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,
steps_per_epoch=alltrain // bitchs,
epochs=epochs,
verbose=1,
validation_data=val,
validation_steps=allval // bitchs,
callbacks=callbacks,
max_q_size=1)
def train_model(train_data,
val_data,
num_train,
num_val,
epochs,
callback=True): #callback用于是否记录训练。
if model_name == "deeplabv3":
model = Deeplabv3((HEIGHT, WIDTH, 3), NCLASSES, bone_name)
elif model_name == 'unet':
model = Unet((HEIGHT, WIDTH, 3), NCLASSES, bone_name)
elif model_name == 'pspnet':
model = PSPnet((HEIGHT, WIDTH, 3), NCLASSES, bone_name)
epoch = 0
if init_with == 'first': #init_with selection = ['first', 'last'] 选择模型的训练模式
print('-' * 100)
# model.load_weights(weights_path, by_name=True, skip_mismatch=True) #加载预训练模型
print('开始从头训练模型...')
else:
model.load_weights(find_last(log_path), by_name=True)
epoch = int(
os.path.basename(find_last(log_path))[:-3].split('_')[-1])
epochs = epoch + epochs #这样可以确保重新训练时，设置的epochs为实际训练的轮数。
print('-' * 100)
print('成功加载最新模型, 重新从第%s轮开始训练...' % epoch)
#保存训练过程
tbCallBack = TensorBoard(log_dir=log_path + "records/",
histogram_freq=0,
write_graph=True,
write_images=True)
# 保存的方式，1次epoch保存一次
checkpoint_path = os.path.join(
log_path, '%s_%s_*epoch*.h5' % (model_name, bone_name))
checkpoint_path = checkpoint_path.replace("*epoch*", "{epoch:04d}")
checkpoint_period = ModelCheckpoint(checkpoint_path,
monitor='val_loss',
save_weights_only=True,
save_best_only=True,
period=1)
# 学习率下降的方式，val_loss五次不下降就下降学习率继续训练
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=5,
verbose=1)
# 是否需要早停，当val_loss一直不下降的时候意味着模型基本训练完毕，可以停止
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=15,
verbose=1)
callbacks = [checkpoint_period, reduce_lr, early_stopping]
if callback:
tbCallBack.set_model(model)
callbacks.append(tbCallBack)
print('训练的样本量为：{} 张图片, 验证集的样本量为：{} 张图片，'.format(num_train, num_val))
print('每份数据集大小为：{} 张图片, 图片大小为: {}'.format(batch_size, (HEIGHT, WIDTH)))
print('以%s为主干的%s模型正式开始训练，请耐心等待，并注意提示...' %
(bone_name, model_name)) #开始训练
print('-' * 100)
model.compile(loss=focal_loss,
optimizer=Adam(lr=1e-3),
metrics=['accuracy'])
model.fit_generator(generate_arrays_from_file(train_data, batch_size),
steps_per_epoch=max(1, num_train // batch_size),
validation_data=generate_arrays_from_file(
val_data, batch_size),
validation_steps=max(1, num_val // batch_size),
epochs=epochs,
initial_epoch=epoch,
shuffle=True,
callbacks=callbacks)
