def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
if params.use_bert and params.bert_emb_path:
features = dataset.get_training_input_with_bert(
params.input + [params.bert_emb_path], params)
else:
features = dataset.get_training_input(params.input, params)
else:
features = record.get_input_features( # ???
os.path.join(params.record, "*train*"), "train", params)
# Build model
initializer = get_initializer(params)
model = model_cls(params)
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer), features, params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
# Create global step
global_step = tf.train.get_or_create_global_step()
# Print parameters
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(
v.shape.as_list())).tolist() # mutiple all dimension size
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
if params.update_cycle == 1:
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=loss,
global_step=global_step,
learning_rate=learning_rate,
clip_gradients=params.clip_grad_norm or None,
optimizer=opt,
colocate_gradients_with_ops=True)
zero_op = tf.no_op("zero_op")
collect_op = tf.no_op("collect_op")
else:
grads_and_vars = opt.compute_gradients(
loss, colocate_gradients_with_ops=True)
gradients = [item[0] for item in grads_and_vars]
variables = [item[1] for item in grads_and_vars]
variables = utils.replicate_variables(variables)
zero_op = utils.zero_variables(variables)
collect_op = utils.collect_gradients(gradients, variables)
scale = 1.0 / params.update_cycle
gradients, variables = utils.scale_gradients(grads_and_vars, scale)
# Gradient clipping avoid greadient explosion!!
if isinstance(params.clip_grad_norm or None, float):
gradients, _ = tf.clip_by_global_norm(gradients,
params.clip_grad_norm)
# Update variables
grads_and_vars = list(zip(gradients, variables))
with tf.control_dependencies([collect_op]):
train_op = opt.apply_gradients(grads_and_vars, global_step)
# Validation
'''
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = files
eval_input_fn = dataset.get_evaluation_input
else:
print("Don't evaluate")
eval_input_fn = None
'''
# Add hooks
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(
loss
), # Monitors the loss tensor and stops training if loss is NaN
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"chars": tf.shape(features["chars"]),
"source": tf.shape(features["source"]),
#"bert": tf.shape(features["bert"]),
"lr": learning_rate
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=tf.train.Saver(max_to_keep=params.keep_checkpoint_max,
sharded=False))
]
config = session_config(params)
'''
if not eval_input_fn is None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: search.create_inference_graph(
model.get_evaluation_func(), f, params
),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps
)
)
'''
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
while not sess.should_stop():
utils.session_run(sess, zero_op)
for i in range(1, params.update_cycle):
utils.session_run(sess, collect_op)
sess.run(train_op)
def main(args):
if args.distribute:
distribute.enable_distributed_training()
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
if distribute.rank() == 0:
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset.get_training_input(params.input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
# Build model
initializer = get_initializer(params)
regularizer = tf.contrib.layers.l1_l2_regularizer(
scale_l1=params.scale_l1, scale_l2=params.scale_l2)
model = model_cls(params)
# Create global step
global_step = tf.train.get_or_create_global_step()
dtype = tf.float16 if args.half else None
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer, regularizer, dtype), features,
params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
loss = loss + tf.losses.get_regularization_loss()
if distribute.rank() == 0:
print_variables()
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("loss", loss)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
opt = optimizers.MultiStepOptimizer(opt, params.update_cycle)
if args.half:
opt = optimizers.LossScalingOptimizer(opt, params.loss_scale)
# Optimization
grads_and_vars = opt.compute_gradients(
loss, colocate_gradients_with_ops=True)
if params.clip_grad_norm:
grads, var_list = list(zip(*grads_and_vars))
grads, _ = tf.clip_by_global_norm(grads, params.clip_grad_norm)
grads_and_vars = zip(grads, var_list)
train_op = opt.apply_gradients(grads_and_vars, global_step=global_step)
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.get_evaluation_input
else:
eval_input_fn = None
# Hooks
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"source": tf.shape(features["source"]),
"target": tf.shape(features["target"])
},
every_n_iter=1)
]
broadcast_hook = distribute.get_broadcast_hook()
if broadcast_hook:
train_hooks.append(broadcast_hook)
if distribute.rank() == 0:
# Add hooks
save_vars = tf.trainable_variables() + [global_step]
saver = tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
train_hooks.append(
hooks.MultiStepHook(tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=saver),
step=params.update_cycle))
if eval_input_fn is not None:
train_hooks.append(
hooks.MultiStepHook(hooks.EvaluationHook(
lambda f: inference.create_inference_graph([model], f,
params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
session_config(params),
device_list=params.device_list,
max_to_keep=params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps),
step=params.update_cycle))
checkpoint_dir = params.output
else:
checkpoint_dir = None
restore_op = restore_variables(args.checkpoint)
def restore_fn(step_context):
step_context.session.run(restore_op)
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_dir,
hooks=train_hooks,
save_checkpoint_secs=None,
config=session_config(params)) as sess:
# Restore pre-trained variables
sess.run_step_fn(restore_fn)
while not sess.should_stop():
sess.run(train_op)
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset.get_training_input(params.input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
update_cycle = params.update_cycle
features, init_op = cache.cache_features(features, update_cycle)
# Build model
initializer = get_initializer(params)
regularizer = tf.contrib.layers.l1_l2_regularizer(
scale_l1=params.scale_l1, scale_l2=params.scale_l2)
model = model_cls(params)
# Create global step
global_step = tf.train.get_or_create_global_step()
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer, regularizer), features,
params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
loss = loss + tf.losses.get_regularization_loss()
# Print parameters
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
loss, ops = optimize.create_train_op(loss, opt, global_step, params)
restore_op = restore_variables(args.checkpoint)
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.get_evaluation_input
else:
eval_input_fn = None
# Add hooks
save_vars = tf.trainable_variables() + [global_step]
saver = tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
multiplier = tf.convert_to_tensor([update_cycle, 1])
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"source": tf.shape(features["source"]) * multiplier,
"target": tf.shape(features["target"]) * multiplier
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=saver)
]
config = session_config(params)
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: inference.create_inference_graph([model], f,
params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
def restore_fn(step_context):
step_context.session.run(restore_op)
def step_fn(step_context):
# Bypass hook calls
step_context.session.run([init_op, ops["zero_op"]])
for i in range(update_cycle - 1):
step_context.session.run(ops["collect_op"])
return step_context.run_with_hooks(ops["train_op"])
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
# Restore pre-trained variables
sess.run_step_fn(restore_fn)
while not sess.should_stop():
sess.run_step_fn(step_fn)
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset.get_training_input(params.input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
# Build model
initializer = get_initializer(params)
model = model_cls(params)
if params.MRT:
assert params.batch_size == 1
features = mrt_utils.get_MRT(features, params, model)
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer), features, params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
# Create global step
global_step = tf.train.get_or_create_global_step()
# Print parameters
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=loss,
global_step=global_step,
learning_rate=learning_rate,
clip_gradients=params.clip_grad_norm or None,
optimizer=opt,
colocate_gradients_with_ops=True)
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.get_evaluation_input
else:
eval_input_fn = None
# Add hooks
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"source": tf.shape(features["source"]),
"target": tf.shape(features["target"])
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=tf.train.Saver(max_to_keep=params.keep_checkpoint_max,
sharded=False))
]
config = session_config(params)
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: search.create_inference_graph(
model.get_evaluation_func(), f, params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
while not sess.should_stop():
sess.run(train_op)
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
# Load configs
model_cls_list = [models.get_model(model) for model in args.models]
params_list = [default_parameters() for _ in range(len(model_cls_list))]
params_list = [
merge_parameters(params, model_cls.get_parameters())
for params, model_cls in zip(params_list, model_cls_list)
]
params_list = [
import_params(args.checkpoints[i], args.models[i], params_list[i])
for i in range(len(args.checkpoints))
]
params_list = [
override_parameters(params_list[i], args)
for i in range(len(model_cls_list))
]
# Build Graph
with tf.Graph().as_default():
model_var_lists = []
# Load checkpoints
for i, checkpoint in enumerate(args.checkpoints):
print("Loading %s" % checkpoint)
var_list = tf.train.list_variables(checkpoint)
values = {}
reader = tf.train.load_checkpoint(checkpoint)
for (name, shape) in var_list:
if not name.startswith(model_cls_list[i].get_name()):
continue
if name.find("losses_avg") >= 0:
continue
tensor = reader.get_tensor(name)
values[name] = tensor
model_var_lists.append(values)
# Build models
model_fns = []
for i in range(len(args.checkpoints)):
name = model_cls_list[i].get_name()
model = model_cls_list[i](params_list[i], name + "_%d" % i)
model_fn = model.get_relevance_func()
model_fns.append(model_fn)
params = params_list[0]
# Build input queue
features = dataset.get_training_input(args.input, params)
relevances = model_fns[0](features, params)
assign_ops = []
all_var_list = tf.trainable_variables()
for i in range(len(args.checkpoints)):
un_init_var_list = []
name = model_cls_list[i].get_name()
for v in all_var_list:
if v.name.startswith(name + "_%d" % i):
un_init_var_list.append(v)
ops = set_variables(un_init_var_list, model_var_lists[i],
name + "_%d" % i)
assign_ops.extend(ops)
assign_op = tf.group(*assign_ops)
sess_creator = tf.train.ChiefSessionCreator(
config=session_config(params))
results = []
num = 10
count = 0
hooks = [tf.train.LoggingTensorHook({}, every_n_iter=1)]
with tf.train.MonitoredSession(session_creator=sess_creator,
hooks=hooks) as sess:
# Restore variables
sess.run(assign_op)
src_seq, trg_seq, rlv_info, loss = sess.run(relevances)
start = time.time()
while count < num: #not sess.should_stop():
src_seq, trg_seq, rlv_info, loss = sess.run(relevances)
print('--result--')
print('loss:', loss)
for i in range(src_seq.shape[0]):
src = to_text(params.vocabulary["source"],
params.mapping["source"], src_seq[i], params)
trg = to_text(params.vocabulary["target"],
params.mapping["target"], trg_seq[i], params)
print('sentence %d' % i)
print('src:', src)
print('src_idx:', src_seq[i])
print('trg:', trg)
print('trg_idx:', trg_seq[i])
print('result:', rlv_info["result"][i])
count += 1
end = time.time()
print('total time:', end - start)
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
with tf.Graph().as_default():
features = dataset.get_training_input(params.input, params)
update_cycle = params.update_cycle
features, init_op = cache.cache_features(features, update_cycle)
initializer = get_initializer(params)
regularizer = tf.contrib.layers.l1_l2_regularizer(
scale_l1=params.scale_l1, scale_l2=params.scale_l2)
model = model_cls(params)
global_step = tf.train.get_or_create_global_step()
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer, regularizer), features,
params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
loss = loss + tf.losses.get_regularization_loss()
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "SGD":
opt = tf.train.GradientDescentOptimizer(learning_rate)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
loss, ops = optimize.create_train_op(loss, opt, global_step, params)
restore_op = restore_variables(args.checkpoint)
if params.validation:
eval_sorted_keys, eval_inputs = dataset.read_eval_input_file(
params.validation)
eval_input_fn = dataset.get_predict_input
else:
eval_input_fn = None
save_vars = tf.trainable_variables() + [global_step]
saver = tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
multiplier = tf.convert_to_tensor([update_cycle, 1])
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"text": tf.shape(features["text"]) * multiplier,
"aspect": tf.shape(features["aspect"]) * multiplier,
"polarity": tf.shape(features["polarity"]) * multiplier
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=saver)
]
config = session_config(params)
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: inference.create_predict_graph([model], f, params
),
lambda: eval_input_fn(eval_inputs, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
def restore_fn(step_context):
step_context.session.run(restore_op)
def step_fn(step_context):
step_context.session.run([init_op, ops["zero_op"]])
for i in range(update_cycle - 1):
step_context.session.run(ops["collect_op"])
return step_context.run_with_hooks(ops["train_op"])
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
sess.run_step_fn(restore_fn)
while not sess.should_stop():
sess.run_step_fn(step_fn)
