def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (
models.get_model_config(FLAGS.model, FLAGS.config_name)
if FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
# Create the estimator.
estimator = estimator_util.create_estimator(
model_class, config.hparams, model_dir=FLAGS.model_dir)
# Read and process the input features.
features = _process_tce(config.inputs.features)
# Create an input function.
def input_fn():
return tf.data.Dataset.from_tensors({"time_series_features": features})
# Generate the predictions.
for predictions in estimator.predict(input_fn):
assert len(predictions) == 1
print("Prediction:", predictions[0])
def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (
models.get_model_config(FLAGS.model, FLAGS.config_name)
if FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
# Create the estimator.
estimator = estimator_util.create_estimator(
model_class, config.hparams, model_dir=FLAGS.model_dir)
# Create an input function that reads the evaluation dataset.
input_fn = estimator_util.create_input_fn(
file_pattern=FLAGS.eval_files,
input_config=config.inputs,
mode=tf.estimator.ModeKeys.EVAL)
# Run evaluation. This will log the result to stderr and also write a summary
# file in the model_dir.
estimator_util.evaluate(estimator, input_fn, eval_name=FLAGS.eval_name)
def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (models.get_model_config(FLAGS.model, FLAGS.config_name) if
FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
# Create the estimator.
estimator = estimator_util.create_estimator(model_class,
config.hparams,
model_dir=FLAGS.model_dir)
# Read and process the input features.
features = _process_tce(config.inputs.features)
print(type(features))
print(features)
# Create an input function.
def input_fn():
return {
"time_series_features":
tf.estimator.inputs.numpy_input_fn(features,
batch_size=1,
shuffle=False,
queue_capacity=1)()
}
# Generate the predictions.
for predictions in estimator.predict(input_fn):
assert len(predictions) == 1
print("Prediction:", predictions[0])
def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (
models.get_model_config(FLAGS.model, FLAGS.config_name)
if FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
# Create the estimator.
estimator = estimator_util.create_estimator(
model_class, config.hparams, model_dir=FLAGS.model_dir)
# Read and process the input features.
features = _process_tce(config.inputs.features)
# Create an input function.
def input_fn():
return {
"time_series_features":
tf.estimator.inputs.numpy_input_fn(
features, batch_size=1, shuffle=False, queue_capacity=1)()
}
# Generate the predictions.
for predictions in estimator.predict(input_fn):
assert len(predictions) == 1
print("Prediction:", predictions[0])
def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (
models.get_model_config(FLAGS.model, FLAGS.config_name)
if FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
# Create the estimator.
estimator = estimator_util.create_estimator(
model_class, config.hparams, model_dir=FLAGS.model_dir)
# Print no. of trainable parameters to console.
var_names = [v for v in estimator.get_variable_names()]
n_params = np.sum([len(estimator.get_variable_value(v).flatten()) for v in var_names])
print("Trainable parameters in model:", int(n_params))
# Create an input function that reads the evaluation dataset.
input_fn = estimator_util.create_input_fn(
file_pattern=FLAGS.eval_files,
input_config=config.inputs,
mode=tf.estimator.ModeKeys.EVAL)
# Run evaluation. This will log the result to stderr and also write a summary
# file in the model_dir.
estimator_util.evaluate(estimator, input_fn, eval_name=FLAGS.eval_name)
def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (
models.get_model_config(FLAGS.model, FLAGS.config_name)
if FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
config_util.log_and_save_config(config, FLAGS.model_dir)
# Create the estimator.
run_config = tf.estimator.RunConfig(keep_checkpoint_max=1)
estimator = estimator_util.create_estimator(model_class, config.hparams,
run_config, FLAGS.model_dir)
# Create an input function that reads the training dataset. We iterate through
# the dataset once at a time if we are alternating with evaluation, otherwise
# we iterate infinitely.
train_input_fn = estimator_util.create_input_fn(
file_pattern=FLAGS.train_files,
input_config=config.inputs,
mode=tf.estimator.ModeKeys.TRAIN,
shuffle_values_buffer=FLAGS.shuffle_buffer_size,
repeat=1 if FLAGS.eval_files else None)
if not FLAGS.eval_files:
estimator.train(train_input_fn, max_steps=FLAGS.train_steps)
else:
eval_input_fn = estimator_util.create_input_fn(
file_pattern=FLAGS.eval_files,
input_config=config.inputs,
mode=tf.estimator.ModeKeys.EVAL)
eval_args = {
"val": (eval_input_fn, None) # eval_name: (input_fn, eval_steps)
}
for _ in estimator_runner.continuous_train_and_eval(
estimator=estimator,
train_input_fn=train_input_fn,
eval_args=eval_args,
train_steps=FLAGS.train_steps):
# continuous_train_and_eval() yields evaluation metrics after each
# training epoch. We don't do anything here.
pass
def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (models.get_model_config(FLAGS.model, FLAGS.config_name) if
FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
# Create the estimator.
estimator = estimator_util.create_estimator(model_class,
config.hparams,
model_dir=FLAGS.model_dir)
# Read and process the input features.
tce_table = pd.read_csv(FLAGS.input_tce_csv_file,
header=0,
usecols=[0, 1, 2, 3, 7, 8, 9, 10, 11, 12, 13, 18],
dtype={
'Sectors': int,
'camera': int,
'ccd': int
})
for ind, tce in tce_table.iterrows():
features = _process_tce(config.inputs.features, tce)
# Create an input function.
def input_fn():
return {
"time_series_features":
tf.compat.v1.estimator.inputs.numpy_input_fn(
features, batch_size=1, shuffle=False, queue_capacity=1)()
}
# Generate the predictions.
for predictions in estimator.predict(input_fn):
assert len(predictions) == 1
print(tce.tic_id, "Prediction:", predictions[0])
print(str(tce.tic_id) + ' ' + str(predictions[0]),
file=open(FLAGS.output_file, 'a'))
def create_estimator(model_dir, model_class, config):
return estimator_util.create_estimator(model_class,
config.hparams,
model_dir=model_dir)
