def experiment_fn(run_config, params):
conversation = Conversation()
estimator = tf.estimator.Estimator(model_fn=conversation.model_fn,
model_dir=Config.train.model_dir,
params=params,
config=run_config)
vocab = data_loader.load_vocab("vocab")
Config.data.vocab_size = len(vocab)
train_X, test_X, train_y, test_y = data_loader.make_train_and_test_set()
train_input_fn, train_input_hook = data_loader.make_batch(
(train_X, train_y), batch_size=Config.model.batch_size)
test_input_fn, test_input_hook = data_loader.make_batch(
(test_X, test_y), batch_size=Config.model.batch_size, scope="test")
experiment = tf.contrib.learn.Experiment(
estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=test_input_fn,
train_steps=Config.train.train_steps,
min_eval_frequency=Config.train.min_eval_frequency,
train_monitors=[
train_input_hook,
hook.print_variables(
variables=['train/enc_0', 'train/dec_0', 'train/pred_0'],
vocab=vocab,
every_n_iter=Config.train.check_hook_n_iter)
],
eval_hooks=[test_input_hook],
eval_delay_secs=0)
return experiment
def main():
params = tf.contrib.training.HParams(**Config.model.to_dict())
run_config = tf.estimator.RunConfig(
model_dir=Config.train.model_dir,
save_checkpoints_steps=Config.train.save_checkpoints_steps,
)
tf_config = os.environ.get('TF_CONFIG', '{}')
tf_config_json = json.loads(tf_config)
cluster = tf_config_json.get('cluster')
job_name = tf_config_json.get('task', {}).get('type')
task_index = tf_config_json.get('task', {}).get('index')
cluster_spec = tf.train.ClusterSpec(cluster)
server = tf.train.Server(cluster_spec,
job_name=job_name,
task_index=task_index)
if job_name == "ps":
tf.logging.info("Started server!")
server.join()
if job_name == "worker":
with tf.Session(server.target):
with tf.device(
tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % task_index,
cluster=cluster)):
tf.logging.info("Initializing Estimator")
conversation = Conversation()
estimator = tf.estimator.Estimator(
model_fn=conversation.model_fn,
model_dir=Config.train.model_dir,
params=params,
config=run_config)
tf.logging.info("Initializing vocabulary")
vocab = data_loader.load_vocab("vocab")
Config.data.vocab_size = len(vocab)
train_X, test_X, train_y, test_y = data_loader.make_train_and_test_set(
)
train_input_fn, train_input_hook = data_loader.make_batch(
(train_X, train_y), batch_size=Config.model.batch_size)
test_input_fn, test_input_hook = data_loader.make_batch(
(test_X, test_y),
batch_size=Config.model.batch_size,
scope="test")
tf.logging.info("Initializing Specifications")
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,
max_steps=1000)
eval_spec = tf.estimator.EvalSpec(input_fn=test_input_fn)
tf.logging.info("Run training")
tf.estimator.train_and_evaluate(estimator, train_spec,
eval_spec)
def experiment_fn(run_config, params):
# 先定义estimator
conversation = Conversation()
estimator = tf.estimator.Estimator(model_fn=conversation.model_fn,
model_dir=Config.train.model_dir,
params=params,
config=run_config)
# 返回字典
vocab = data_loader.load_vocab("vocab")
Config.data.vocab_size = len(vocab)
# 定义训练数据
train_X, test_X, train_y, test_y = data_loader.make_train_and_test_set()
train_input_fn, train_input_hook = data_loader.make_batch(
(train_X, train_y), batch_size=Config.model.batch_size)
test_input_fn, test_input_hook = data_loader.make_batch(
(test_X, test_y), batch_size=Config.model.batch_size, scope="test")
train_hooks = [train_input_hook]
if Config.train.print_verbose:
train_hooks.append(
hook.print_variables(
variables=['train/enc_0', 'train/dec_0', 'train/pred_0'],
rev_vocab=utils.get_rev_vocab(vocab),
every_n_iter=Config.train.check_hook_n_iter))
if Config.train.debug:
train_hooks.append(tf_debug.LocalCLIDebugHook())
eval_hooks = [test_input_hook]
if Config.train.debug:
eval_hooks.append(tf_debug.LocalCLIDebugHook())
# 定义实验
experiment = tf.contrib.learn.Experiment(
estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=test_input_fn,
train_steps=Config.train.train_steps,
min_eval_frequency=Config.train.min_eval_frequency,
train_monitors=train_hooks,
eval_hooks=eval_hooks,
eval_delay_secs=0)
return experiment
def experiment_fn(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)
vocab = data_loader.load_vocab("vocab")
Config.data.vocab_size = len(vocab)
train_data, test_data = data_loader.make_train_and_test_set()
train_input_fn, train_input_hook = data_loader.make_batch(train_data,
batch_size=Config.model.batch_size,
scope="train")
test_input_fn, test_input_hook = data_loader.make_batch(test_data,
batch_size=Config.model.batch_size,
scope="test")
train_hooks = [train_input_hook]
if Config.train.print_verbose:
train_hooks.append(
hook.print_variables(variables=['train/input_0'],
rev_vocab=get_rev_vocab(vocab),
every_n_iter=Config.train.check_hook_n_iter))
train_hooks.append(
hook.print_target(variables=['train/target_0', 'train/pred_0'],
every_n_iter=Config.train.check_hook_n_iter))
if Config.train.debug:
train_hooks.append(tf_debug.LocalCLIDebugHook())
eval_hooks = [test_input_hook]
if Config.train.debug:
eval_hooks.append(tf_debug.LocalCLIDebugHook())
experiment = tf.contrib.learn.Experiment(estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=test_input_fn,
train_steps=Config.train.train_steps,
min_eval_frequency=Config.train.min_eval_frequency,
train_monitors=train_hooks,
eval_hooks=eval_hooks)
return experiment
def experiment_fn(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)
train_data, test_data = data_loader.make_train_and_test_set()
train_input_fn, train_input_hook = data_loader.make_batch(
train_data, batch_size=Config.model.batch_size, scope="train")
test_input_fn, test_input_hook = data_loader.make_batch(
test_data, batch_size=Config.model.batch_size, scope="test")
train_hooks = [train_input_hook]
if Config.train.debug:
train_hooks.append(tf_debug.LocalCLIDebugHook())
if Config.train.print_verbose:
train_hooks.append(
tf.train.LoggingTensorHook(
["loss/reconstruction_error", "loss/kl_divergence"],
every_n_iter=Config.train.check_hook_n_iter))
eval_hooks = [test_input_hook]
if Config.train.debug:
eval_hooks.append(tf_debug.LocalCLIDebugHook())
experiment = tf.contrib.learn.Experiment(
estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=test_input_fn,
train_steps=Config.train.train_steps,
min_eval_frequency=Config.train.min_eval_frequency,
train_monitors=train_hooks,
eval_hooks=eval_hooks)
return experiment
