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This CL allows model_to_estimator to export signatures for multi-head Keras, or customize signatures for single-head Keras.

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PiperOrigin-RevId: 329517928
Change-Id: I89b16614d1fd47b67122026aafb9155dab25cb9e
This commit is contained in:
Zhenyu Tan 2020-09-01 09:06:24 -07:00 committed by TensorFlower Gardener
parent 6e195c5d4e
commit 138133264c
3 changed files with 97 additions and 6 deletions
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99
tensorflow/python/keras/estimator/__init__.py
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@ -38,7 +38,9 @@ def model_to_estimator(
custom_objects=None,
model_dir=None,
config=None,
checkpoint_format='saver'):
checkpoint_format='saver',
metric_names_map=None,
export_outputs=None):
"""Constructs an `Estimator` instance from given keras model.
If you use infrastructure or other tooling that relies on Estimators, you can
@ -71,6 +73,28 @@ def model_to_estimator(
estimator.train(input_fn, steps=1)
```
Example with customized export signature:
```python
inputs = {'a': tf.keras.Input(..., name='a'),
'b': tf.keras.Input(..., name='b')}
outputs = {'c': tf.keras.layers.Dense(..., name='c')(inputs['a']),
'd': tf.keras.layers.Dense(..., name='d')(inputs['b'])}
keras_model = tf.keras.Model(inputs, outputs)
keras_model.compile(...)
export_outputs = {'c': tf.estimator.export.RegressionOutput,
'd': tf.estimator.export.ClassificationOutput}
estimator = tf.keras.estimator.model_to_estimator(
keras_model, export_outputs=export_outputs)
def input_fn():
return dataset_ops.Dataset.from_tensors(
({'features': features, 'sample_weights': sample_weights},
targets))
estimator.train(input_fn, steps=1)
```
Args:
keras_model: A compiled Keras model object. This argument is mutually
exclusive with `keras_model_path`. Estimator's `model_fn` uses the
@ -100,6 +124,32 @@ def model_to_estimator(
`tf.train.Checkpoint`. Currently, saving object-based checkpoints from
`model_to_estimator` is only supported by Functional and Sequential
models. Defaults to 'saver'.
metric_names_map: Optional dictionary mapping Keras model output metric
names to custom names. This can be used to override the default Keras
model output metrics names in a multi IO model use case and provide custom
names for the `eval_metric_ops` in Estimator.
The Keras model metric names can be obtained using `model.metrics_names`
excluding any loss metrics such as total loss and output losses.
For example, if your Keras model has two outputs `out_1` and `out_2`,
with `mse` loss and `acc` metric, then `model.metrics_names` will be
`['loss', 'out_1_loss', 'out_2_loss', 'out_1_acc', 'out_2_acc']`.
The model metric names excluding the loss metrics will be
`['out_1_acc', 'out_2_acc']`.
export_outputs: Optional dictionary. This can be used to override the
default Keras model output exports in a multi IO model use case and
provide custom names for the `export_outputs` in
`tf.estimator.EstimatorSpec`. Default is None, which is equivalent to
{'serving_default': `tf.estimator.export.PredictOutput`}. If not None,
the keys must match the keys of `model.output_names`.
A dict `{name: output}` where:
* name: An arbitrary name for this output.
* output: an `ExportOutput` class such as `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`. Single-headed models only need
to specify one entry in this dictionary. Multi-headed models should
specify one entry for each head, one of which must be named using
`tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`
If no entry is provided, a default `PredictOutput` mapping to
`predictions` will be created.
Returns:
An Estimator from given keras model.
@ -126,7 +176,9 @@ def model_to_estimator(
model_dir=model_dir,
config=config,
checkpoint_format=checkpoint_format,
use_v2_estimator=False)
use_v2_estimator=False,
metric_names_map=metric_names_map,
export_outputs=export_outputs)
@keras_export('keras.estimator.model_to_estimator', v1=[])
@ -136,7 +188,8 @@ def model_to_estimator_v2(keras_model=None,
model_dir=None,
config=None,
checkpoint_format='checkpoint',
metric_names_map=None):
metric_names_map=None,
export_outputs=None):
"""Constructs an `Estimator` instance from given keras model.
If you use infrastructure or other tooling that relies on Estimators, you can
@ -169,6 +222,28 @@ def model_to_estimator_v2(keras_model=None,
estimator.train(input_fn, steps=1)
```
Example with customized export signature:
```python
inputs = {'a': tf.keras.Input(..., name='a'),
'b': tf.keras.Input(..., name='b')}
outputs = {'c': tf.keras.layers.Dense(..., name='c')(inputs['a']),
'd': tf.keras.layers.Dense(..., name='d')(inputs['b'])}
keras_model = tf.keras.Model(inputs, outputs)
keras_model.compile(...)
export_outputs = {'c': tf.estimator.export.RegressionOutput,
'd': tf.estimator.export.ClassificationOutput}
estimator = tf.keras.estimator.model_to_estimator(
keras_model, export_outputs=export_outputs)
def input_fn():
return dataset_ops.Dataset.from_tensors(
({'features': features, 'sample_weights': sample_weights},
targets))
estimator.train(input_fn, steps=1)
```
Note: We do not support creating weighted metrics in Keras and converting them
to weighted metrics in the Estimator API using `model_to_estimator`.
You will have to create these metrics directly on the estimator spec using the
@ -248,6 +323,21 @@ def model_to_estimator_v2(keras_model=None,
`['loss', 'out_1_loss', 'out_2_loss', 'out_1_acc', 'out_2_acc']`.
The model metric names excluding the loss metrics will be
`['out_1_acc', 'out_2_acc']`.
export_outputs: Optional dictionary. This can be used to override the
default Keras model output exports in a multi IO model use case and
provide custom names for the `export_outputs` in
`tf.estimator.EstimatorSpec`. Default is None, which is equivalent to
{'serving_default': `tf.estimator.export.PredictOutput`}. If not None,
the keys must match the keys of `model.output_names`.
A dict `{name: output}` where:
* name: An arbitrary name for this output.
* output: an `ExportOutput` class such as `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`. Single-headed models only need
to specify one entry in this dictionary. Multi-headed models should
specify one entry for each head, one of which must be named using
`tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`
If no entry is provided, a default `PredictOutput` mapping to
`predictions` will be created.
Returns:
An Estimator from given keras model.
@ -275,5 +365,6 @@ def model_to_estimator_v2(keras_model=None,
config=config,
checkpoint_format=checkpoint_format,
use_v2_estimator=True,
metric_names_map=metric_names_map)
metric_names_map=metric_names_map,
export_outputs=export_outputs)
# LINT.ThenChange(//tensorflow_estimator/python/estimator/keras.py)

2
tensorflow/tools/api/golden/v1/tensorflow.keras.estimator.pbtxt
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@ -2,6 +2,6 @@ path: "tensorflow.keras.estimator"
tf_module {
member_method {
name: "model_to_estimator"
argspec: "args=[\'keras_model\', \'keras_model_path\', \'custom_objects\', \'model_dir\', \'config\', \'checkpoint_format\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'saver\'], "
argspec: "args=[\'keras_model\', \'keras_model_path\', \'custom_objects\', \'model_dir\', \'config\', \'checkpoint_format\', \'metric_names_map\', \'export_outputs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'saver\', \'None\', \'None\'], "
}
}

2
tensorflow/tools/api/golden/v2/tensorflow.keras.estimator.pbtxt
View File

@ -2,6 +2,6 @@ path: "tensorflow.keras.estimator"
tf_module {
member_method {
name: "model_to_estimator"
argspec: "args=[\'keras_model\', \'keras_model_path\', \'custom_objects\', \'model_dir\', \'config\', \'checkpoint_format\', \'metric_names_map\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'checkpoint\', \'None\'], "
argspec: "args=[\'keras_model\', \'keras_model_path\', \'custom_objects\', \'model_dir\', \'config\', \'checkpoint_format\', \'metric_names_map\', \'export_outputs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'checkpoint\', \'None\', \'None\'], "
}
}