def main(unused_argv):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
hyper_params = create_hparams()
model_fn = model.create_model_fn(hyper_params,
model_impl=dual_encoder_model)
estimator = tf.contrib.learn.Estimator(
model_fn=model_fn,
model_dir=MODEL_DIR,
config=tf.contrib.learn.RunConfig(session_config=config))
input_fn_train = inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.TRAIN,
input_files=[TRAIN_FILE],
batch_size=hyper_params.batch_size,
num_epochs=FLAGS.num_epochs)
input_fn_eval = inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.EVAL,
input_files=[VALIDATION_FILE],
batch_size=hyper_params.eval_batch_size,
num_epochs=1)
eval_metrics = metrics.create_evaluation_metrics()
eval_monitor = tf.contrib.learn.monitors.ValidationMonitor(
input_fn=input_fn_eval,
every_n_steps=FLAGS.eval_every,
metrics=eval_metrics)
estimator.fit(input_fn=input_fn_train, steps=None, monitors=[eval_monitor])
def main(unused_argv):
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('label_map_path')
flags.mark_flag_as_required('train_file_pattern')
flags.mark_flag_as_required('eval_file_pattern')
config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir)
run_config = {"label_map_path": FLAGS.label_map_path,
"num_classes": FLAGS.num_classes}
if FLAGS.finetune_ckpt:
run_config["finetune_ckpt"] = FLAGS.finetune_ckpt
model_fn = create_model_fn(run_config)
estimator = tf.estimator.Estimator(model_fn=model_fn, config=config)
train_input_fn = create_input_fn(FLAGS.train_file_pattern, True, FLAGS.image_size, FLAGS.batch_size)
eval_input_fn = create_input_fn(FLAGS.eval_file_pattern, False, FLAGS.image_size)
prediction_fn = create_prediction_input_fn()
train_spec, eval_spec = create_train_and_eval_specs(train_input_fn, eval_input_fn, prediction_fn, FLAGS.num_train_steps)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def main(_):
"""Launch training"""
# Load hyperparameters
params = config.create_config()
# Prepare function that will be used for loading context/utterance
model_fn = model.create_model_fn(params, model_impl=selected_model)
# Prepare estimator
estimator = tf.contrib.learn.Estimator(model_fn=model_fn,
model_dir=MODEL_DIR,
config=tf.contrib.learn.RunConfig(
gpu_memory_fraction=0.25,
save_checkpoints_secs=60 * 2,
keep_checkpoint_max=1,
log_device_placement=False))
# Prepare input training examples
input_fn_train = inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.TRAIN,
input_files=[TRAIN_FILE],
batch_size=params.batch_size,
num_epochs=FLAGS.num_epochs,
params=params)
# Prepare input validation examples
input_fn_eval = inputs.create_input_fn(mode=tf.contrib.learn.ModeKeys.EVAL,
input_files=[VALIDATION_FILE],
batch_size=params.eval_batch_size,
num_epochs=1,
params=params)
# Load recall metrics for validation
eval_metrics = metrics.create_evaluation_metrics()
# Prepare monitor for validation
eval_monitor = tf.contrib.learn.monitors.ValidationMonitor(
input_fn=input_fn_eval,
every_n_steps=FLAGS.eval_every,
metrics=eval_metrics)
# Lauch training
estimator.fit(input_fn=input_fn_train, steps=None, monitors=[eval_monitor])
def main(unused_argv):
# Replace MODEL_DIR with the folder current run to resume training from a set of hyperparameters
# MODEL_DIR = '/Users/eduardolitonjua/Desktop/Retrieval-System/runs/1472130056'
hparams = hyperparameters.create_hparams()
model_fn = model.create_model_fn(
hparams,
model_impl=dual_encoder_model)
estimator = tf.contrib.learn.Estimator(
model_fn=model_fn,
model_dir=MODEL_DIR,
config=tf.contrib.learn.RunConfig())
input_fn_train = inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.TRAIN,
input_files=[TRAIN_FILE],
batch_size=hparams.batch_size,
num_epochs=FLAGS.num_epochs)
input_fn_eval = inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.EVAL,
input_files=[VALIDATION_FILE],
batch_size=hparams.eval_batch_size,
num_epochs=1)
eval_metrics = metrics.create_evaluation_metrics()
class EvaluationMonitor(tf.contrib.learn.monitors.EveryN):
def every_n_step_end(self, step, outputs):
self._estimator.evaluate(
input_fn=input_fn_eval,
metrics=eval_metrics,
steps=None)
eval_monitor = EvaluationMonitor(every_n_steps=FLAGS.eval_every, first_n_steps=-1)
estimator.fit(input_fn=input_fn_train, steps=None, monitors=[eval_monitor])
tf.flags.DEFINE_string("test_file", "./data/test.tfrecords",
"Path of test data in TFRecords format")
tf.flags.DEFINE_string("model_dir", None,
"Directory to load model checkpoints from")
tf.flags.DEFINE_integer("loglevel", 20, "Tensorflow log level")
tf.flags.DEFINE_integer("test_batch_size", 16, "Batch size for testing")
FLAGS = tf.flags.FLAGS
if not FLAGS.model_dir:
print("You must specify a model directory")
sys.exit(1)
tf.logging.set_verbosity(FLAGS.loglevel)
if __name__ == "__main__":
hparams = hparams.create_hparams()
model_fn = model.create_model_fn(hparams, model_impl=dual_encoder_model)
estimator = tf.contrib.learn.Estimator(model_fn=model_fn,
model_dir=FLAGS.model_dir,
config=tf.contrib.learn.RunConfig())
input_fn_test = inputs.create_input_fn(mode=tf.contrib.learn.ModeKeys.EVAL,
input_files=[FLAGS.test_file],
batch_size=FLAGS.test_batch_size,
num_epochs=1)
eval_metrics = metrics.create_evaluation_metrics()
estimator.evaluate(input_fn=input_fn_test,
steps=None,
metrics=eval_metrics)
def main(unused_argv):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
hyper_params = create_hparams()
print("\n\nModel hyperparameters", hyper_params)
model_fn = model.create_model_fn(hyper_params,
model_impl=dual_encoder_model)
estimator = tf.contrib.learn.Estimator(
model_fn=model_fn,
model_dir=MODEL_DIR,
config=tf.contrib.learn.RunConfig(session_config=config))
# Training mode
if not FLAGS.infer_mode:
input_fn_train = inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.TRAIN,
input_files=[TRAIN_FILE],
batch_size=hyper_params.batch_size,
num_epochs=FLAGS.num_epochs,
has_dssm=hyper_params.dssm,
has_lcs=hyper_params.lcs,
)
input_fn_eval = inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.EVAL,
input_files=[VALIDATION_FILE],
batch_size=hyper_params.eval_batch_size,
num_epochs=1,
has_dssm=hyper_params.dssm,
has_lcs=hyper_params.lcs,
)
eval_metrics = metrics.create_evaluation_metrics()
eval_monitor = tf.contrib.learn.monitors.ValidationMonitor(
input_fn=input_fn_eval,
every_n_steps=FLAGS.eval_every,
metrics=eval_metrics,
)
estimator.fit(input_fn=input_fn_train,
steps=None,
monitors=[eval_monitor])
# Testing mode
else:
input_fn_infer = inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.INFER,
input_files=[TEST_FILE],
batch_size=hyper_params.eval_batch_size,
num_epochs=1,
has_dssm=hyper_params.dssm,
has_lcs=hyper_params.lcs,
randomize=False)
preds = estimator.predict(input_fn=input_fn_infer)
i = 0
with open(FLAGS.test_out, 'w') as f:
for pred in preds:
i += 1
output_string = ",".join([("%.15f" % indi) for indi in pred])
f.write(output_string + "\n")
print(i)