def main():
config = configparser.RawConfigParser()
config.read('config.txt')
experiment_name = config.get('train', 'name')
if not os.path.exists('./logs/' + experiment_name):
os.system('mkdir ./logs/' + experiment_name)
epochs_num = int(config.get('train', 'epochs_num'))
batch_size = int(config.get('train', 'batch_size'))
# Load datasets.
datasets = config.get('train', 'datasets')
datasets_path = config.get(datasets, 'h5py_save_path')
height = int(config.get(datasets, 'height'))
width = int(config.get(datasets, 'width'))
pad_height = int(config.get(datasets, 'pad_height'))
pad_width = int(config.get(datasets, 'pad_width'))
x_train, y_train, masks = Generator(datasets_path, 'train', height, width,
pad_height, pad_width)()
visualize(group_images(x_train, 4),
'./logs/' + experiment_name + '/train_images.png').show()
visualize(group_images(y_train, 4),
'./logs/' + experiment_name + '/train_labels.png').show()
visualize(group_images(masks, 4),
'./logs/' + experiment_name + '/train_masks.png').show()
y_train = to_categorical(y_train)
# Build model and save.
unet = Unet((pad_height, pad_width, 1), 5)
unet.summary()
unet_json = unet.to_json()
open('./logs/' + experiment_name + '/architecture.json',
'w').write(unet_json)
plot_model(unet, to_file='./logs/' + experiment_name + '/model.png')
# Training.
checkpointer = ModelCheckpoint(filepath='./logs/' + experiment_name +
'/weights.h5',
verbose=1,
monitor='val_loss',
mode='auto',
save_best_only=True)
unet.fit(
x_train,
y_train,
epochs=epochs_num,
batch_size=batch_size,
verbose=1,
shuffle=True,
validation_split=0.1,
#class_weight=(0.5, 1.3),
callbacks=[checkpointer])
batch_size = int(config.get('train', 'batch_size'))
# Load datasets.
datasets = config.get('train', 'datasets')
sub_height = int(config.get('generator', 'sub_height'))
sub_width = int(config.get('generator', 'sub_width'))
x_train, y_train = Generator(datasets, 'train', config)()
y_train = to_categorical(y_train)
print(np.max(x_train), np.min(x_train), x_train.shape, x_train.dtype)
print(np.max(y_train), np.min(y_train), y_train.shape, y_train.dtype)
# Build model and save.
unet = Unet((sub_height, sub_width, 1))
unet.summary()
unet_json = unet.to_json()
open('./logs/' + experiment_name + '_architecture.json', 'w').write(unet_json)
plot_model(unet, to_file='./logs/' + experiment_name + '_model.png')
# Training.
checkpointer = ModelCheckpoint(filepath='./logs/' + experiment_name +
'_best_weights.h5',
verbose=1,
monitor='val_loss',
mode='auto',
save_best_only=True)
unet.fit(x_train,
y_train,
epochs=epochs_num,
batch_size=batch_size,
