def testExportOutputsNoDict(self):
with ops.Graph().as_default(), self.test_session():
predictions = {'loss': constant_op.constant(1.)}
classes = constant_op.constant('hello')
with self.assertRaisesRegexp(TypeError,
'export_outputs must be dict'):
model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs=export.ClassificationOutput(
classes=classes))
def _model_fn_for_export_tests(features, labels, mode):
_, _ = features, labels
variables.Variable(1., name='weight')
scores = constant_op.constant([3.])
classes = constant_op.constant(['wumpus'])
return model_fn_lib.EstimatorSpec(
mode,
predictions=constant_op.constant(10.),
loss=constant_op.constant(1.),
train_op=constant_op.constant(2.),
export_outputs={
'test': export.ClassificationOutput(scores, classes)})
def _model_fn_scaffold(features, labels, mode):
_, _ = features, labels
variables.Variable(1., name='weight')
real_saver = saver.Saver()
self.mock_saver = test.mock.Mock(
wraps=real_saver, saver_def=real_saver.saver_def)
scores = constant_op.constant([3.])
return model_fn_lib.EstimatorSpec(
mode=mode,
predictions=constant_op.constant([[1.]]),
loss=constant_op.constant(0.),
train_op=constant_op.constant(0.),
scaffold=training.Scaffold(saver=self.mock_saver),
export_outputs={'test': export.ClassificationOutput(scores)})
def testAllArgumentsSet(self):
"""Tests that no errors are raised when all arguments are set."""
with ops.Graph().as_default(), self.test_session():
loss = constant_op.constant(1.)
predictions = {'loss': loss}
classes = constant_op.constant('hello')
model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.TRAIN,
predictions=predictions,
loss=loss,
train_op=control_flow_ops.no_op(),
eval_metric_ops={'loss': (control_flow_ops.no_op(), loss)},
export_outputs={
'head_name': export.ClassificationOutput(classes=classes)
},
training_chief_hooks=[_FakeHook()],
training_hooks=[_FakeHook()],
scaffold=monitored_session.Scaffold())
def _model_fn_scaffold(features, labels, mode):
_, _ = features, labels
my_int = variables.Variable(1, name='my_int',
collections=[ops.GraphKeys.LOCAL_VARIABLES])
scores = constant_op.constant([3.])
with ops.control_dependencies(
[variables.local_variables_initializer(),
data_flow_ops.tables_initializer()]):
assign_op = state_ops.assign(my_int, 12345)
# local_initSop must be an Operation, not a Tensor.
custom_local_init_op = control_flow_ops.group(assign_op)
return model_fn_lib.EstimatorSpec(
mode=mode,
predictions=constant_op.constant([[1.]]),
loss=constant_op.constant(0.),
train_op=constant_op.constant(0.),
scaffold=training.Scaffold(local_init_op=custom_local_init_op),
export_outputs={'test': export.ClassificationOutput(scores)})
def testExportOutputsMultiheadWithDefault(self):
with ops.Graph().as_default(), self.test_session():
predictions = {'loss': constant_op.constant(1.)}
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(['2'])
output_3 = constant_op.constant(['3'])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export.RegressionOutput(value=output_1),
'head-2':
export.ClassificationOutput(classes=output_2),
'head-3':
export.PredictOutput(outputs={'some_output_3': output_3})
}
estimator_spec = model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs=export_outputs)
self.assertEqual(export_outputs, estimator_spec.export_outputs)
def testExportOutputsMultiheadMissingDefault(self):
with ops.Graph().as_default(), self.test_session():
predictions = {'loss': constant_op.constant(1.)}
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(['2'])
output_3 = constant_op.constant(['3'])
export_outputs = {
'head-1': export.RegressionOutput(value=output_1),
'head-2': export.ClassificationOutput(classes=output_2),
'head-3':
export.PredictOutput(outputs={'some_output_3': output_3})
}
with self.assertRaisesRegexp(
ValueError,
'Multiple export_outputs were provided, but none of them is '
'specified as the default. Do this by naming one of them with '
'signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY.'):
model_fn.EstimatorSpec(mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs=export_outputs)