Exemplos de create_evaluation_metrics em Python

Exemplo n.º 1

def main(unused_argv):
    hparams = udc_hparams.create_hparams()

    model_fn = udc_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 = udc_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 = udc_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 = udc_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,
        early_stopping_metric="recall_at_1",
        early_stopping_metric_minimize=False,
        early_stopping_rounds=4000)

    estimator.fit(input_fn=input_fn_train, steps=None, monitors=[eval_monitor])

Exemplo n.º 2

Arquivo: udc_train.py Projeto: jt17383/chatbot-retrieval

def main(unused_argv):
    hparams = udc_hparams.create_hparams()

    model_fn = udc_model.create_model_fn(hparams,
                                         model_impl=dual_encoder_model)

    estimator = tf.contrib.learn.Estimator(model_fn=model_fn,
                                           model_dir=MODEL_DIR)

    input_fn_train = udc_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 = udc_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 = udc_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)

    dbg_hook = tfdbg.LocalCLIDebugHook()
    estimator.fit(input_fn=input_fn_train,
                  steps=FLAGS.num_steps,
                  monitors=[eval_monitor])

Exemplo n.º 3

def main(unused_argv):
    hparams = udc_hparams.create_hparams()
    writer = tf.summary.FileWriter(FLAGS.logdir)

    model_fn = udc_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 = udc_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 = udc_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 = udc_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])

Exemplo n.º 4

Arquivo: udc_train.py Projeto: iliemihai/Tensorflow_Stanford

def main(unused_argv):
    hparams = udc_hparams.create_hparams()

    model_fn = udc_model.create_model_fn(hparams,
                                         model_impl=dual_encoder_model)

    estimator = tf.estimator.Estimator(model_fn=model_fn,
                                       model_dir=MODEL_DIR,
                                       config=tf.estimator.RunConfig())

    input_fn_train = udc_inputs.create_input_fn(
        mode=tf.estimator.ModeKeys.TRAIN,
        input_files=[TRAIN_FILE],
        batch_size=hparams.batch_size,
        num_epochs=FLAGS.num_epochs)

    input_fn_eval = udc_inputs.create_input_fn(
        mode=tf.estimator.ModeKeys.EVAL,
        input_files=[VALIDATION_FILE],
        batch_size=hparams.eval_batch_size,
        num_epochs=1)

    eval_metrics = udc_metrics.create_evaluation_metrics()

    estimator.train(input_fn=input_fn_train, steps=FLAGS.num_epochs)

Exemplo n.º 5

Arquivo: udc_train.py Projeto: dandancat123/bilibili_notes

def main(unused_argv):
  hparams = udc_hparams.create_hparams()

  model_fn = udc_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 = udc_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 = udc_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 = udc_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])

Exemplo n.º 6

Arquivo: udc_train.py Projeto: BinbinBian/chatbot-retrieval

def main(unused_argv):
  hparams = udc_hparams.create_hparams()

  model_fn = udc_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 = udc_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 = udc_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 = udc_metrics.create_evaluation_metrics()

  # We need to subclass theis manually for now. The next TF version will
  # have support ValidationMonitors with metrics built-in.
  # It's already on the master branch.
  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)
  estimator.fit(input_fn=input_fn_train, steps=None, monitors=[eval_monitor])

Exemplo n.º 7

Arquivo: udc_train.py Projeto: rtygbwwwerr/chatbot

def main(unused_argv):
    hparams = udc_hparams.create_hparams()

    model_fn = udc_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 = udc_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 = udc_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 = udc_metrics.create_evaluation_metrics()

    # We need to subclass theis manually for now. The next TF version will
    # have support ValidationMonitors with metrics built-in.
    # It's already on the master branch.
    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])

Exemplo n.º 8

Arquivo: udc_test.py Projeto: BinbinBian/chatbot-retrieval

from models.dual_encoder import dual_encoder_model

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 = udc_hparams.create_hparams()
  model_fn = udc_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 = udc_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 = udc_metrics.create_evaluation_metrics()
  estimator.evaluate(input_fn=input_fn_test, steps=None, metrics=eval_metrics)

Exemplo n.º 9

Arquivo: udc_test.py Projeto: wcLegend/persona.chatbot

tf.flags.DEFINE_string("test_file", "./data/persona/test.tfrecords", "Path of test data in TFRecords format")
tf.flags.DEFINE_string("model_dir", "./runs/1542774662", "Directory to load model checkpoints from")
tf.flags.DEFINE_integer("loglevel", 20, "Tensorflow log level")
tf.flags.DEFINE_integer("test_batch_size", 8, "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 = udc_hparams.create_hparams()
  model_fn = udc_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 = udc_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 = udc_metrics.create_evaluation_metrics()
  estimator.evaluate(input_fn=input_fn_test, steps=None, metrics=eval_metrics)