def predict(data_path, model_str, ckpt_path_=''):
#add models here:
if model_str == 'unet': from designs import unet as design
if model_str == 'unet64filters':
from designs import unet64filters as design
if model_str == 'unet64filters_weighted':
from designs import unet64filters as design
if model_str == 'flatunet': from designs import flatunet as design
if model_str == 'unet64batchnorm':
from designs import unet64batchnorm as design
if model_str == 'u64_onehot': from designs import u64_onehot as design
if model_str == 'nomaxpool': from designs import u64_onehot as design
imgs_train, imgs_mask_train = load_train_data(data_path)
print('Creating and compiling model...')
model = design.build()
print('Loading and preprocessing test data...')
imgs_test, imgs_id_test = load_test_data(data_path)
imgs_test = preprocess(imgs_test)
imgs_test = normalize(imgs_test)
if ckpt_path_ == '':
ckpt_path = os.path.join(data_path, 'internal/checkpoints')
else:
ckpt_path = ckpt_path_
ckpt_file = os.path.join(ckpt_path, 'weights_' + model_str + '.h5')
model.load_weights(ckpt_file)
print('Loading saved weights :', ckpt_file)
print('Predicting masks on test data...')
imgs_mask_test = model.predict(imgs_test, verbose=1)
out_path = os.path.join(data_path, 'output')
out_file = os.path.join(data_path, 'internal/npy/imgs_mask_test.npy')
if ckpt_path_ != '':
if not os.path.exists(ckpt_path + '/predictions/' + model_str + '/'):
os.mkdir(ckpt_path + '/predictions/' + model_str + '/')
out_path = ckpt_path + '/predictions/'
out_file = ckpt_path + '/predictions/' + model_str + '/imgs_mask_test.npy'
np.save(out_file, imgs_mask_test)
print('Saving predicted masks to files to:', out_path + '/' + model_str)
if not os.path.exists(out_path + '/' + model_str):
os.mkdir(out_path + '/' + model_str)
for image, image_id in zip(imgs_mask_test, imgs_id_test):
image = (image[:, :, 0] * 255.).astype(np.uint8)
imsave(
os.path.join(out_path + '/' + model_str,
'{0:0>5}_pred.png'.format(image_id)), image)
from common import ex_num
N_day = 30
M_day = 30
N = 2000
hidden = [128, 64, 32]
corruption_levels = [0.0, 0.0, 0.0]
cuda.init(0)
# Prepare dataset
#データセットのロード
print('load dataset')
ex = data.load_exchange_data()
# データの正規化
exchange = data.normalize(ex)
x_all = data.make_xdata(exchange, N_day, M_day)
x_train, x_test = np.vsplit(x_all, [N])
in_units = x_train.shape[1] # 入力層のユニット数
train = [x_train, x_test]
teach = exchange[:, ex_num.ExNumber(1)].reshape(len(exchange[:, 0]), 1)
y_all = data.make_ydata(teach, N_day, M_day)
y_train, y_test = np.vsplit(y_all, [N])
test = [y_train, y_test]
for i in [1, 2, 4]:
SA_file = "SA%d.model" % i
n_hidden = [x * i for x in hidden]
def train(data_path,
model_str,
number_of_epochs=2,
batch_size=10,
test_data_fraction=0.15,
checkpoint_period=10,
load_prev_weights=False,
early_stop_patience=10):
#add models here:
if model_str == 'unet': from designs import unet as design
if model_str == 'unet64filters':
from designs import unet64filters as design
if model_str == 'unet64filters_weighted':
from designs import unet64filters_weighted as design
if model_str == 'flatunet': from designs import flatunet as design
if model_str == 'unet64batchnorm':
from designs import unet64batchnorm as design
if model_str == 'u64_onehot': from designs import u64_onehot as design
if model_str == 'nomaxpool': from designs import nomaxpool as design
# DATA LOADING AND PREPROCESSING
print('Loading and preprocessing train data...')
# load input images
imgs_train, imgs_mask_train = load_train_data(data_path)
imgs_ew = load_ew_data(data_path)
imgs_train = preprocess(imgs_train)
imgs_mask_train = preprocess(imgs_mask_train)
imgs_ew = preprocess(imgs_ew)
imgs_train = normalize(imgs_train)
imgs_mask_train = normalize_mask(imgs_mask_train)
imgs_ew = normalize_errorweight(imgs_ew)
if 'weighted' in model_str:
mask_combined = []
for i in range(len(imgs_mask_train)):
mask_combined.append(
np.concatenate([imgs_mask_train[i], imgs_ew[i]], axis=2))
print(mask_combined[0])
# BUILD MODEL
print('Creating and compiling model...')
model = design.build()
#print layout of model:
#model.summary()
# set up saving weights at checkpoints,
if not os.path.exists(data_path + '/internal/checkpoints'):
os.makedirs(data_path + '/internal/checkpoints')
ckpt_dir = os.path.join(data_path, 'internal/checkpoints')
ckpt_file = os.path.join(ckpt_dir,
'weights_' + model_str + '_epoch{epoch:02d}.h5')
model_checkpoint = ModelCheckpoint(ckpt_file,
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
period=checkpoint_period)
# save epoch logs to txt
CSV_LOG_FILENAME = os.path.join(ckpt_dir, 'log_' + model_str + '.csv')
csv_logger = CSVLogger(CSV_LOG_FILENAME)
# Early stopping
early_stop = EarlyStopping(monitor='val_loss',
patience=early_stop_patience,
verbose=1)
if (load_prev_weights):
try:
model.load_weights(data_path +
'/internal/prev_checkpoints_to_load/weights_' +
model_str + '.h5')
print(
'Loading prev weights:',
data_path + '/internal/prev_checkpoints_to_load/weights_' +
model_str + '.h5')
except Exception as e:
print('Problem loading the saved weight!')
print(e)
return
# FIT MODEL
print('Fitting model...')
model_out = model.fit(imgs_train,
imgs_mask_train,
batch_size=batch_size,
epochs=number_of_epochs,
verbose=1,
shuffle=True,
validation_split=test_data_fraction,
callbacks=[model_checkpoint, csv_logger, early_stop])
model.save(ckpt_dir + '/weights_' + model_str +
'.h5') #save model and final weights.
return model_out