def run(mode, run_config):
model = Model()
estimator = tf.estimator.Estimator(
model_fn=model.model_fn,
model_dir=Config.train.model_dir,
config=run_config)
if mode == 'train':
train_data = data_loader.get_tfrecord('train')
val_data = data_loader.get_tfrecord('test')
train_input_fn, train_input_hook = data_loader.get_dataset_batch(train_data, buffer_size=5000,
batch_size=Config.train.batch_size,
scope="val")
val_input_fn, val_input_hook = data_loader.get_dataset_batch(val_data, batch_size=512,
scope="val")
while True:
print('*' * 40)
print("epoch", Config.train.epoch + 1, 'start')
print('*' * 40)
estimator.train(input_fn=train_input_fn, hooks=[train_input_hook])
estimator.evaluate(input_fn=val_input_fn, hooks=[val_input_hook])
Config.train.epoch += 1
if Config.train.epoch == Config.train.max_epoch:
break
elif mode == 'eval':
val_data = data_loader.get_tfrecord('test')
val_input_fn, val_input_hook = data_loader.get_dataset_batch(val_data, batch_size=512,
scope="val")
estimator.evaluate(input_fn=val_input_fn, hooks=[val_input_hook])
def run(mode, run_config):
model = Model()
estimator = tf.estimator.Estimator(model_fn=model.model_fn,
model_dir=Config.train.model_dir,
config=run_config)
if mode == 'train':
train_data = data_loader.get_tfrecord('train')
val_data = data_loader.get_tfrecord('test')
train_input_fn, train_input_hook = data_loader.get_dataset_batch(
train_data,
buffer_size=1000,
batch_size=Config.train.batch_size,
scope="val")
val_input_fn, val_input_hook = data_loader.get_dataset_batch(
val_data, batch_size=20, scope="val", shuffle=False)
min_loss = 100
min_loss_epoch = 0
while True:
print('*' * 40)
print("epoch", Config.train.epoch + 1, 'start')
print('*' * 40)
estimator.train(input_fn=train_input_fn, hooks=[train_input_hook])
eval_results = estimator.evaluate(input_fn=val_input_fn,
hooks=[val_input_hook])
Config.train.epoch += 1
if Config.train.epoch == Config.train.max_epoch:
break
if eval_results['loss'] < min_loss:
min_loss = eval_results['loss']
min_loss_epoch = Config.train.epoch
print('min loss:', min_loss, ' min_loss_epoch:', min_loss_epoch)
if Config.train.switch_optimizer == 1 and (Config.train.epoch -
min_loss_epoch) >= 3:
break
if Config.train.switch_optimizer == 0 and (Config.train.epoch -
min_loss_epoch) >= 3:
print('switch optimizer to SGD')
Config.train.switch_optimizer = 1
elif mode == 'eval':
val_data = data_loader.get_tfrecord('test')
val_input_fn, val_input_hook = data_loader.get_dataset_batch(
val_data, batch_size=20, scope="val", shuffle=False)
estimator.evaluate(input_fn=val_input_fn, hooks=[val_input_hook])
def run(mode, run_config, params):
model = Model()
estimator = tf.estimator.Estimator(model_fn=model.model_fn,
model_dir=Config.train.model_dir,
params=params,
config=run_config)
if Config.train.debug:
debug_hooks = tf_debug.LocalCLIDebugHook()
hooks = [debug_hooks]
else:
hooks = []
loss_hooks = tf.train.LoggingTensorHook(
{
'loss': 'loss/total_loss:0',
'step': 'global_step:0'
},
every_n_iter=Config.train.check_hook_n_iter)
train_data = data_loader.get_tfrecord(shuffle=True)
train_input_fn, train_input_hook = data_loader.get_dataset_batch(
train_data, batch_size=Config.model.batch_size, scope="train")
hooks.extend([train_input_hook, loss_hooks])
estimator.train(input_fn=train_input_fn,
hooks=hooks,
max_steps=Config.train.max_steps)
def run(mode, run_config, params):
model = Model()
# ws = tf.estimator.WarmStartSettings(ckpt_to_initialize_from='logs/pretrained/vgg_16.ckpt',vars_to_warm_start='vgg_16.*')
estimator = tf.estimator.Estimator(
model_fn=model.model_fn,
model_dir=Config.train.model_dir,
params=params,
# warm_start_from=ws,
config=run_config)
if Config.train.debug:
debug_hooks = tf_debug.LocalCLIDebugHook()
hooks = [debug_hooks]
else:
hooks = []
loss_hooks = tf.train.LoggingTensorHook({'total_loss': 'loss/add_7',
'content_loss': 'loss/mul_1',
'style_loss': 'loss/mul_6:0',
'step': 'global_step:0'}, every_n_iter=Config.train.check_hook_n_iter)
if mode == 'train':
train_data = data_loader.get_tfrecord(mode, shuffle=True)
train_input_fn, train_input_hook = data_loader.get_dataset_batch(train_data, buffer_size=1000,
batch_size=Config.model.batch_size,
scope="train")
hooks.extend([train_input_hook, loss_hooks])
estimator.train(input_fn=train_input_fn, hooks=hooks, max_steps=Config.train.max_steps)
else:
raise ValueError('no %s mode' % (mode))
def run(run_config):
model = Model()
estimator = tf.estimator.Estimator(
model_fn=model.model_fn,
model_dir=Config.train.model_dir,
config=run_config)
train_data = data_loader.get_tfrecord('train')
train_input_fn, train_input_hook = data_loader.get_dataset_batch(train_data, buffer_size=5000,
batch_size=Config.train.batch_size,
scope="train")
estimator.train(input_fn=train_input_fn, max_steps=Config.train.max_steps, hooks=[train_input_hook])
def run(mode, run_config):
model = Model()
estimator = tf.estimator.Estimator(
model_fn=model.model_fn,
model_dir=Config.train.model_dir,
config=run_config)
if mode == 'train':
train_data = data_loader.get_tfrecord('train')
val_data = data_loader.get_tfrecord('test')
train_input_fn, train_input_hook = data_loader.get_dataset_batch(train_data, buffer_size=5000,
batch_size=Config.train.batch_size,
scope="val")
val_input_fn, val_input_hook = data_loader.get_dataset_batch(val_data, batch_size=128, scope="val")
logginghook = tf.train.LoggingTensorHook({'arc_loss': "sparse_softmax_cross_entropy_loss/value:0",
'label_loss': "sparse_softmax_cross_entropy_loss_1/value:0",
'step': 'global_step:0'}, every_n_iter=100)
while True:
print('*' * 40)
print("epoch", Config.train.epoch + 1, 'start')
print('*' * 40)
estimator.train(input_fn=train_input_fn, hooks=[logginghook, train_input_hook])
estimator.evaluate(input_fn=val_input_fn, hooks=[val_input_hook, MSTHook()])
Config.train.epoch += 1
if Config.train.epoch == Config.train.max_epoch:
break
elif mode == 'eval':
val_data = data_loader.get_tfrecord('test')
val_input_fn, val_input_hook = data_loader.get_dataset_batch(val_data, batch_size=128, scope="val")
estimator.evaluate(input_fn=val_input_fn, hooks=[val_input_hook, MSTHook()])
def run(mode, run_config, params):
model = Model()
estimator = tf.estimator.Estimator(model_fn=model.model_fn,
model_dir=Config.train.model_dir,
params=params,
config=run_config)
if Config.train.debug:
debug_hooks = tf_debug.LocalCLIDebugHook()
hooks = [debug_hooks]
else:
hooks = []
loss_hooks = tf.train.LoggingTensorHook(
{
'G_loss': 'loss/G_loss/add:0',
'F_loss': 'loss/F_loss/add:0',
'D_X_loss': 'loss/D_X_loss/Mean:0',
'D_Y_loss': 'loss/D_Y_loss/Mean:0',
'step': 'global_step:0'
},
every_n_iter=Config.train.check_hook_n_iter)
if mode == 'train':
train_dataA = data_loader.get_tfrecord('trainA')
train_dataB = data_loader.get_tfrecord('trainB')
train_input_fn, train_input_hook = data_loader.get_both_batch(
train_dataA,
train_dataB,
buffer_size=1000,
batch_size=Config.train.batch_size,
scope="train")
hooks.extend(train_input_hook + [loss_hooks])
estimator.train(input_fn=train_input_fn,
hooks=hooks,
max_steps=Config.train.max_steps)
def run(mode, run_config):
model = Model()
estimator = tf.estimator.Estimator(model_fn=model.model_fn,
model_dir=Config.train.model_dir,
config=run_config)
logginghook = tf.train.LoggingTensorHook(
{
'for_loss': "Mean:0",
'back_loss': "Mean_1:0",
'step': 'global_step:0'
},
every_n_iter=100)
if mode == 'train':
for_train_data = data_loader.get_tfrecord('for-tfrecord', 'test')
for_val_data = data_loader.get_tfrecord('for-tfrecord', 'test')
back_train_data = data_loader.get_tfrecord('back-tfrecord', 'test')
back_val_data = data_loader.get_tfrecord('back-tfrecord', 'test')
train_input_fn, train_input_hook = data_loader.get_both_batch(
for_train_data,
back_train_data,
buffer_size=5000,
batch_size=Config.train.batch_size,
scope='val')
val_input_fn, val_input_hook = data_loader.get_both_batch(
for_val_data, back_val_data, batch_size=20, scope="val")
while True:
print('*' * 40)
print("epoch", Config.train.epoch + 1, 'start')
print('*' * 40)
estimator.train(input_fn=train_input_fn,
hooks=[logginghook] + train_input_hook)
estimator.evaluate(input_fn=val_input_fn, hooks=val_input_hook)
Config.train.epoch += 1
if Config.train.epoch == Config.train.max_epoch:
break
elif mode == 'eval':
for_val_data = data_loader.get_tfrecord('for-tfrecord', 'test')
back_val_data = data_loader.get_tfrecord('back-tfrecord', 'test')
val_input_fn, val_input_hook = data_loader.get_both_batch(
for_val_data, back_val_data, batch_size=20, scope="val")
estimator.evaluate(input_fn=val_input_fn, hooks=val_input_hook)
def run(mode, run_config):
model = Model()
estimator = tfgan.estimator.GANEstimator(
generator_fn=model.generator_fn,
discriminator_fn=model.discriminator_fn,
generator_loss_fn=model.generator_loss_fn,
discriminator_loss_fn=model.discriminator_loss_fn,
generator_optimizer=model.generator_optimizer,
discriminator_optimizer=model.discriminator_optimizer,
get_hooks_fn=tfgan.get_sequential_train_hooks(
tfgan.GANTrainSteps(Config.train.G_step, 1)),
config=run_config)
if Config.train.debug:
debug_hooks = tf_debug.LocalCLIDebugHook()
hooks = [debug_hooks]
else:
hooks = []
loss_hooks = tf.train.LoggingTensorHook(
{
'G_loss': 'GANHead/G_loss:0',
'D_loss': 'GANHead/D_loss:0',
'D_real_loss': 'GANHead/D_real_loss:0',
'D_gen_loss': 'GANHead/D_gen_loss:0',
'step': 'global_step:0'
},
every_n_iter=Config.train.check_hook_n_iter)
if mode == 'train':
train_data = data_loader.get_tfrecord('train')
train_input_fn, train_input_hook = data_loader.get_dataset_batch(
train_data,
buffer_size=2000,
batch_size=Config.train.batch_size,
scope="train")
hooks.extend([train_input_hook, loss_hooks])
estimator.train(input_fn=train_input_fn, hooks=hooks)
def run(mode, run_config):
model = Model()
estimator = tf.estimator.Estimator(model_fn=model.model_fn,
model_dir=Config.train.model_dir,
config=run_config)
if mode == 'train':
train_data = data_loader.get_tfrecord('train')
val_data1 = data_loader.get_tfrecord('pku_test')
val_data2 = data_loader.get_tfrecord('msr_test')
val_data3 = data_loader.get_tfrecord('ctb_test')
train_input_fn, train_input_hook = data_loader.get_dataset_batch(
train_data,
buffer_size=5000,
batch_size=Config.train.batch_size,
scope="val")
val_input_fn1, val_input_hook1 = data_loader.get_dataset_batch(
val_data1, batch_size=1024, scope="val")
val_input_fn2, val_input_hook2 = data_loader.get_dataset_batch(
val_data2, batch_size=1024, scope="val")
val_input_fn3, val_input_hook3 = data_loader.get_dataset_batch(
val_data3, batch_size=1024, scope="val")
while True:
print('*' * 40)
print("epoch", Config.train.epoch + 1, 'start')
print('*' * 40)
estimator.train(input_fn=train_input_fn, hooks=[train_input_hook])
estimator.evaluate(input_fn=val_input_fn1,
hooks=[val_input_hook1,
PRFScoreHook('pku')],
name='pku')
estimator.evaluate(input_fn=val_input_fn2,
hooks=[val_input_hook2,
PRFScoreHook('msr')],
name='msr')
estimator.evaluate(input_fn=val_input_fn3,
hooks=[val_input_hook3,
PRFScoreHook('ctb')],
name='ctb')
Config.train.epoch += 1
if Config.train.epoch == Config.train.max_epoch:
break
elif mode == 'eval':
val_data1 = data_loader.get_tfrecord('pku_test')
val_data2 = data_loader.get_tfrecord('msr_test')
val_data3 = data_loader.get_tfrecord('ctb_test')
val_input_fn1, val_input_hook1 = data_loader.get_dataset_batch(
val_data1, batch_size=1024, scope="val")
val_input_fn2, val_input_hook2 = data_loader.get_dataset_batch(
val_data2, batch_size=1024, scope="val")
val_input_fn3, val_input_hook3 = data_loader.get_dataset_batch(
val_data3, batch_size=1024, scope="val")
estimator.evaluate(input_fn=val_input_fn1,
hooks=[val_input_hook1,
PRFScoreHook('pku')],
name='pku')
estimator.evaluate(input_fn=val_input_fn2,
hooks=[val_input_hook2,
PRFScoreHook('msr')],
name='msr')
estimator.evaluate(input_fn=val_input_fn3,
hooks=[val_input_hook3,
PRFScoreHook('ctb')],
name='ctb')
