def test_loss_reduction_must_be_same(self):
"""Tests the loss reduction must be the same for different heads."""
head1 = multi_label_head.MultiLabelHead(
n_classes=2,
name='head1',
loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE)
head2 = multi_label_head.MultiLabelHead(
n_classes=3,
name='head2',
loss_reduction=losses_utils.ReductionV2.AUTO)
multi_head = multi_head_lib.MultiHead([head1, head2])
logits = {
'head1':
np.array([[-10., 10.], [-15., 10.]], dtype=np.float32),
'head2':
np.array([[20., -20., 20.], [-30., 20., -20.]], dtype=np.float32),
}
labels = {
'head1': np.array([[1, 0], [1, 1]], dtype=np.int64),
'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64),
}
with self.assertRaisesRegexp(ValueError, 'must be the same'):
multi_head.create_estimator_spec(
features={'x': np.array(((42, ), ), dtype=np.int32)},
mode=ModeKeys.TRAIN,
logits=logits,
labels=labels)
def test_train_loss_logits_tensor_wrong_shape(self):
"""Tests loss with a logits Tensor of the wrong shape."""
weights1 = np.array([[1.], [2.]], dtype=np.float32)
weights2 = np.array([[2.], [3.]])
head1 = multi_label_head.MultiLabelHead(n_classes=2,
name='head1',
weight_column='weights1')
head2 = multi_label_head.MultiLabelHead(n_classes=3,
name='head2',
weight_column='weights2')
multi_head = multi_head_lib.MultiHead([head1, head2],
head_weights=[1., 2.])
# logits tensor is 2x6 instead of 2x5
logits = np.array([[-10., 10., 20., -20., 20., 70.],
[-15., 10., -30., 20., -20., 80.]],
dtype=np.float32)
labels = {
'head1': np.array([[1, 0], [1, 1]], dtype=np.int64),
'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64),
}
with self.assertRaisesRegexp(ValueError, r'Could not split logits'):
multi_head.loss(features={
'x': np.array(((42, ), ), dtype=np.int32),
'weights1': weights1,
'weights2': weights2
},
mode=ModeKeys.TRAIN,
logits=logits,
labels=labels)
def test_head_weights_wrong_size(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
head2 = multi_label_head.MultiLabelHead(n_classes=3, name='head2')
with self.assertRaisesRegexp(
ValueError,
r'heads and head_weights must have the same size\. '
r'Given len\(heads\): 2. Given len\(head_weights\): 1\.'):
multi_head_lib.MultiHead([head1, head2], head_weights=[1.])
def test_train_create_loss_two_heads_with_weights(self):
# Use different example weighting for each head weighting.
weights1 = np.array([[1.], [2.]], dtype=np.float32)
weights2 = np.array([[2.], [3.]])
head1 = multi_label_head.MultiLabelHead(n_classes=2,
name='head1',
weight_column='weights1')
head2 = multi_label_head.MultiLabelHead(n_classes=3,
name='head2',
weight_column='weights2')
multi_head = multi_head_lib.MultiHead([head1, head2],
head_weights=[1., 2.])
logits = {
'head1':
np.array([[-10., 10.], [-15., 10.]], dtype=np.float32),
'head2':
np.array([[20., -20., 20.], [-30., 20., -20.]], dtype=np.float32),
}
labels = {
'head1': np.array([[1, 0], [1, 1]], dtype=np.int64),
'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64),
}
training_loss = multi_head.loss(logits=logits,
labels=labels,
features={
'x':
np.array(((42, ), ),
dtype=np.int32),
'weights1':
weights1,
'weights2':
weights2
},
mode=model_fn.ModeKeys.TRAIN)
tol = 1e-3
# loss of the first head is [[(10 + 10) / 2], [(15 + 0) / 2]]
# = [10, 7.5]
# training_loss = (1 * 10 + 2 * 7.5) / 2 = 12.5
# head-weighted unreduced_loss = 1 * [10, 7.5]
# loss of the second head is [[(20 + 20 + 20) / 3], [(30 + 0 + 0) / 3]]
# = [20, 10]
# training_loss = (2 * 20 + 3 * 10) / 2 = 35
# head-weighted unreduced_loss = 2 * [20, 10]
# head-weighted training_loss = 1 * 12.5 + 2 * 35 = 82.5
self.assertAllClose(82.5,
self.evaluate(training_loss),
rtol=tol,
atol=tol)
def test_train_one_head_with_optimizer(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
multi_head = multi_head_lib.MultiHead([head1])
logits = {
'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32)
}
labels = {'head1': np.array([[1, 0], [1, 1]], dtype=np.int64)}
features = {'x': np.array(((42, ), ), dtype=np.int32)}
# For large logits, sigmoid cross entropy loss is approximated as:
# loss = labels * (logits < 0) * (-logits) +
# (1 - labels) * (logits > 0) * logits =>
# expected_unweighted_loss = [[10., 10.], [15., 0.]]
# loss = ((10 + 10) / 2 + (15 + 0) / 2) / 2 = 8.75
expected_loss = 8.75
tol = 1e-3
loss = multi_head.loss(logits=logits,
labels=labels,
features=features,
mode=ModeKeys.TRAIN)
self.assertAllClose(expected_loss,
self.evaluate(loss),
rtol=tol,
atol=tol)
if tf.executing_eagerly():
return
expected_train_result = 'my_train_op'
class _Optimizer(optimizer_v2.OptimizerV2):
def get_updates(self, loss, params):
del params
return [
tf.strings.join([
tf.constant(expected_train_result),
tf.strings.as_string(loss, precision=3)
])
]
def get_config(self):
config = super(_Optimizer, self).get_config()
return config
spec = multi_head.create_estimator_spec(
features=features,
mode=ModeKeys.TRAIN,
logits=logits,
labels=labels,
optimizer=_Optimizer('my_optimizer'),
trainable_variables=[
tf.Variable([1.0, 2.0], dtype=tf.dtypes.float32)
])
with self.cached_session() as sess:
test_lib._initialize_variables(self, spec.scaffold)
loss, train_result = sess.run((spec.loss, spec.train_op))
self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol)
self.assertEqual(
six.b('{0:s}{1:.3f}'.format(expected_train_result,
expected_loss)), train_result)
def test_train_one_head(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
multi_head = multi_head_lib.MultiHead([head1])
logits = {'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32)}
labels = {'head1': np.array([[1, 0], [1, 1]], dtype=np.int64)}
features = {'x': np.array(((42,),), dtype=np.int32)}
# For large logits, sigmoid cross entropy loss is approximated as:
# loss = labels * (logits < 0) * (-logits) +
# (1 - labels) * (logits > 0) * logits =>
# expected_unweighted_loss = [[10., 10.], [15., 0.]]
# loss = ((10 + 10) / 2 + (15 + 0) / 2) / 2 = 8.75
expected_loss = 8.75
tol = 1e-3
loss = multi_head.loss(
logits=logits,
labels=labels,
features=features,
mode=model_fn.ModeKeys.TRAIN)
self.assertAllClose(expected_loss, self.evaluate(loss), rtol=tol, atol=tol)
if context.executing_eagerly():
return
expected_train_result = 'my_train_op'
def _train_op_fn(loss):
return string_ops.string_join(
[constant_op.constant(expected_train_result),
string_ops.as_string(loss, precision=3)])
spec = multi_head.create_estimator_spec(
features=features,
mode=model_fn.ModeKeys.TRAIN,
logits=logits,
labels=labels,
train_op_fn=_train_op_fn)
self.assertIsNotNone(spec.loss)
self.assertEqual({}, spec.eval_metric_ops)
self.assertIsNotNone(spec.train_op)
self.assertIsNone(spec.export_outputs)
test_lib._assert_no_hooks(self, spec)
# Assert predictions, loss, train_op, and summaries.
with self.cached_session() as sess:
test_lib._initialize_variables(self, spec.scaffold)
self.assertIsNotNone(spec.scaffold.summary_op)
loss, train_result, summary_str = sess.run((spec.loss, spec.train_op,
spec.scaffold.summary_op))
self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol)
self.assertEqual(
six.b('{0:s}{1:.3f}'.format(expected_train_result, expected_loss)),
train_result)
test_lib._assert_simple_summaries(self, {
metric_keys.MetricKeys.LOSS: expected_loss,
metric_keys.MetricKeys.LOSS + '/head1': expected_loss,
}, summary_str, tol)
def test_train_one_head_with_optimizer(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
multi_head = multi_head_lib.MultiHead([head1])
logits = {
'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32)
}
labels = {'head1': np.array([[1, 0], [1, 1]], dtype=np.int64)}
features = {'x': np.array(((42, ), ), dtype=np.int32)}
# For large logits, sigmoid cross entropy loss is approximated as:
# loss = labels * (logits < 0) * (-logits) +
# (1 - labels) * (logits > 0) * logits =>
# expected_unweighted_loss = [[10., 10.], [15., 0.]]
# loss = ((10 + 10) / 2 + (15 + 0) / 2) / 2 = 8.75
expected_loss = 8.75
tol = 1e-3
loss = multi_head.loss(logits=logits,
labels=labels,
features=features,
mode=model_fn.ModeKeys.TRAIN)
self.assertAllClose(expected_loss,
self.evaluate(loss),
rtol=tol,
atol=tol)
if context.executing_eagerly():
return
expected_train_result = 'my_train_op'
class _Optimizer(object):
def minimize(self, loss, global_step):
del global_step
return string_ops.string_join([
constant_op.constant(expected_train_result),
string_ops.as_string(loss, precision=3)
])
spec = multi_head.create_estimator_spec(features=features,
mode=model_fn.ModeKeys.TRAIN,
logits=logits,
labels=labels,
optimizer=_Optimizer())
with self.cached_session() as sess:
test_lib._initialize_variables(self, spec.scaffold)
loss, train_result = sess.run((spec.loss, spec.train_op))
self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol)
self.assertEqual(
six.b('{0:s}{1:.3f}'.format(expected_train_result,
expected_loss)), train_result)
def test_train_create_loss_one_head(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
multi_head = multi_head_lib.MultiHead([head1])
logits = {'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32)}
labels = {'head1': np.array([[1, 0], [1, 1]], dtype=np.int64)}
loss = multi_head.loss(
logits=logits,
labels=labels,
features={'x': np.array(((42,),), dtype=np.int32)},
mode=model_fn.ModeKeys.TRAIN)
tol = 1e-3
# Unreduced loss of the head is [[(10 + 10) / 2], (15 + 0) / 2]
# (averaged over classes, averaged over examples).
# loss = sum(unreduced_loss) / 2 = sum([10, 7.5]) / 2 = 8.75
self.assertAllClose(8.75, self.evaluate(loss), rtol=tol, atol=tol)
def test_predict_two_heads_logits_dict(self):
"""Tests predict with logits as dict."""
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
head2 = multi_label_head.MultiLabelHead(n_classes=3, name='head2')
multi_head = multi_head_lib.MultiHead([head1, head2])
logits = {
'head1': np.array([[-1., 1.], [-1.5, 1.]], dtype=np.float32),
'head2': np.array([[2., -2., 2.], [-3., 2., -2.]], dtype=np.float32)
}
expected_probabilities = {
'head1': nn.sigmoid(logits['head1']),
'head2': nn.sigmoid(logits['head2']),
}
pred_keys = prediction_keys.PredictionKeys
predictions = multi_head.predictions(logits)
self.assertAllClose(
logits['head1'],
self.evaluate(predictions[('head1', pred_keys.LOGITS)]))
self.assertAllClose(
logits['head2'],
self.evaluate(predictions[('head2', pred_keys.LOGITS)]))
self.assertAllClose(
expected_probabilities['head1'],
self.evaluate(predictions[('head1', pred_keys.PROBABILITIES)]))
self.assertAllClose(
expected_probabilities['head2'],
self.evaluate(predictions[('head2', pred_keys.PROBABILITIES)]))
if context.executing_eagerly():
return
spec = multi_head.create_estimator_spec(
features={'x': np.array(((42,),), dtype=np.int32)},
mode=model_fn.ModeKeys.PREDICT,
logits=logits)
self.assertItemsEqual(
(test_lib._DEFAULT_SERVING_KEY, 'predict', 'head1',
'head1/classification', 'head1/predict', 'head2',
'head2/classification', 'head2/predict'), spec.export_outputs.keys())
# Assert predictions and export_outputs.
with self.cached_session() as sess:
test_lib._initialize_variables(self, spec.scaffold)
self.assertIsNone(spec.scaffold.summary_op)
predictions = sess.run(spec.predictions)
self.assertAllClose(
logits['head1'],
predictions[('head1', pred_keys.LOGITS)])
self.assertAllClose(
logits['head2'],
predictions[('head2', pred_keys.LOGITS)])
self.assertAllClose(
expected_probabilities['head1'],
predictions[('head1', pred_keys.PROBABILITIES)])
self.assertAllClose(
expected_probabilities['head2'],
predictions[('head2', pred_keys.PROBABILITIES)])
self.assertAllClose(
expected_probabilities['head1'],
sess.run(spec.export_outputs[test_lib._DEFAULT_SERVING_KEY].scores))
self.assertAllClose(
expected_probabilities['head1'],
sess.run(spec.export_outputs['head1'].scores))
self.assertAllClose(
expected_probabilities['head2'],
sess.run(spec.export_outputs['head2'].scores))
self.assertAllClose(
expected_probabilities['head1'],
sess.run(
spec.export_outputs['predict'].outputs['head1/probabilities']))
self.assertAllClose(
expected_probabilities['head2'],
sess.run(
spec.export_outputs['predict'].outputs['head2/probabilities']))
self.assertAllClose(
expected_probabilities['head1'],
sess.run(
spec.export_outputs['head1/predict'].outputs['probabilities']))
self.assertAllClose(
expected_probabilities['head2'],
sess.run(
spec.export_outputs['head2/predict'].outputs['probabilities']))
def test_train_with_regularization_losses(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
head2 = multi_label_head.MultiLabelHead(n_classes=3, name='head2')
multi_head = multi_head_lib.MultiHead(
[head1, head2], head_weights=[1., 2.])
logits = {
'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32),
'head2': np.array([[20., -20., 20.], [-30., 20., -20.]],
dtype=np.float32),
}
labels = {
'head1': np.array([[1, 0], [1, 1]], dtype=np.int64),
'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64),
}
features = {'x': np.array(((42,),), dtype=np.int32)}
regularization_losses = [1.5, 0.5]
# For large logits, sigmoid cross entropy loss is approximated as:
# loss = labels * (logits < 0) * (-logits) +
# (1 - labels) * (logits > 0) * logits =>
# head1: expected_unweighted_loss = [[10., 10.], [15., 0.]]
# loss1 = ((10 + 10) / 2 + (15 + 0) / 2) / 2 = 8.75
# head2: expected_unweighted_loss = [[20., 20., 20.], [30., 0., 0]]
# loss2 = ((20 + 20 + 20) / 3 + (30 + 0 + 0) / 3) / 2 = 15
# Average over classes, weighted sum over batch and heads.
# weights = [1., 2.]
# merged_training_loss = 1. * loss1 + 2. * loss2
# training_loss = merged_training_loss + regularization_loss
# = 1. * loss1 + 2. * loss2 + sum([1.5, 0.5])
expected_loss_head1 = 8.75
expected_loss_head2 = 15.0
expected_regularization_loss = 2.
# training loss.
expected_loss = (1. * expected_loss_head1 + 2. * expected_loss_head2
+ expected_regularization_loss)
tol = 1e-3
loss = multi_head.loss(
logits=logits,
labels=labels,
features=features,
mode=model_fn.ModeKeys.TRAIN,
regularization_losses=regularization_losses)
self.assertAllClose(expected_loss, self.evaluate(loss), rtol=tol, atol=tol)
if context.executing_eagerly():
return
keys = metric_keys.MetricKeys
expected_train_result = 'my_train_op'
def _train_op_fn(loss):
return string_ops.string_join(
[constant_op.constant(expected_train_result),
string_ops.as_string(loss, precision=3)])
spec = multi_head.create_estimator_spec(
features=features,
mode=model_fn.ModeKeys.TRAIN,
logits=logits,
labels=labels,
train_op_fn=_train_op_fn,
regularization_losses=regularization_losses)
self.assertIsNotNone(spec.loss)
self.assertEqual({}, spec.eval_metric_ops)
self.assertIsNotNone(spec.train_op)
self.assertIsNone(spec.export_outputs)
test_lib._assert_no_hooks(self, spec)
# Assert predictions, loss, train_op, and summaries.
with self.cached_session() as sess:
test_lib._initialize_variables(self, spec.scaffold)
self.assertIsNotNone(spec.scaffold.summary_op)
loss, train_result, summary_str = sess.run((spec.loss, spec.train_op,
spec.scaffold.summary_op))
self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol)
self.assertEqual(
six.b('{0:s}{1:.3f}'.format(expected_train_result, expected_loss)),
train_result)
test_lib._assert_simple_summaries(self, {
keys.LOSS_REGULARIZATION: expected_regularization_loss,
keys.LOSS: expected_loss,
keys.LOSS + '/head1': expected_loss_head1,
keys.LOSS + '/head2': expected_loss_head2,
}, summary_str, tol)
def test_name(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
head2 = multi_label_head.MultiLabelHead(n_classes=3, name='head2')
multi_head = multi_head_lib.MultiHead([head1, head2])
self.assertEqual('head1_head2', multi_head.name)
def test_head_name_missing(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
head2 = multi_label_head.MultiLabelHead(n_classes=3)
with self.assertRaisesRegexp(
ValueError, r'All given heads must have name specified\.'):
multi_head_lib.MultiHead([head1, head2])
def test_eval_two_heads_with_weights(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
head2 = multi_label_head.MultiLabelHead(n_classes=3, name='head2')
multi_head = multi_head_lib.MultiHead(
[head1, head2], head_weights=[1., 2.])
logits = {
'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32),
'head2': np.array([[20., -20., 20.], [-30., 20., -20.]],
dtype=np.float32),
}
labels = {
'head1': np.array([[1, 0], [1, 1]], dtype=np.int64),
'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64),
}
features = {'x': np.array(((42,),), dtype=np.int32)}
# For large logits, sigmoid cross entropy loss is approximated as:
# loss = labels * (logits < 0) * (-logits) +
# (1 - labels) * (logits > 0) * logits =>
# head1: expected_unweighted_loss = [[10., 10.], [15., 0.]]
# loss = ((10 + 10) / 2 + (15 + 0) / 2) / 2 = 8.75
# head2: expected_unweighted_loss = [[20., 20., 20.], [30., 0., 0]]
# loss = ((20 + 20 + 20) / 3 + (30 + 0 + 0) / 3) / 2 = 15
expected_loss_head1 = 8.75
expected_loss_head2 = 15.
expected_loss = 1. * expected_loss_head1 + 2. * expected_loss_head2
tol = 1e-3
keys = metric_keys.MetricKeys
expected_metrics = {
keys.LOSS + '/head1': expected_loss_head1,
keys.LOSS + '/head2': expected_loss_head2,
# Average loss over examples.
keys.LOSS_MEAN + '/head1': expected_loss_head1,
keys.LOSS_MEAN + '/head2': expected_loss_head2,
# auc and auc_pr cannot be reliably calculated for only 4-6 samples, but
# this assert tests that the algorithm remains consistent.
# TODO(yhliang): update metrics
# keys.AUC + '/head1': 0.1667,
# keys.AUC + '/head2': 0.3333,
# keys.AUC_PR + '/head1': 0.6667,
# keys.AUC_PR + '/head2': 0.5000,
}
if context.executing_eagerly():
loss = multi_head.loss(
logits, labels, features=features, mode=model_fn.ModeKeys.EVAL)
self.assertIsNotNone(loss)
self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol)
eval_metrics = multi_head.metrics()
updated_metrics = multi_head.update_metrics(
eval_metrics, features, logits, labels)
self.assertItemsEqual(expected_metrics.keys(), updated_metrics.keys())
self.assertAllClose(
expected_metrics,
{k: updated_metrics[k].result() for k in updated_metrics},
rtol=tol,
atol=tol)
return
spec = multi_head.create_estimator_spec(
features=features,
mode=model_fn.ModeKeys.EVAL,
logits=logits,
labels=labels)
# Assert spec contains expected tensors.
self.assertIsNotNone(spec.loss)
self.assertItemsEqual(expected_metrics.keys(), spec.eval_metric_ops.keys())
self.assertIsNone(spec.train_op)
self.assertIsNone(spec.export_outputs)
test_lib._assert_no_hooks(self, spec)
# Assert predictions, loss, and metrics.
with self.cached_session() as sess:
test_lib._initialize_variables(self, spec.scaffold)
self.assertIsNone(spec.scaffold.summary_op)
value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops}
update_ops = {k: spec.eval_metric_ops[k][1] for k in spec.eval_metric_ops}
loss, _ = sess.run((spec.loss, update_ops))
self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol)
# Check results of value ops (in `metrics`).
self.assertAllClose(
expected_metrics, {k: value_ops[k].eval() for k in value_ops},
rtol=tol,
atol=tol)
def test_train_two_heads_with_weights(self):
head1 = multi_label_head.MultiLabelHead(n_classes=2, name='head1')
head2 = multi_label_head.MultiLabelHead(n_classes=3, name='head2')
multi_head = multi_head_lib.MultiHead([head1, head2],
head_weights=[1., 2.])
logits = {
'head1':
np.array([[-10., 10.], [-15., 10.]], dtype=np.float32),
'head2':
np.array([[20., -20., 20.], [-30., 20., -20.]], dtype=np.float32),
}
expected_probabilities = {
'head1': tf.math.sigmoid(logits['head1']),
'head2': tf.math.sigmoid(logits['head2']),
}
labels = {
'head1': np.array([[1, 0], [1, 1]], dtype=np.int64),
'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64),
}
features = {'x': np.array(((42, ), ), dtype=np.int32)}
# For large logits, sigmoid cross entropy loss is approximated as:
# loss = labels * (logits < 0) * (-logits) +
# (1 - labels) * (logits > 0) * logits =>
# head1: expected_unweighted_loss = [[10., 10.], [15., 0.]]
# loss = ((10 + 10) / 2 + (15 + 0) / 2) / 2 = 8.75
# head2: expected_unweighted_loss = [[20., 20., 20.], [30., 0., 0]]
# loss = ((20 + 20 + 20) / 3 + (30 + 0 + 0) / 3) / 2 = 15
# Average over classes, weighted sum over batch and heads.
expected_loss_head1 = 8.75
expected_loss_head2 = 15.0
expected_loss = 1. * expected_loss_head1 + 2. * expected_loss_head2
tol = 1e-3
loss = multi_head.loss(logits=logits,
labels=labels,
features=features,
mode=ModeKeys.TRAIN)
self.assertAllClose(expected_loss,
self.evaluate(loss),
rtol=tol,
atol=tol)
if tf.executing_eagerly():
return
expected_train_result = 'my_train_op'
def _train_op_fn(loss):
return tf.strings.join([
tf.constant(expected_train_result),
tf.strings.as_string(loss, precision=3)
])
spec = multi_head.create_estimator_spec(features=features,
mode=ModeKeys.TRAIN,
logits=logits,
labels=labels,
train_op_fn=_train_op_fn)
self.assertIsNotNone(spec.loss)
self.assertEqual({}, spec.eval_metric_ops)
self.assertIsNotNone(spec.train_op)
self.assertIsNone(spec.export_outputs)
test_lib._assert_no_hooks(self, spec)
# Assert predictions, loss, train_op, and summaries.
with self.cached_session() as sess:
test_lib._initialize_variables(self, spec.scaffold)
self.assertIsNotNone(spec.scaffold.summary_op)
loss, train_result, summary_str, predictions = sess.run(
(spec.loss, spec.train_op, spec.scaffold.summary_op,
spec.predictions))
self.assertAllClose(
logits['head1'],
predictions[('head1', prediction_keys.PredictionKeys.LOGITS)])
self.assertAllClose(
expected_probabilities['head1'],
predictions[('head1',
prediction_keys.PredictionKeys.PROBABILITIES)])
self.assertAllClose(
logits['head2'],
predictions[('head2', prediction_keys.PredictionKeys.LOGITS)])
self.assertAllClose(
expected_probabilities['head2'],
predictions[('head2',
prediction_keys.PredictionKeys.PROBABILITIES)])
self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol)
self.assertEqual(
six.b('{0:s}{1:.3f}'.format(expected_train_result,
expected_loss)), train_result)
test_lib._assert_simple_summaries(
self, {
metric_keys.MetricKeys.LOSS: expected_loss,
metric_keys.MetricKeys.LOSS + '/head1':
expected_loss_head1,
metric_keys.MetricKeys.LOSS + '/head2':
expected_loss_head2,
}, summary_str, tol)
