def simple_fixed_prediction_estimator_extra_fields(export_path,
eval_export_path):
"""Exports a simple fixed prediction estimator that parses extra fields."""
def model_fn(features, labels, mode, params):
"""Model function for custom estimator."""
del params
predictions = features['prediction']
predictions_dict = {
prediction_keys.PredictionKeys.PREDICTIONS: predictions,
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions_dict,
export_outputs={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
tf.estimator.export.RegressionOutput(predictions)
})
loss = tf.losses.mean_squared_error(predictions, labels)
train_op = tf.assign_add(tf.train.get_global_step(), 1)
eval_metric_ops = {
metric_keys.MetricKeys.LOSS_MEAN: tf.metrics.mean(loss),
}
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
predictions=predictions_dict,
eval_metric_ops=eval_metric_ops)
def train_input_fn():
"""Train input function."""
return {
'prediction': tf.constant([[1.0], [2.0], [3.0], [4.0]]),
}, tf.constant([[1.0], [2.0], [3.0], [4.0]]),
feature_spec = {'prediction': tf.FixedLenFeature([1], dtype=tf.float32)}
eval_feature_spec = {
'prediction': tf.FixedLenFeature([1], dtype=tf.float32),
'label': tf.FixedLenFeature([1], dtype=tf.float32),
'fixed_float': tf.FixedLenFeature([1], dtype=tf.float32),
'fixed_string': tf.FixedLenFeature([1], dtype=tf.string),
'fixed_int': tf.FixedLenFeature([1], dtype=tf.int64),
'var_float': tf.VarLenFeature(dtype=tf.float32),
'var_string': tf.VarLenFeature(dtype=tf.string),
'var_int': tf.VarLenFeature(dtype=tf.int64),
}
estimator = tf.estimator.Estimator(model_fn=model_fn)
estimator.train(input_fn=train_input_fn, steps=1)
return util.export_model_and_eval_model(
estimator=estimator,
serving_input_receiver_fn=(
tf.estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec)),
eval_input_receiver_fn=export.build_parsing_eval_input_receiver_fn(
eval_feature_spec, label_key='label'),
export_path=export_path,
eval_export_path=eval_export_path)
def simple_fixed_prediction_estimator_no_labels(
export_path,
eval_export_path,
output_prediction_key=prediction_keys.PredictionKeys.PREDICTIONS):
"""Exports a simple fixed prediction estimator with no labels."""
def model_fn(features, mode, params):
"""Model function for custom estimator."""
del params
predictions = features['prediction']
if output_prediction_key is not None:
predictions_dict = {
output_prediction_key: predictions,
}
else:
# For simulating Estimators which don't return a predictions dict in
# EVAL mode.
predictions_dict = {}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions_dict,
export_outputs={
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
tf.estimator.export.RegressionOutput(predictions)
})
# We use create a nonsensical loss that is easy to compute:
# loss = mean(predictions^2) and export it as as the average loss for
# testing that the metrics are computed correctly.
loss = tf.compat.v1.losses.mean_squared_error(
predictions, tf.zeros_like(predictions))
train_op = tf.compat.v1.assign_add(
tf.compat.v1.train.get_global_step(), 1)
eval_metric_ops = {
metric_keys.MetricKeys.LOSS_MEAN: tf.compat.v1.metrics.mean(loss),
}
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
predictions=predictions_dict,
eval_metric_ops=eval_metric_ops)
def train_input_fn():
"""Train input function."""
return {
'prediction': tf.constant([[1.0], [2.0], [3.0], [4.0]]),
}
estimator = tf.estimator.Estimator(model_fn=model_fn)
estimator.train(input_fn=train_input_fn, steps=1)
feature_spec = {'prediction': tf.io.FixedLenFeature([1], dtype=tf.float32)}
eval_feature_spec = {
'prediction': tf.io.FixedLenFeature([1], dtype=tf.float32),
}
return util.export_model_and_eval_model(
estimator=estimator,
serving_input_receiver_fn=(
tf.estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec)),
eval_input_receiver_fn=export.build_parsing_eval_input_receiver_fn(
eval_feature_spec, label_key=None),
export_path=export_path,
eval_export_path=eval_export_path)
def get_simple_fixed_prediction_estimator_and_metadata(
output_prediction_key=prediction_keys.PredictionKeys.PREDICTIONS):
"""Returns a simple fixed prediction estimator and metadata.
Exposed for use with ExporterTest.
Args:
output_prediction_key: Output prediction key.
Returns:
Dictionary containing estimator, eval_input_receiver_fn,
serving_input_receiver_fn, train_input_fn and model_fn.
"""
def model_fn(features, labels, mode, params):
"""Model function for custom estimator."""
del params
predictions = features['prediction']
if output_prediction_key is not None:
predictions_dict = {
output_prediction_key: predictions,
}
else:
# For simulating Estimators which don't return a predictions dict in
# EVAL mode.
predictions_dict = {}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions_dict,
export_outputs={
tf.saved_model.signature_constants
.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
tf.estimator.export.RegressionOutput(predictions)
})
loss = tf.losses.mean_squared_error(predictions, labels)
train_op = tf.assign_add(tf.train.get_global_step(), 1)
eval_metric_ops = {
metric_keys.MetricKeys.LOSS_MEAN: tf.metrics.mean(loss),
}
return tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
train_op=train_op,
predictions=predictions_dict,
eval_metric_ops=eval_metric_ops)
def train_input_fn():
"""Train input function."""
return {
'prediction': tf.constant([[1.0], [2.0], [3.0], [4.0]]),
}, tf.constant([[1.0], [2.0], [3.0], [4.0]]),
estimator = tf.estimator.Estimator(model_fn=model_fn)
feature_spec = {'prediction': tf.FixedLenFeature([1], dtype=tf.float32)}
eval_feature_spec = {
'prediction': tf.FixedLenFeature([1], dtype=tf.float32),
'label': tf.FixedLenFeature([1], dtype=tf.float32),
}
return {
'estimator':
estimator,
'serving_input_receiver_fn': (
tf.estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec)),
'eval_input_receiver_fn':
export.build_parsing_eval_input_receiver_fn(
eval_feature_spec, label_key='label'),
'train_input_fn':
train_input_fn,
'model_fn':
model_fn,
}
def simple_fixed_prediction_classifier_extra_fields(export_path,
eval_export_path):
"""Exports a simple fixed prediction classifier that parses extra fields."""
def model_fn(features, labels, mode, params):
"""Model function for custom estimator."""
del labels
del params
classes = tf.sparse_tensor_to_dense(features['classes'],
default_value='?')
scores = tf.sparse_tensor_to_dense(features['scores'],
default_value=0.0)
predictions = {
prediction_keys.PredictionKeys.LOGITS: scores,
prediction_keys.PredictionKeys.PROBABILITIES: scores,
prediction_keys.PredictionKeys.CLASSES: classes,
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
tf.estimator.export.ClassificationOutput(scores=scores,
classes=classes),
})
# Note that this is always going to be 0.
loss = tf.losses.mean_squared_error(scores, tf.ones_like(scores))
train_op = tf.assign_add(tf.train.get_global_step(), 1)
eval_metric_ops = {
metric_keys.MetricKeys.LOSS_MEAN: tf.metrics.mean(loss),
}
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
predictions=predictions,
eval_metric_ops=eval_metric_ops)
def train_input_fn():
"""Train input function."""
classes = tf.SparseTensor(values=[
'first1',
'first2',
'second1',
'third1',
'third2',
'third3',
'fourth1',
],
indices=[[0, 0], [0, 1], [1, 0], [2, 0],
[2, 1], [2, 2], [3, 0]],
dense_shape=[4, 3])
scores = tf.SparseTensor(values=[0.9, 0.9, 0.9, 0.9, 0.8, 0.7, 0.9],
indices=[[0, 0], [0, 1], [1, 0], [2, 0],
[2, 1], [2, 2], [3, 0]],
dense_shape=[4, 3])
return {
'classes': classes,
'scores': scores,
}, classes
estimator = tf.estimator.Estimator(model_fn=model_fn)
estimator.train(input_fn=train_input_fn, steps=1)
serving_input_receiver_fn = (
tf.estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec={
'classes': tf.VarLenFeature(dtype=tf.string),
'scores': tf.VarLenFeature(dtype=tf.float32)
}))
eval_input_receiver_fn = export.build_parsing_eval_input_receiver_fn(
feature_spec={
'classes': tf.VarLenFeature(dtype=tf.string),
'scores': tf.VarLenFeature(dtype=tf.float32),
'labels': tf.VarLenFeature(dtype=tf.string),
'fixed_float': tf.FixedLenFeature([1], dtype=tf.float32),
'fixed_string': tf.FixedLenFeature([1], dtype=tf.string),
'fixed_int': tf.FixedLenFeature([1], dtype=tf.int64),
'var_float': tf.VarLenFeature(dtype=tf.float32),
'var_string': tf.VarLenFeature(dtype=tf.string),
'var_int': tf.VarLenFeature(dtype=tf.int64),
},
label_key='labels')
return util.export_model_and_eval_model(
estimator=estimator,
serving_input_receiver_fn=serving_input_receiver_fn,
eval_input_receiver_fn=eval_input_receiver_fn,
export_path=export_path,
eval_export_path=eval_export_path)