def lead_to_sample():
x, y = load_data()
x, y, x_test, y_test = utils.split_data(x, y, conf.train_ratio)
x = x.reshape(-1, 5000, 1)
y = np.repeat(y, 12)
print('x.shape,y.shape', x.shape, y.shape)
print('x_test.shape,y_test.shape', x_test.shape, y_test.shape)
return x, y, x_test, y_test
def train_model(x, y, model_compiled):
scores = []
if not conf.ensemble:
print(x.shape, y.shape)
x_train, y_train, x_test, y_test = utils.split_data(
x, y, train_ratio=conf.train_ratio)
'''
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape, '\n', y_train, '\n', y_test)
if conf.lead_as_sample:
x_train = x_train.reshape(-1, 5000, 1)
x_test = x_test.reshape(-1, 5000, 1)
y_train = np.repeat(y_train, 12)
y_test = np.repeat(y_test, 12)
'''
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape, '\n',
y_train, '\n', y_test)
model = model_compiled
score = fit_model(x_train, y_train, x_test, y_test, model, 0)
print('F1 score of the model: ', score)
else:
kf = KFold(n_splits=conf.num_model)
# sss = StratifiedShuffleSplit(n_splits=conf.num_model,test_size=1-conf.train_ratio,
# random_state=conf.seed,)
i = 1
for train_index, test_index in kf.split(x, y):
print("TRAIN:", train_index, "TEST:", test_index)
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape,
y_test)
model = model_compiled
score = fit_model(x_train, y_train, x_test, y_test, model, i)
scores.append(score)
i = i + 1
scores = np.array(scores)
print('F1 score of all model: ', scores)
print('F1 score mean+/-std: ',
"%0.3f (+/- %0.3f)" % (np.mean(scores), np.std(scores)))
Epoch 6/20
96/96 [==============================] - 15s - loss: 0.2773 - acc: 0.9167 - val_loss: 0.3014 - val_acc: 0.9286
Epoch 7/20
96/96 [==============================] - 15s - loss: 0.2409 - acc: 0.9167 - val_loss: 0.2914 - val_acc: 0.9286
Epoch 8/20
96/96 [==============================] - 15s - loss: 0.2181 - acc: 0.9375 - val_loss: 0.2629 - val_acc: 0.9286
'''
method = 'hks'
x_data, y_data = read_data(descriptor_dir='shrec11-kp',
method=method,
descriptor_rows=KP_DESCRIPTOR_ROWS,
descriptor_cols=KP_DESCRIPTOR_COLS)
(train_x, val_x, train_y, val_y) = split_data(x_data,
y_data,
split_percentage=0.7)
train_x = train_x.reshape((-1, KP_UNITS))
val_x = val_x.reshape((-1, KP_UNITS))
mlp_model = MLP(input_units=KP_UNITS,
output_units=OUTPUT_UNITS,
hidden_layers=(10000, ),
activations=('relu', 'softmax'))
scores = mlp_model.train(train_x,
train_y,
val_x,
val_y,
epochs=12,
@author: danna.li
@date: 2019/4/2
@file: train_tradition.py
@description: python -W ignore train_tradition.py
"""
from common.conf import current_config as conf
from tradition.extract_feature import get_all_feature
import common.utils as utils
import tradition.tradition_model as tradition_model
import os
signals = utils.load_dataset()
labels = utils.load_label()
all_feature = get_all_feature(signals)
x_train, y_train, x_test, y_test = utils.split_data(all_feature, labels,
conf.train_ratio)
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
if __name__ == '__main__':
utils.re_create_path(conf.output_dir)
save_model = False
print('fitting knn model..')
result_file = os.path.join(conf.output_dir, 'knn01.txt')
tradition_model.do_knn(x_train, y_train, x_test, y_test, save_model,
result_file)
print('fitting svm model..')
result_file = os.path.join(conf.output_dir, 'svm01.txt')
tradition_model.do_svm(x_train, y_train, x_test, y_test, save_model,
result_file)