def test_model():
vocab, embeddings = data_helper.load_embeddings(config.get('data', 'embedding_file'))
model = RNNModel(embeddings, num_classes=5)
model.load(config.get('data', 'model_dir'))
test_data = data_helper.load_data(os.path.join(config.get('data', 'treebank_dir'), 'test.txt'))
numeric_test_samples = data_helper.convert_to_numeric_samples(test_data, vocab, num_classes=5)
model.eval(numeric_test_samples)
class EnsembleModel(_ModelBase):
def __init__(self):
super().__init__()
self.mfcc_rnn = RNNModel()
self.pitch_clf01 = PitchModel([0, 1])
self.pitch_clf67 = PitchModel([6, 7])
self.mfcc_rnn.load()
self.pitch_clf01.load()
self.pitch_clf67.load()
self.mfcc_rnn.clf.eval()
def test(self):
dev_data = self.reader.mini_batch_iterator(self.reader.val_person)
y, pred = [], []
for itr, total_iter, feat, label, files in dev_data:
pred_, prob_ = self.mfcc_rnn.test_iter(itr, total_iter, feat,
label, files)
for i, p in enumerate(pred_):
if p in [0, 1] and prob_[i][p] < 0.8:
pred_[i] = self.pitch_clf01.test_iter(*feat[i])
if p in [6, 7] and prob_[i][p] < 0.7:
pred_[i] = self.pitch_clf67.test_iter(*feat[i])
y.extend(label)
pred.extend(pred_)
printer.info('%d/%d' % (itr, total_iter))
# for i,_ in enumerate(pred_):
# if pred_[i] != label[i]:
# logger.info(files[i])
# if itr > 1000: break
acc = accuracy_score(y, pred)
printer.info(acc)
cm = confusion_matrix(y, pred)
pickle.dump(cm, open('models/cm.pkl', 'wb'))
print(cm)
return acc
def interactive(self):
test_data = self.reader.new_file_detect_iterator()
y, pred = [], []
for itr, total_iter, feat, label, files in test_data:
pred_, prob_ = self.mfcc_rnn.test_iter(itr, total_iter, feat,
label, files)
for i, p in enumerate(pred_):
if p in [0, 1] and prob_[i][p] < 0.8:
pred_[i] = self.pitch_clf01.test_iter(*feat[i])
if p in [6, 7] and prob_[i][p] < 0.7:
pred_[i] = self.pitch_clf67.test_iter(*feat[i])
print('***Prediction***\n%s\n' % output_dict[pred_[0]])
print('***Confidence***\n%s\n' % str(prob_[0][pred_[0]]))
exit(0)
tf.gfile.MakeDirs(config.summary_dir)
tf.gfile.MakeDirs(config.checkpoint_dir)
model = RNNModel(config)
with tf.Session() as sess:
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer(),
tf.tables_initializer()
])
model.load(sess)
trainer = Trainer(sess, model, config)
if args.train:
trainer.train()
if args.test:
for line in sys.stdin:
try:
data_x = np.array([brackets.parse_string(line)])
data_y = np.array([brackets.statistics(b) for b in data_x])
_, preds, _, _ = trainer.predict(data_x, data_y)
for x, y, p in zip(data_x, data_y, preds):
print(x, ',', *y, ',', *p)
except:
