:evaluate_threshold(self.thresh[u], vbf_dict[u])
for u in (user_ls if (user_ls != None)
else self.thresh.keys())}
return results
if __name__=='__main__':
from CV import CV
from preprocessor import split_samples, load_data, filter_users_val
P.np.seterr(all='ignore')
all_data, pkd = filter_users_val(split_samples(load_data()))
for u in all_data.keys():
if all_data[u] == []:
del all_data[u]
del pkd[u]
gbfa = CV(lambda: GammaBFAuth(all_data),
all_data,
pkd)
with open('./bf_result.csv', 'rw+') as res_file:
result_writer = csv.writer(res_file)
result_writer.writerow(['user',
'CV_IPR', 'CV_FRR', 'CV_GT', 'CV_IT'])
print('No GPU found. Please use a GPU to train your neural network.')
# Number of words in a sequence.
# This parameter is used both for training and generating.
sequence_length = args.sequence_len
try:
print("Loading the model...")
_, vocab_to_int, int_to_vocab, token_dict = preprocessor.load_preprocess()
trained_rnn = preprocessor.load_model('./save/trained_rnn')
except:
print("Unable to load a checkpoint. Input data needs to be preprocessed.")
data_dir = './data/Seinfeld_Scripts.txt'
text = preprocessor.load_data(data_dir)
int_text, vocab_to_int, int_to_vocab, token_dict = preprocessor.preprocess_and_save_data(
data_dir, token_lookup, create_lookup_tables)
batch_size = args.batch_size # 32
train_loader = batch_data(int_text, sequence_length, batch_size)
print("Training...")
#num_epochs = 20
num_epochs = args.n_epochs
learning_rate = args.learning_rate #0.0001
vocab_size = len(vocab_to_int)
output_size = vocab_size
if __name__=='__main__':
"""
test_data = {'a' : [coll.defaultdict(list, {'aa' : range(10,100,10),
'ab' : range(1,10)}),
coll.defaultdict(list, {'ac' : range(1,10,2),
'aa' : [2,2,2]}),
coll.defaultdict(list, {'ad' : range(10, 20, 3)})
],
'b' : [coll.defaultdict(list, {'ba' : range(1,20,3),
'bb' : range(1,15)}),
coll.defaultdict(list, {'bc' : range(2,20,4)}),
coll.defaultdict(list, {'bd' : range(50,300,150)})
],
}
"""
test_data = pp.split_samples(pp.load_data())
for u in test_data.keys():
if u not in {'9999999','SERLHOU'}:
del test_data[u]
print test_data.keys()
test_cv = CV(DensityAuth, test_data)
'''
for i in test_cv.partition_data('shit', test_data['a'], 1):
f**k.pprint(i)
'''
for i in test_cv.validate():
pass
print "DONESKI"
from keras.models import load_model
from preprocessor import load_data
import numpy as np
_, VOC = load_data(limit=100)
r_VOC = dict(zip(VOC.values(), VOC.keys()))
model = load_model('model_title_based.h5')
print(VOC)
title = '初晴落景 '
title_int = []
for t in title:
i = VOC.get(t)
if i is None:
raise Exception('{} is not in VOC'.format(t))
title_int.append(i)
title_int = np.array([title_int])
print(title_int)
content = '^'
c_index = 1
while content[-1] != '$' and len(content) < 74:
c_content = content
for i in range(74 - c_index):
c_content += ' '
p = [VOC[i] for i in c_content]
x = np.array([p])
print('')
print('interrupted by user')
pass
def run():
model = build_graph()
train(model, x_train, y_train)
model.save(MODEL_FILE_NAME)
if __name__ == '__main__':
EPOCHS = 80
BATCH_SIZE = 8
NETWORK_SIZE = (2, 256)
DATA_SIZE = 10000
CELL_TYPE = 'gru'
i = 3
MODEL_FILE_NAME = './models/model'+str(i)+'.h5'
CHECKPOINT_WEIGHTS_DIR = './weights/'+str(i)
file_path = './data/poetry_add'+str(i)+'.txt'
(x_train, y_train), VOC = load_data(file_path)
VOC_SIZE = len(VOC)
print('training set information:')
print('vocabulary size:', VOC_SIZE)
print('X shape:', x_train.shape)
print('Y shape:', y_train.shape)
run()
def load_data():
training, test, training_fe = preprocessor.load_data()
#result = training + test
result = training + test
return result
dest='mode',
type='choice',
choices=['train', 'test', 'parsing'],
default='test')
optpr.mode = 'train'
# In[3]:
print('GPU:', torch.cuda.get_device_name(0),
', # of gpu:(' + str(torch.cuda.device_count()) + ')')
print('Torch Version:', torch.version.cuda)
# In[4]:
training_data, test_data, dev_data, exemplar_data = preprocessor.load_data()
# In[5]:
preprocessor.data_stat()
# In[6]:
configuration = {
'token_dim': 60,
'hidden_dim': 64,
'pos_dim': 4,
'lu_dim': 64,
'lu_pos_dim': 5,
'lstm_input_dim': 64,
'lstm_dim': 64,