def _experiment_fn(run_config, hparams):
# num_epochs can control duration if train_steps isn't
# passed to Experiment
train_input = lambda: model.generate_input_fn(
hparams.train_files,
num_epochs=hparams.num_epochs,
batch_size=hparams.train_batch_size,
)
# Don't shuffle evaluation data
eval_input = lambda: model.generate_input_fn(hparams.eval_files,
batch_size=hparams.
eval_batch_size,
shuffle=False)
return tf.contrib.learn.Experiment(
model.build_estimator(
embedding_size=hparams.embedding_size,
# Construct layers sizes with exponetial decay
hidden_units=[
max(
2,
int(hparams.first_layer_size *
hparams.scale_factor**i))
for i in range(hparams.num_layers)
],
config=run_config),
train_input_fn=train_input,
eval_input_fn=eval_input,
**experiment_args)
def _experiment_fn(output_dir):
# num_epochs can control duration if train_steps isn't
# passed to Experiment
train_input = model.generate_input_fn(
train_files,
num_epochs=num_epochs,
batch_size=train_batch_size,
)
# Don't shuffle evaluation data
eval_input = model.generate_input_fn(eval_files,
batch_size=eval_batch_size,
shuffle=False)
return tf.contrib.learn.Experiment(
model.build_estimator(
output_dir,
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(first_layer_size * scale_factor**i))
for i in range(num_layers)
]),
train_input_fn=train_input,
eval_input_fn=eval_input,
# export strategies control the prediction graph structure
# of exported binaries.
export_strategies=[
saved_model_export_utils.make_export_strategy(
model.serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1)
],
**experiment_args)
def run_experiment(hparams):
# Create training and eval input functions
train_input = lambda: model.generate_input_fn(
args.train_files,
num_epochs=hparams.num_epochs,
batch_size=hparams.train_batch_size,
)
# Don't shuffle evaluation data
eval_input = lambda: model.generate_input_fn(
args.eval_files, batch_size=hparams.eval_batch_size, shuffle=False)
# Define TrainSpec and EvalSpec instances
# Define exporters for Eval Spec
exporters = []
exporters.append(
tf.estimator.FinalExporter('mnist', model.example_serving_input_fn))
eval_spec = tf.estimator.EvalSpec(eval_input,
steps=hparams.eval_steps,
exporters=exporters,
throttle_secs=60)
train_spec = tf.estimator.TrainSpec(train_input,
max_steps=hparams.max_steps)
# Create estimator
estimator = tf.estimator.Estimator(model.generate_model_fn(hparams),
config=tf.estimator.RunConfig(
model_dir=hparams.job_dir, ))
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def _experiment_fn(run_config, hparams):
# num_epochs can control duration if train_steps isn't
# passed to Experiment
train_input = lambda: model.generate_input_fn(
hparams.train_files,
num_epochs=hparams.num_epochs,
batch_size=hparams.train_batch_size,
)
# Don't shuffle evaluation data
eval_input = lambda: model.generate_input_fn(
hparams.eval_files,
batch_size=hparams.eval_batch_size,
shuffle=False
)
return tf.contrib.learn.Experiment(
tf.estimator.Estimator(
model.generate_model_fn(
embedding_size=hparams.embedding_size,
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(hparams.first_layer_size *
hparams.scale_factor**i))
for i in range(hparams.num_layers)
],
learning_rate=hparams.learning_rate
),
config=run_config
),
train_input_fn=train_input,
eval_input_fn=eval_input,
**experiment_args
)
def _experiment_fn(run_config, hparams):
# num_epochs can control duration if train_steps isn't
# passed to Experiment
train_input = lambda: model.generate_input_fn()
# Don't shuffle evaluation data
eval_input = lambda: model.generate_input_fn(shuffle=False)
return tf.contrib.learn.Experiment(
model.build_estimator(config=run_config),
train_input_fn=train_input,
eval_input_fn=eval_input,
**experiment_args)
def _experiment_fn(output_dir):
train_input = model.generate_input_fn(train_file,
num_epochs=num_epochs,
batch_size=train_batch_size)
eval_input = model.generate_input_fn(eval_file,
batch_size=eval_batch_size)
return Experiment(model.build_estimator(job_dir,
embedding_size=embedding_size,
hidden_units=hidden_units),
train_input_fn=train_input,
eval_input_fn=eval_input,
export_strategies=[
saved_model_export_utils.make_export_strategy(
model.serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1)
],
**experiment_args)
def _experiment_fn(run_config, hparams):
"""Definicion de experimento"""
# Funcion de entrada de entrenamiento
train_input = lambda: model.generate_input_fn(
hparams.train_files,
num_epochs=hparams.num_epochs,
batch_size=hparams.train_batch_size,
)
# Funcion de entrada de evaluacion
eval_input = lambda: model.generate_input_fn(
hparams.eval_files,
batch_size=hparams.eval_batch_size
)
# Experimento
return tf.contrib.learn.Experiment(
model.build_estimator(hparams.job_dir),
train_input_fn=train_input,
eval_input_fn=eval_input,
**experiment_args
)
def _experiment_fn(run_config, hparams):
# num_epochs can control duration if train_steps isn't
# passed to Experiment
train_input = lambda: model.generate_input_fn(
[
os.path.join(os.environ['PIPELINE_INPUT_PATH'], train_file)
for train_file in hparams.train_files
],
num_epochs=hparams.num_epochs,
batch_size=hparams.train_batch_size,
)
# Don't shuffle evaluation data
eval_input = lambda: model.generate_input_fn([
os.path.join(os.environ['PIPELINE_INPUT_PATH'], eval_file)
for eval_file in hparams.eval_files
],
batch_size=hparams.
eval_batch_size,
shuffle=False)
return tf.contrib.learn.Experiment(
tf.estimator.Estimator(
model.generate_model_fn(
embedding_size=hparams.embedding_size,
# Construct layers sizes with exponential decay
hidden_units=[
max(
2,
int(hparams.first_layer_size *
hparams.scale_factor**i))
for i in range(hparams.num_layers)
],
learning_rate=hparams.learning_rate),
config=run_config),
train_input_fn=train_input,
eval_input_fn=eval_input,
# export_fn
# checkpoint_and_export=True,
**experiment_args)
def _experiment_fn(output_dir):
train_input = model.generate_input_fn(train_file,
num_epochs=num_epochs,
batch_size=train_batch_size)
eval_input = model.generate_input_fn(eval_file,
batch_size=eval_batch_size)
return learn.Experiment(
model.build_estimator(job_dir,
embedding_size=embedding_size,
hidden_units=hidden_units),
train_input_fn=train_input,
eval_input_fn=eval_input,
eval_metrics={
'training/hptuning/metric':
learn.MetricSpec(metric_fn=metrics.streaming_accuracy,
prediction_key='logits')
},
export_strategies=[
saved_model_export_utils.make_export_strategy(
model.serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1)
],
**experiment_args)
