def _test_logits(
self, mode, hidden_units, logits_dimension, inputs, expected_logits):
"""Tests that the expected logits are passed to mock head."""
with ops.Graph().as_default():
training_util.create_global_step()
head = _mock_head(
self,
hidden_units=hidden_units,
logits_dimension=logits_dimension,
expected_logits=expected_logits)
estimator_spec = dnn._dnn_model_fn(
features={'age': constant_op.constant(inputs)},
labels=constant_op.constant([[1]]),
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=[
feature_column.numeric_column('age',
shape=np.array(inputs).shape[1:])],
optimizer=_mock_optimizer(self, hidden_units))
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
if mode == model_fn.ModeKeys.TRAIN:
sess.run(estimator_spec.train_op)
elif mode == model_fn.ModeKeys.EVAL:
sess.run(estimator_spec.loss)
elif mode == model_fn.ModeKeys.PREDICT:
sess.run(estimator_spec.predictions)
else:
self.fail('Invalid mode: {}'.format(mode))
def _test_logits(self, mode, hidden_units, logits_dimension, inputs,
expected_logits):
"""Tests that the expected logits are passed to mock head."""
with ops.Graph().as_default():
training_util.create_global_step()
head = _mock_head(self,
hidden_units=hidden_units,
logits_dimension=logits_dimension,
expected_logits=expected_logits)
estimator_spec = dnn._dnn_model_fn(
features={'age': constant_op.constant(inputs)},
labels=constant_op.constant([[1]]),
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=[
feature_column.numeric_column(
'age', shape=np.array(inputs).shape[1:])
],
optimizer=_mock_optimizer(self, hidden_units))
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
if mode == model_fn.ModeKeys.TRAIN:
sess.run(estimator_spec.train_op)
elif mode == model_fn.ModeKeys.EVAL:
sess.run(estimator_spec.loss)
elif mode == model_fn.ModeKeys.PREDICT:
sess.run(estimator_spec.predictions)
else:
self.fail('Invalid mode: {}'.format(mode))
def test_multi_feature_column_multi_dim_logits(self):
"""Tests multiple feature columns and multi-dimensional logits.
All numbers are the same as test_multi_dim_input_multi_dim_logits. The only
difference is that the input consists of two 1D feature columns, instead of
one 2D feature column.
"""
base_global_step = 100
_create_checkpoint((
([[.6, .5], [-.6, -.5]], [.1, -.1]),
([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),
), base_global_step, self._model_dir)
hidden_units = (2, 2)
logits_dimension = 3
inputs = ([[10.]], [[8.]])
expected_logits = [[-0.48, 0.48, 0.39]]
for mode in [
model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL,
model_fn.ModeKeys.PREDICT
]:
with ops.Graph().as_default():
training_util.create_global_step()
head = _mock_head(self,
hidden_units=hidden_units,
logits_dimension=logits_dimension,
expected_logits=expected_logits)
estimator_spec = dnn._dnn_model_fn(
features={
'age': constant_op.constant(inputs[0]),
'height': constant_op.constant(inputs[1])
},
labels=constant_op.constant([[1]]),
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=[
feature_column.numeric_column('age'),
feature_column.numeric_column('height')
],
optimizer=_mock_optimizer(self, hidden_units))
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
if mode == model_fn.ModeKeys.TRAIN:
sess.run(estimator_spec.train_op)
elif mode == model_fn.ModeKeys.EVAL:
sess.run(estimator_spec.loss)
elif mode == model_fn.ModeKeys.PREDICT:
sess.run(estimator_spec.predictions)
else:
self.fail('Invalid mode: {}'.format(mode))
def _model_fn(features, labels, mode, config):
return dnn_lib._dnn_model_fn( # pylint: disable=protected-access
features=features,
labels=labels,
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=tuple(feature_columns or []),
optimizer=optimizer,
activation_fn=activation_fn,
dropout=dropout,
input_layer_partitioner=input_layer_partitioner,
config=config)
def _model_fn(features, labels, mode, config):
return dnn_lib._dnn_model_fn( # pylint: disable=protected-access
features=features,
labels=labels,
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=tuple(feature_columns or []),
optimizer=optimizer,
activation_fn=activation_fn,
dropout=dropout,
input_layer_partitioner=input_layer_partitioner,
config=config)
def test_multi_feature_column_multi_dim_logits(self):
"""Tests multiple feature columns and multi-dimensional logits.
All numbers are the same as test_multi_dim_input_multi_dim_logits. The only
difference is that the input consists of two 1D feature columns, instead of
one 2D feature column.
"""
base_global_step = 100
_create_checkpoint((
([[.6, .5], [-.6, -.5]], [.1, -.1]),
([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),
), base_global_step, self._model_dir)
hidden_units = (2, 2)
logits_dimension = 3
inputs = ([[10.]], [[8.]])
expected_logits = [[-0.48, 0.48, 0.39]]
for mode in [model_fn.ModeKeys.TRAIN,
model_fn.ModeKeys.EVAL,
model_fn.ModeKeys.PREDICT]:
with ops.Graph().as_default():
training_util.create_global_step()
head = _mock_head(
self,
hidden_units=hidden_units,
logits_dimension=logits_dimension,
expected_logits=expected_logits)
estimator_spec = dnn._dnn_model_fn(
features={'age': constant_op.constant(inputs[0]),
'height': constant_op.constant(inputs[1])},
labels=constant_op.constant([[1]]),
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=[
feature_column.numeric_column('age'),
feature_column.numeric_column('height')],
optimizer=_mock_optimizer(self, hidden_units))
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
if mode == model_fn.ModeKeys.TRAIN:
sess.run(estimator_spec.train_op)
elif mode == model_fn.ModeKeys.EVAL:
sess.run(estimator_spec.loss)
elif mode == model_fn.ModeKeys.PREDICT:
sess.run(estimator_spec.predictions)
else:
self.fail('Invalid mode: {}'.format(mode))
def _model_fn(features, labels, mode, params):
"""Build TF graph based on canned DNN classifier."""
key_column = features.pop(constants.KEY_COLUMN, None)
if key_column is None:
raise ValueError('Key is missing from features.')
spec = _dnn_model_fn(features=features,
labels=labels,
mode=mode,
**params)
predictions = spec.predictions
if predictions:
predictions[
constants.KEY_COLUMN] = tf.convert_to_tensor_or_sparse_tensor(
key_column)
spec = spec._replace(predictions=predictions)
spec = spec._replace(
export_outputs={
'classes': tf.estimator.export.PredictOutput(predictions)
})
return spec