def train(name, hparams, multi_gpu=False, n_models=1, train_completeness_threshold=0.01,
seed=None, logdir='data/logs', max_epoch=100, patience=2, train_sampling=1.0,
eval_sampling=1.0, eval_memsize=5, gpu=0, gpu_allow_growth=False, save_best_model=False,
forward_split=False, write_summaries=False, verbose=False, asgd_decay=None, tqdm=True,
side_split=True, max_steps=None, save_from_step=None, do_eval=True, predict_window=63):
eval_k = int(round(26214 * eval_memsize / n_models))
eval_batch_size = int(
eval_k / (hparams.rnn_depth * hparams.encoder_rnn_layers)) # 128 -> 1024, 256->512, 512->256
eval_pct = 0.1
batch_size = hparams.batch_size
train_window = hparams.train_window
tf.reset_default_graph()
if seed:
tf.set_random_seed(seed)
with tf.device("/cpu:0"):
inp = VarFeeder.read_vars("data/vars")
if side_split:
splitter = Splitter(page_features(inp), inp.page_map, 3, train_sampling=train_sampling,
test_sampling=eval_sampling, seed=seed)
else:
splitter = FakeSplitter(page_features(inp), 3, seed=seed, test_sampling=eval_sampling)
real_train_pages = splitter.splits[0].train_size
real_eval_pages = splitter.splits[0].test_size
items_per_eval = real_eval_pages * eval_pct
eval_batches = int(np.ceil(items_per_eval / eval_batch_size))
steps_per_epoch = real_train_pages // batch_size
eval_every_step = int(round(steps_per_epoch * eval_pct))
# eval_every_step = int(round(items_per_eval * train_sampling / batch_size))
global_step = tf.train.get_or_create_global_step()
inc_step = tf.assign_add(global_step, 1)
all_models: List[ModelTrainerV2] = []
def create_model(scope, index, prefix, seed):
with tf.variable_scope('input') as inp_scope:
with tf.device("/cpu:0"):
split = splitter.splits[index]
pipe = InputPipe(inp, features=split.train_set, n_pages=split.train_size,
mode=ModelMode.TRAIN, batch_size=batch_size, n_epoch=None, verbose=verbose,
train_completeness_threshold=train_completeness_threshold,
predict_completeness_threshold=train_completeness_threshold, train_window=train_window,
predict_window=predict_window,
rand_seed=seed, train_skip_first=hparams.train_skip_first,
back_offset=predict_window if forward_split else 0)
inp_scope.reuse_variables()
if side_split:
side_eval_pipe = InputPipe(inp, features=split.test_set, n_pages=split.test_size,
mode=ModelMode.EVAL, batch_size=eval_batch_size, n_epoch=None,
verbose=verbose, predict_window=predict_window,
train_completeness_threshold=0.01, predict_completeness_threshold=0,
train_window=train_window, rand_seed=seed, runs_in_burst=eval_batches,
back_offset=predict_window * (2 if forward_split else 1))
else:
side_eval_pipe = None
if forward_split:
forward_eval_pipe = InputPipe(inp, features=split.test_set, n_pages=split.test_size,
mode=ModelMode.EVAL, batch_size=eval_batch_size, n_epoch=None,
verbose=verbose, predict_window=predict_window,
train_completeness_threshold=0.01, predict_completeness_threshold=0,
train_window=train_window, rand_seed=seed, runs_in_burst=eval_batches,
back_offset=predict_window)
else:
forward_eval_pipe = None
avg_sgd = asgd_decay is not None
#asgd_decay = 0.99 if avg_sgd else None
train_model = Model(pipe, hparams, is_train=True, graph_prefix=prefix, asgd_decay=asgd_decay, seed=seed)
scope.reuse_variables()
eval_stages = []
if side_split:
side_eval_model = Model(side_eval_pipe, hparams, is_train=False,
#loss_mask=np.concatenate([np.zeros(50, dtype=np.float32), np.ones(10, dtype=np.float32)]),
seed=seed)
eval_stages.append((Stage.EVAL_SIDE, side_eval_model))
if avg_sgd:
eval_stages.append((Stage.EVAL_SIDE_EMA, side_eval_model))
if forward_split:
forward_eval_model = Model(forward_eval_pipe, hparams, is_train=False, seed=seed)
eval_stages.append((Stage.EVAL_FRWD, forward_eval_model))
if avg_sgd:
eval_stages.append((Stage.EVAL_FRWD_EMA, forward_eval_model))
if write_summaries:
summ_path = f"{logdir}/{name}_{index}"
if os.path.exists(summ_path):
shutil.rmtree(summ_path)
summ_writer = tf.summary.FileWriter(summ_path) # , graph=tf.get_default_graph()
else:
summ_writer = None
if do_eval and forward_split:
stop_metric = lambda metrics: metrics[Stage.EVAL_FRWD]['SMAPE'].avg_epoch
else:
stop_metric = None
return ModelTrainerV2(train_model, eval_stages, index, patience=patience,
stop_metric=stop_metric,
summary_writer=summ_writer)
if n_models == 1:
with tf.device(f"/gpu:{gpu}"):
scope = tf.get_variable_scope()
all_models = [create_model(scope, 0, None, seed=seed)]
else:
for i in range(n_models):
device = f"/gpu:{i}" if multi_gpu else f"/gpu:{gpu}"
with tf.device(device):
prefix = f"m_{i}"
with tf.variable_scope(prefix) as scope:
all_models.append(create_model(scope, i, prefix=prefix, seed=seed + i))
trainer = MultiModelTrainer(all_models, inc_step)
if save_best_model or save_from_step:
saver_path = f'data/cpt/{name}'
if os.path.exists(saver_path):
shutil.rmtree(saver_path)
os.makedirs(saver_path)
saver = tf.train.Saver(max_to_keep=10, name='train_saver')
else:
saver = None
avg_sgd = asgd_decay is not None
if avg_sgd:
from itertools import chain
def ema_vars(model):
ema = model.train_model.ema
return {ema.average_name(v):v for v in model.train_model.ema._averages}
ema_names = dict(chain(*[ema_vars(model).items() for model in all_models]))
#ema_names = all_models[0].train_model.ema.variables_to_restore()
ema_loader = tf.train.Saver(var_list=ema_names, max_to_keep=1, name='ema_loader')
ema_saver = tf.train.Saver(max_to_keep=1, name='ema_saver')
else:
ema_loader = None
init = tf.global_variables_initializer()
if forward_split and do_eval:
eval_smape = trainer.metric(Stage.EVAL_FRWD, 'SMAPE')
eval_mae = trainer.metric(Stage.EVAL_FRWD, 'MAE')
else:
eval_smape = DummyMetric()
eval_mae = DummyMetric()
if side_split and do_eval:
eval_mae_side = trainer.metric(Stage.EVAL_SIDE, 'MAE')
eval_smape_side = trainer.metric(Stage.EVAL_SIDE, 'SMAPE')
else:
eval_mae_side = DummyMetric()
eval_smape_side = DummyMetric()
train_smape = trainer.metric(Stage.TRAIN, 'SMAPE')
train_mae = trainer.metric(Stage.TRAIN, 'MAE')
grad_norm = trainer.metric(Stage.TRAIN, 'GrNorm')
eval_stages = []
ema_eval_stages = []
if forward_split and do_eval:
eval_stages.append(Stage.EVAL_FRWD)
ema_eval_stages.append(Stage.EVAL_FRWD_EMA)
if side_split and do_eval:
eval_stages.append(Stage.EVAL_SIDE)
ema_eval_stages.append(Stage.EVAL_SIDE_EMA)
# gpu_options=tf.GPUOptions(allow_growth=False),
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=gpu_allow_growth))) as sess:
sess.run(init)
# pipe.load_vars(sess)
inp.restore(sess)
for model in all_models:
model.init(sess)
# if beholder:
# visualizer = Beholder(session=sess, logdir=summ_path)
step = 0
prev_top = np.inf
best_smape = np.inf
# Contains best value (first item) and subsequent values
best_epoch_smape = []
for epoch in range(max_epoch):
# n_steps = pusher.n_pages // batch_size
if tqdm:
tqr = trange(steps_per_epoch, desc="%2d" % (epoch + 1), leave=False)
else:
tqr = range(steps_per_epoch)
for _ in tqr:
try:
step = trainer.train_step(sess, epoch)
except tf.errors.OutOfRangeError:
break
# if beholder:
# if step % 5 == 0:
# noinspection PyUnboundLocalVariable
# visualizer.update()
if step % eval_every_step == 0:
if eval_stages:
trainer.eval_step(sess, epoch, step, eval_batches, stages=eval_stages)
if save_best_model and epoch > 0 and eval_smape.last < best_smape:
best_smape = eval_smape.last
saver.save(sess, f'data/cpt/{name}/cpt', global_step=step)
if save_from_step and step >= save_from_step:
saver.save(sess, f'data/cpt/{name}/cpt', global_step=step)
if avg_sgd and ema_eval_stages:
ema_saver.save(sess, 'data/cpt_tmp/ema', write_meta_graph=False)
# restore ema-backed vars
ema_loader.restore(sess, 'data/cpt_tmp/ema')
trainer.eval_step(sess, epoch, step, eval_batches, stages=ema_eval_stages)
# restore normal vars
ema_saver.restore(sess, 'data/cpt_tmp/ema')
MAE = "%.3f/%.3f/%.3f" % (eval_mae.last, eval_mae_side.last, train_mae.last)
improvement = '↑' if eval_smape.improved else ' '
SMAPE = "%s%.3f/%.3f/%.3f" % (improvement, eval_smape.last, eval_smape_side.last, train_smape.last)
if tqdm:
tqr.set_postfix(gr=grad_norm.last, MAE=MAE, SMAPE=SMAPE)
if not trainer.has_active() or (max_steps and step > max_steps):
break
if tqdm:
tqr.close()
trainer.end_epoch()
if not best_epoch_smape or eval_smape.avg_epoch < best_epoch_smape[0]:
best_epoch_smape = [eval_smape.avg_epoch]
else:
best_epoch_smape.append(eval_smape.avg_epoch)
current_top = eval_smape.top
if prev_top > current_top:
prev_top = current_top
has_best_indicator = '↑'
else:
has_best_indicator = ' '
status = "%2d: Best top SMAPE=%.3f%s (%s)" % (
epoch + 1, current_top, has_best_indicator,
",".join(["%.3f" % m.top for m in eval_smape.metrics]))
if trainer.has_active():
status += ", frwd/side best MAE=%.3f/%.3f, SMAPE=%.3f/%.3f; avg MAE=%.3f/%.3f, SMAPE=%.3f/%.3f, %d am" % \
(eval_mae.best_epoch, eval_mae_side.best_epoch, eval_smape.best_epoch, eval_smape_side.best_epoch,
eval_mae.avg_epoch, eval_mae_side.avg_epoch, eval_smape.avg_epoch, eval_smape_side.avg_epoch,
trainer.has_active())
print(status, file=sys.stderr)
else:
print(status, file=sys.stderr)
print("Early stopping!", file=sys.stderr)
break
if max_steps and step > max_steps:
print("Max steps calculated", file=sys.stderr)
break
sys.stderr.flush()
# noinspection PyUnboundLocalVariable
return np.mean(best_epoch_smape, dtype=np.float64)
def train(name,
hparams,
multi_gpu=False,
n_models=1,
train_completeness_threshold=0.01,
seed=None,
logdir='data/logs',
max_epoch=100,
patience=2,
train_sampling=1.0,
eval_sampling=1.0,
eval_memsize=5,
gpu=0,
gpu_allow_growth=False,
save_best_model=False,
forward_split=False,
write_summaries=False,
verbose=False,
asgd_decay=None,
tqdm=True,
side_split=True,
max_steps=None,
save_from_step=None,
do_eval=True,
predict_window=63):
eval_k = int(round(26214 * eval_memsize / n_models))
eval_batch_size = int(
eval_k /
(hparams.rnn_depth *
hparams.encoder_rnn_layers)) # 128 -> 1024, 256->512, 512->256
eval_pct = 0.1
batch_size = hparams.batch_size
train_window = hparams.train_window
# todo eval_k = 43690,eval_batch_size = 163,eval_pct = 0,batch_size = 128,train_window = 283
# print("eval_k = %d,eval_batch_size = %d,eval_pct = %d,batch_size = %d,train_window = %d" %(eval_k,eval_batch_size,eval_pct,batch_size,train_window))
tf.reset_default_graph()
if seed:
tf.set_random_seed(seed)
with tf.device("/cpu:0"):
inp = VarFeeder.read_vars("data/vars")
# print("side_split = %d,train_sampling= %d,eval_sampling= %d,seed= %d" % (
# side_split,train_sampling,eval_sampling,seed),f"inp={inp}, side_split={side_split}; type(inp)={type(inp)}")
# todo side_split = 0,train_sampling= 1,eval_sampling= 1,seed= 5
# inp={'hits': <tf.Variable 'hits:0' shape=(145036, 805) dtype=float32_ref>,
# 'lagged_ix': <tf.Variable 'lagged_ix:0' shape=(867, 4) dtype=int16_ref>,
# 'page_map': <tf.Variable 'page_map:0' shape=(52752, 4) dtype=int32_ref>,
# 'page_ix': <tf.Variable 'page_ix:0' shape=(145036,) dtype=string_ref>,
# 'pf_agent': <tf.Variable 'pf_agent:0' shape=(145036, 4) dtype=float32_ref>,
# 'pf_country': <tf.Variable 'pf_country:0' shape=(145036, 7) dtype=float32_ref>,
# 'pf_site': <tf.Variable 'pf_site:0' shape=(145036, 3) dtype=float32_ref>,
# 'page_popularity': <tf.Variable 'page_popularity:0' shape=(145036,) dtype=float32_ref>,
# 'year_autocorr': <tf.Variable 'year_autocorr:0' shape=(145036,) dtype=float32_ref>,
# 'quarter_autocorr': <tf.Variable 'quarter_autocorr:0' shape=(145036,) dtype=float32_ref>,
# 'dow': <tf.Variable 'dow:0' shape=(867, 2) dtype=float32_ref>,'features_days': 867,
# 'data_days': 805, 'n_pages': 145036, 'data_start': '2015-07-01',
# 'data_end': Timestamp('2017-09-11 00:00:00'), 'features_end': Timestamp('2017-11-13 00:00:00')}
# side_split=False;
# type(inp)=<class 'feeder.FeederVars'>;
# if True:
if side_split:
splitter = Splitter(page_features(inp),
inp.page_map,
3,
train_sampling=train_sampling,
test_sampling=eval_sampling,
seed=seed)
else:
splitter = FakeSplitter(page_features(inp),
3,
seed=seed,
test_sampling=eval_sampling)
real_train_pages = splitter.splits[0].train_size
real_eval_pages = splitter.splits[0].test_size
items_per_eval = real_eval_pages * eval_pct
eval_batches = int(np.ceil(items_per_eval / eval_batch_size))
steps_per_epoch = real_train_pages // batch_size
eval_every_step = int(round(steps_per_epoch * eval_pct))
# todo real_train_pages = 145036,real_eval_pages= 145036,items_per_eval= 14503,eval_batches= 89,
# steps_per_epoch= 1133,eval_every_step= 113 -- 每个epoch有1133个step，每113个step打印一下当前模型的效果
# print("real_train_pages = %d,real_eval_pages= %d,items_per_eval= %d,eval_batches= %d,steps_per_epoch= %d,eval_every_step= %d; eval_pct" % (
# real_train_pages, real_eval_pages, items_per_eval, eval_batches, steps_per_epoch, eval_every_step,eval_pct
# ))
# return
# eval_every_step = int(round(items_per_eval * train_sampling / batch_size))
# todo get_or_create_global_step 这个函数主要用于返回或者创建（如果有必要的话）一个全局步数的tensor变量。
global_step = tf.train.get_or_create_global_step()
# todo tf.assign_add(ref,value,use_locking=None,name=None)；通过增加value，更新ref的值，即：ref = ref + value；
# inc increase_step
inc_step = tf.assign_add(global_step, 1)
all_models: List[ModelTrainerV2] = []
def create_model(scope, index, prefix, seed):
# todo 主要是创建了模型，以及返回一些None的东西。
# 数据在构建模型的时候使用了，模型中只使用了数据的预测窗口的长度--不对，应该是创建模型的时候直接喂入数据了。
with tf.variable_scope('input') as inp_scope:
with tf.device("/cpu:0"):
split = splitter.splits[index]
pipe = InputPipe(
inp,
features=split.train_set,
n_pages=split.train_size,
mode=ModelMode.TRAIN,
batch_size=batch_size,
n_epoch=None,
verbose=verbose,
train_completeness_threshold=train_completeness_threshold,
predict_completeness_threshold=train_completeness_threshold,
train_window=train_window,
predict_window=predict_window,
rand_seed=seed,
train_skip_first=hparams.train_skip_first,
back_offset=predict_window if forward_split else 0)
inp_scope.reuse_variables()
# todo side_split: False; forward_split:False; eval_stages: [];
if side_split:
side_eval_pipe = InputPipe(
inp,
features=split.test_set,
n_pages=split.test_size,
mode=ModelMode.EVAL,
batch_size=eval_batch_size,
n_epoch=None,
verbose=verbose,
predict_window=predict_window,
train_completeness_threshold=0.01,
predict_completeness_threshold=0,
train_window=train_window,
rand_seed=seed,
runs_in_burst=eval_batches,
back_offset=predict_window *
(2 if forward_split else 1))
else:
side_eval_pipe = None
if forward_split:
forward_eval_pipe = InputPipe(
inp,
features=split.test_set,
n_pages=split.test_size,
mode=ModelMode.EVAL,
batch_size=eval_batch_size,
n_epoch=None,
verbose=verbose,
predict_window=predict_window,
train_completeness_threshold=0.01,
predict_completeness_threshold=0,
train_window=train_window,
rand_seed=seed,
runs_in_burst=eval_batches,
back_offset=predict_window)
else:
forward_eval_pipe = None
avg_sgd = asgd_decay is not None
#asgd_decay = 0.99 if avg_sgd else None
train_model = Model(pipe,
hparams,
is_train=True,
graph_prefix=prefix,
asgd_decay=asgd_decay,
seed=seed)
scope.reuse_variables()
eval_stages = []
if side_split:
# print('2 side_split side_eval_model')
side_eval_model = Model(
side_eval_pipe,
hparams,
is_train=False,
#loss_mask=np.concatenate([np.zeros(50, dtype=np.float32), np.ones(10, dtype=np.float32)]),
seed=seed)
# print("2 side_eval_model -- 2")
# todo TRAIN = 0; EVAL_SIDE = 1; EVAL_FRWD = 2; EVAL_SIDE_EMA = 3; EVAL_FRWD_EMA = 4
eval_stages.append((Stage.EVAL_SIDE, side_eval_model))
if avg_sgd:
eval_stages.append((Stage.EVAL_SIDE_EMA, side_eval_model))
if forward_split:
# print("3 forward_split forward_eval_model")
# tf.reset_default_graph()
forward_eval_model = Model(forward_eval_pipe,
hparams,
is_train=False,
seed=seed)
# print("3 forward_split forward_eval_model -- 2")
eval_stages.append((Stage.EVAL_FRWD, forward_eval_model))
if avg_sgd:
eval_stages.append((Stage.EVAL_FRWD_EMA, forward_eval_model))
if write_summaries:
summ_path = f"{logdir}/{name}_{index}"
# print("write_summaries summ_path",summ_path)
if os.path.exists(summ_path):
shutil.rmtree(summ_path)
summ_writer = tf.summary.FileWriter(
summ_path) # , graph=tf.get_default_graph()
else:
summ_writer = None
if do_eval and forward_split:
stop_metric = lambda metrics: metrics[Stage.EVAL_FRWD]['SMAPE'
].avg_epoch
else:
stop_metric = None
# todo side_split: False; forward_split:False;
# summ_writer=<tensorflow.python.summary.writer.writer.FileWriter object at 0x7ff5dc176710>；
# eval_stages: []; stop_metric=None; patience=2; index=0
# print(f"side_split: {side_split}; forward_split:{forward_split}; summ_writer={summ_writer}；"
# f"eval_stages: {eval_stages}； stop_metric={stop_metric}; patience={patience}; index={index}")
return ModelTrainerV2(train_model,
eval_stages,
index,
patience=patience,
stop_metric=stop_metric,
summary_writer=summ_writer)
# todo n_models == 3
if n_models == 1:
with tf.device(f"/gpu:{gpu}"):
scope = tf.get_variable_scope()
all_models = [create_model(scope, 0, None, seed=seed)]
else:
for i in range(n_models):
device = f"/gpu:{i}" if multi_gpu else f"/gpu:{gpu}"
with tf.device(device):
prefix = f"m_{i}"
with tf.variable_scope(prefix) as scope:
all_models.append(
create_model(scope, i, prefix=prefix, seed=seed + i))
# todo inc_step = tf.assign_add(global_step, 1)
trainer = MultiModelTrainer(all_models, inc_step)
# return
# todo save_best_model or save_from_step: False 10500
# print("save_best_model or save_from_step: ", save_best_model, save_from_step)
if save_best_model or save_from_step:
saver_path = f'data/cpt/{name}'
# todo saver_path: data/cpt/s32
# print("saver_path: ",saver_path)
if os.path.exists(saver_path):
shutil.rmtree(saver_path)
os.makedirs(saver_path)
# todo max_to_keep 参数，这个是用来设置保存模型的个数，默认为5，即 max_to_keep=5，保存最近的5个模型
saver = tf.train.Saver(max_to_keep=10, name='train_saver')
else:
saver = None
# todo EMA decay for averaged SGD. Not use ASGD if not set
avg_sgd = asgd_decay is not None
# todo asgd_decay=0.99; avg_sgd=True
# print(f"asgd_decay={asgd_decay}; avg_sgd={avg_sgd}")
if avg_sgd:
from itertools import chain
def ema_vars(model):
ema = model.train_model.ema
# todo: average_name() methods give access to the shadow variables and their names
return {
ema.average_name(v): v
for v in model.train_model.ema._averages
}
ema_names = dict(
chain(*[ema_vars(model).items() for model in all_models]))
# todo ema_names=
# {'m_0/m_0/cudnn_gru/opaque_kernel/ExponentialMovingAverage': <tf.Variable 'm_0/cudnn_gru/opaque_kernel:0' shape=<unknown> dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d/kernel:0' shape=(7, 5, 16) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d/bias/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d/bias:0' shape=(16,) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_1/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_1/kernel:0' shape=(3, 16, 16) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_1/bias/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_1/bias:0' shape=(16,) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_2/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_2/kernel:0' shape=(3, 16, 32) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_2/bias/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_2/bias:0' shape=(32,) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_3/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_3/kernel:0' shape=(3, 32, 32) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_3/bias/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_3/bias:0' shape=(32,) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_4/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_4/kernel:0' shape=(3, 32, 64) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_4/bias/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_4/bias:0' shape=(64,) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_5/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_5/kernel:0' shape=(3, 64, 64) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/convnet/conv1d_5/bias/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/convnet/conv1d_5/bias:0' shape=(64,) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/fc_convnet/fc_encoder/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/fc_convnet/fc_encoder/kernel:0' shape=(2304, 512) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/fc_convnet/fc_encoder/bias/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/fc_convnet/fc_encoder/bias:0' shape=(512,) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/fc_convnet/out_encoder/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/fc_convnet/out_encoder/kernel:0' shape=(512, 16) dtype=float32_ref>,
# 'm_0/m_0/fingerpint/fc_convnet/out_encoder/bias/ExponentialMovingAverage': <tf.Variable 'm_0/fingerpint/fc_convnet/out_encoder/bias:0' shape=(16,) dtype=float32_ref>,
# 'm_0/m_0/attn_focus/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/attn_focus/kernel:0' shape=(16, 221) dtype=float32_ref>,
# 'm_0/m_0/attn_focus/bias/ExponentialMovingAverage': <tf.Variable 'm_0/attn_focus/bias:0' shape=(221,) dtype=float32_ref>,
# 'm_0/m_0/gru_cell/w_ru/ExponentialMovingAverage': <tf.Variable 'm_0/gru_cell/w_ru:0' shape=(291, 534) dtype=float32_ref>,
# 'm_0/m_0/gru_cell/b_ru/ExponentialMovingAverage': <tf.Variable 'm_0/gru_cell/b_ru:0' shape=(534,) dtype=float32_ref>,
# 'm_0/m_0/gru_cell/w_c/ExponentialMovingAverage': <tf.Variable 'm_0/gru_cell/w_c:0' shape=(291, 267) dtype=float32_ref>,
# 'm_0/m_0/gru_cell/b_c/ExponentialMovingAverage': <tf.Variable 'm_0/gru_cell/b_c:0' shape=(267,) dtype=float32_ref>,
# 'm_0/m_0/decoder_output_proj/kernel/ExponentialMovingAverage': <tf.Variable 'm_0/decoder_output_proj/kernel:0' shape=(267, 1) dtype=float32_ref>,
# 'm_0/m_0/decoder_output_proj/bias/ExponentialMovingAverage': <tf.Variable 'm_0/decoder_output_proj/bias:0' shape=(1,) dtype=float32_ref>,
# print(f"ema_names={ema_names}")
# todo chain=<itertools.chain object at 0x7fe6587cbf98>,
# [] = [dict_items([
# ('m_0/m_0/cudnn_gru/opaque_kernel/ExponentialMovingAverage', <tf.Variable 'm_0/cudnn_gru/opaque_kernel:0' shape=<unknown> dtype=float32_ref>),
# ...
# ('m_2/m_2/decoder_output_proj/bias/ExponentialMovingAverage', <tf.Variable 'm_2/decoder_output_proj/bias:0' shape=(1,) dtype=float32_ref>)
# ])]
# print(f"chain={chain(*[ema_vars(model).items() for model in all_models])},\n[] = {[ema_vars(model).items() for model in all_models]}")
#ema_names = all_models[0].train_model.ema.variables_to_restore()
ema_loader = tf.train.Saver(var_list=ema_names,
max_to_keep=1,
name='ema_loader')
ema_saver = tf.train.Saver(max_to_keep=1, name='ema_saver')
else:
ema_loader = None
init = tf.global_variables_initializer()
# print(f"forward_split={forward_split}; do_eval={do_eval}; side_split={side_split}")
if forward_split and do_eval:
eval_smape = trainer.metric(Stage.EVAL_FRWD, 'SMAPE')
eval_mae = trainer.metric(Stage.EVAL_FRWD, 'MAE')
else:
eval_smape = DummyMetric()
eval_mae = DummyMetric()
if side_split and do_eval:
eval_mae_side = trainer.metric(Stage.EVAL_SIDE, 'MAE')
eval_smape_side = trainer.metric(Stage.EVAL_SIDE, 'SMAPE')
else:
eval_mae_side = DummyMetric()
eval_smape_side = DummyMetric()
train_smape = trainer.metric(Stage.TRAIN, 'SMAPE')
train_mae = trainer.metric(Stage.TRAIN, 'MAE')
grad_norm = trainer.metric(Stage.TRAIN, 'GrNorm')
eval_stages = []
ema_eval_stages = []
if forward_split and do_eval:
eval_stages.append(Stage.EVAL_FRWD)
ema_eval_stages.append(Stage.EVAL_FRWD_EMA)
if side_split and do_eval:
eval_stages.append(Stage.EVAL_SIDE)
ema_eval_stages.append(Stage.EVAL_SIDE_EMA)
# todo eval_stages=[]; ema_eval_stages=[]
# print(f"eval_stages={eval_stages}; ema_eval_stages={ema_eval_stages}")
# gpu_options=tf.GPUOptions(allow_growth=False),
with tf.Session(
config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=tf.GPUOptions(
allow_growth=gpu_allow_growth))) as sess:
sess.run(init)
# pipe.load_vars(sess)
# todo 之前inp是加载了这个数据对象，restore是把数据tensor加载到sess中吧？
# 这里加载了数据在哪里用到了呢？
inp.restore(sess)
for model in all_models:
# todo 这里是什么意思呢？这样的到什么呢？运行了一下init_iterator？
# 上面建好模型结构之后，在哪里喂入数据呢？
model.init(sess)
# if beholder:
# visualizer = Beholder(session=sess, logdir=summ_path)
step = 0
prev_top = np.inf
best_smape = np.inf
# Contains best value (first item) and subsequent values
best_epoch_smape = []
for epoch in range(max_epoch):
# n_steps = pusher.n_pages // batch_size
if tqdm:
# todo Tqdm 是一个快速，可扩展的Python进度条，可以在 Python 长循环中添加一个进度提示信息，
# 用户只需要封装任意的迭代器 tqdm(iterator)。trange(i) 是 tqdm(range(i)) 的简单写法
# desc=进度条前面的描述；leave：保留进度条存在的痕迹，简单来说就是会把进度条的最终形态保留下来，默认为True
tqr = trange(steps_per_epoch,
desc="%2d" % (epoch + 1),
leave=False)
else:
tqr = range(steps_per_epoch)
for _ in tqr:
try:
# print("PRINT step = trainer.train_step")
# todo 训练模型只有这一行对吧
step = trainer.train_step(sess, epoch)
# if epoch == 0:
# print(f"step={step}, _={_}, epoch = {epoch}")
except tf.errors.OutOfRangeError:
break
# if beholder:
# if step % 5 == 0:
# noinspection PyUnboundLocalVariable
# visualizer.update()
# todo 应该是每训练一个epoch，会对其中的100（eval_pct）个batch的结果做一个评估；eval_every_step= 113
if step % eval_every_step == 0:
# todo eval_stages=[]；save_best_model=False; save_from_step=10500; avg_sgd=True; ema_eval_stages=[]
# print(f"eval_stages={eval_stages}；save_best_model={save_best_model}; save_from_step={save_from_step}; avg_sgd={avg_sgd}; ema_eval_stages={ema_eval_stages}")
if eval_stages:
trainer.eval_step(sess,
epoch,
step,
eval_batches,
stages=eval_stages)
if save_best_model and epoch > 0 and eval_smape.last < best_smape:
best_smape = eval_smape.last
saver.save(sess,
f'data/cpt/{name}/cpt',
global_step=step)
if save_from_step and step >= save_from_step:
saver.save(sess,
f'data/cpt/{name}/cpt',
global_step=step)
if avg_sgd and ema_eval_stages:
ema_saver.save(sess,
'data/cpt_tmp/ema',
write_meta_graph=False)
# restore ema-backed vars
ema_loader.restore(sess, 'data/cpt_tmp/ema')
trainer.eval_step(sess,
epoch,
step,
eval_batches,
stages=ema_eval_stages)
# restore normal vars
ema_saver.restore(sess, 'data/cpt_tmp/ema')
MAE = "%.3f/%.3f/%.3f" % (eval_mae.last, eval_mae_side.last,
train_mae.last)
improvement = '↑' if eval_smape.improved else ' '
SMAPE = "%s%.3f/%.3f/%.3f" % (improvement, eval_smape.last,
eval_smape_side.last,
train_smape.last)
if tqdm:
# todo .set_description("GEN %i"%i) #设置进度条左边显示的信息
# .set_postfix(loss=random(),gen=randint(1,999),str="h",lst=[1,2]) #设置进度条右边显示的信息
tqr.set_postfix(gr=grad_norm.last, MAE=MAE, SMAPE=SMAPE)
if not trainer.has_active() or (max_steps
and step > max_steps):
break
if tqdm:
tqr.close()
trainer.end_epoch()
if not best_epoch_smape or eval_smape.avg_epoch < best_epoch_smape[
0]:
best_epoch_smape = [eval_smape.avg_epoch]
else:
best_epoch_smape.append(eval_smape.avg_epoch)
current_top = eval_smape.top
if prev_top > current_top:
prev_top = current_top
has_best_indicator = '↑'
else:
has_best_indicator = ' '
status = "%2d: Best top SMAPE=%.3f%s (%s)" % (
epoch + 1, current_top, has_best_indicator, ",".join(
["%.3f" % m.top for m in eval_smape.metrics]))
if trainer.has_active():
status += ", frwd/side best MAE=%.3f/%.3f, SMAPE=%.3f/%.3f; avg MAE=%.3f/%.3f, SMAPE=%.3f/%.3f, %d am" % \
(eval_mae.best_epoch, eval_mae_side.best_epoch, eval_smape.best_epoch, eval_smape_side.best_epoch,
eval_mae.avg_epoch, eval_mae_side.avg_epoch, eval_smape.avg_epoch, eval_smape_side.avg_epoch,
trainer.has_active())
else:
print("Early stopping!", file=sys.stderr)
break
if max_steps and step > max_steps:
print("Max steps calculated", file=sys.stderr)
break
sys.stderr.flush()
# todo best_epoch_smape=[nan]; eval_smape.avg_epoch=nan; trainer.has_active()=3; prev_top=inf; current_top=nan
# print(f"best_epoch_smape={best_epoch_smape}; eval_smape.avg_epoch={eval_smape.avg_epoch}; "
# f"trainer.has_active()={trainer.has_active()}; prev_top={prev_top}; current_top={current_top}")
# noinspection PyUnboundLocalVariable
return np.mean(best_epoch_smape, dtype=np.float64)