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])
def get_lr(epoch):
w = epoch // 10
lr = init_lr / (lr_epoch_decay**w)
if lr < 1e-10:
lr = 1e-10
return lr
callback = LearningRateScheduler(get_lr)
callbacks.append(callback)
return callbacks
model = Unet(input_shape, num)
model.summary()
if os.path.exists(mode1_path): #继续训练
model.load_weights(mode1_path)
callbacks = make_callbacks()
path = r'F:\cmm\yumi\pic'
train_set, val_set = get_train_val(path, 'tif')
train = train_data(train_set, bitchs)
val = val_data(val_set, bitchs)
alltrain = len(train_set)
allval = len(val_set)
loss_f = loss.mean_iou
metrics = [loss_f]
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=metrics)
A = model.fit_generator(generator=train,
from unet import Unet unet = Unet() unet.summary()
