def test_from_scratch_validate_summary(self):
hidden_units = (2, 2)
mock_optimizer = dnn_testing_utils.mock_optimizer(
self, hidden_units=hidden_units)
dnn_classifier = dnn.DNNClassifier(
hidden_units=hidden_units,
feature_columns=(feature_column.numeric_column('age'),),
optimizer=mock_optimizer,
model_dir=self._model_dir)
self.assertEqual(0, mock_optimizer.minimize.call_count)
# Train for a few steps, then validate optimizer, summaries, and
# checkpoint.
num_steps = 5
summary_hook = _SummaryHook()
dnn_classifier.train(
input_fn=lambda: ({'age': [[10.]]}, [[1]]), steps=num_steps,
hooks=(summary_hook,))
self.assertEqual(1, mock_optimizer.minimize.call_count)
_assert_checkpoint(
self, num_steps, input_units=1, hidden_units=hidden_units,
output_units=1, model_dir=self._model_dir)
summaries = summary_hook.summaries()
self.assertEqual(num_steps, len(summaries))
for summary in summaries:
summary_keys = [v.tag for v in summary.value]
self.assertIn(metric_keys.MetricKeys.LOSS, summary_keys)
self.assertIn(metric_keys.MetricKeys.LOSS_MEAN, summary_keys)
def test_from_scratch(self):
hidden_units = (2, 2)
mock_optimizer = dnn_testing_utils.mock_optimizer(
self, hidden_units=hidden_units)
dnn_regressor = dnn.DNNRegressor(
hidden_units=hidden_units,
feature_columns=(feature_column.numeric_column('age'), ),
optimizer=mock_optimizer,
model_dir=self._model_dir)
self.assertEqual(0, mock_optimizer.minimize.call_count)
# Train for a few steps, then validate optimizer, summaries, and
# checkpoint.
num_steps = 5
summary_hook = _SummaryHook()
dnn_regressor.train(input_fn=lambda: ({
'age': ((1, ), )
}, ((5., ), )),
steps=num_steps,
hooks=(summary_hook, ))
self.assertEqual(1, mock_optimizer.minimize.call_count)
_assert_checkpoint(self,
num_steps,
input_units=1,
hidden_units=hidden_units,
output_units=1,
model_dir=self._model_dir)
summaries = summary_hook.summaries()
self.assertEqual(num_steps, len(summaries))
for summary in summaries:
summary_keys = [v.tag for v in summary.value]
self.assertIn(metric_keys.MetricKeys.LOSS, summary_keys)
self.assertIn(metric_keys.MetricKeys.LOSS_MEAN, summary_keys)
def test_multi_dim(self):
"""Asserts train loss for multi-dimensional input and logits."""
base_global_step = 100
hidden_units = (2, 2)
dnn_testing_utils.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)
input_dimension = 2
label_dimension = 3
# Uses identical numbers as
# DNNModelFnTest.test_multi_dim_input_multi_dim_logits.
# See that test for calculation of logits.
# logits = [[-0.48, 0.48, 0.39]]
# loss = (1+0.48)^2 + (-1-0.48)^2 + (0.5-0.39)^2 = 4.3929
expected_loss = 4.3929
mock_optimizer = dnn_testing_utils.mock_optimizer(
self, hidden_units=hidden_units, expected_loss=expected_loss)
dnn_regressor = dnn.DNNRegressor(hidden_units=hidden_units,
feature_columns=[
feature_column.numeric_column(
'age',
shape=[input_dimension])
],
label_dimension=label_dimension,
optimizer=mock_optimizer,
model_dir=self._model_dir)
self.assertEqual(0, mock_optimizer.minimize.call_count)
# Train for a few steps, then validate optimizer, summaries, and
# checkpoint.
num_steps = 5
summary_hook = _SummaryHook()
dnn_regressor.train(input_fn=lambda: ({
'age': [[10., 8.]]
}, [[1., -1., 0.5]]),
steps=num_steps,
hooks=(summary_hook, ))
self.assertEqual(1, mock_optimizer.minimize.call_count)
summaries = summary_hook.summaries()
self.assertEqual(num_steps, len(summaries))
for summary in summaries:
_assert_simple_summary(
self, {
metric_keys.MetricKeys.LOSS_MEAN:
expected_loss / label_dimension,
'dnn/dnn/hiddenlayer_0_fraction_of_zero_values': 0.,
'dnn/dnn/hiddenlayer_1_fraction_of_zero_values': 0.5,
'dnn/dnn/logits_fraction_of_zero_values': 0.,
metric_keys.MetricKeys.LOSS: expected_loss,
}, summary)
_assert_checkpoint(self,
base_global_step + num_steps,
input_units=input_dimension,
hidden_units=hidden_units,
output_units=label_dimension,
model_dir=self._model_dir)
def test_multi_dim(self):
"""Asserts train loss for multi-dimensional input and logits."""
base_global_step = 100
hidden_units = (2, 2)
dnn_testing_utils.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)
input_dimension = 2
label_dimension = 3
# Uses identical numbers as
# DNNModelFnTest.test_multi_dim_input_multi_dim_logits.
# See that test for calculation of logits.
# logits = [[-0.48, 0.48, 0.39]]
# loss = (1+0.48)^2 + (-1-0.48)^2 + (0.5-0.39)^2 = 4.3929
expected_loss = 4.3929
mock_optimizer = dnn_testing_utils.mock_optimizer(
self, hidden_units=hidden_units, expected_loss=expected_loss)
dnn_regressor = dnn.DNNRegressor(
hidden_units=hidden_units,
feature_columns=[
feature_column.numeric_column('age', shape=[input_dimension])],
label_dimension=label_dimension,
optimizer=mock_optimizer,
model_dir=self._model_dir)
self.assertEqual(0, mock_optimizer.minimize.call_count)
# Train for a few steps, then validate optimizer, summaries, and
# checkpoint.
num_steps = 5
summary_hook = _SummaryHook()
dnn_regressor.train(
input_fn=lambda: ({'age': [[10., 8.]]}, [[1., -1., 0.5]]),
steps=num_steps,
hooks=(summary_hook,))
self.assertEqual(1, mock_optimizer.minimize.call_count)
summaries = summary_hook.summaries()
self.assertEqual(num_steps, len(summaries))
for summary in summaries:
_assert_simple_summary(
self,
{
metric_keys.MetricKeys.LOSS_MEAN: expected_loss / label_dimension,
'dnn/dnn/hiddenlayer_0_fraction_of_zero_values': 0.,
'dnn/dnn/hiddenlayer_1_fraction_of_zero_values': 0.5,
'dnn/dnn/logits_fraction_of_zero_values': 0.,
metric_keys.MetricKeys.LOSS: expected_loss,
},
summary)
_assert_checkpoint(
self, base_global_step + num_steps, input_units=input_dimension,
hidden_units=hidden_units, output_units=label_dimension,
model_dir=self._model_dir)
def test_multi_class(self):
n_classes = 3
base_global_step = 100
hidden_units = (2, 2)
dnn_testing_utils.create_checkpoint((
([[.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)
# Uses identical numbers as DNNModelFnTest.test_multi_dim_logits.
# See that test for calculation of logits.
# logits = [-2.08, 2.08, 1.19] => probabilities = [0.0109, 0.7011, 0.2879]
# loss = -1. * log(0.7011) = 0.35505795
expected_loss = 0.35505795
mock_optimizer = dnn_testing_utils.mock_optimizer(
self, hidden_units=hidden_units, expected_loss=expected_loss)
dnn_classifier = dnn.DNNClassifier(
n_classes=n_classes,
hidden_units=hidden_units,
feature_columns=(feature_column.numeric_column('age'), ),
optimizer=mock_optimizer,
model_dir=self._model_dir)
self.assertEqual(0, mock_optimizer.minimize.call_count)
# Train for a few steps, then validate optimizer, summaries, and
# checkpoint.
num_steps = 5
summary_hook = _SummaryHook()
dnn_classifier.train(input_fn=lambda: ({
'age': [[10.]]
}, [[1]]),
steps=num_steps,
hooks=(summary_hook, ))
self.assertEqual(1, mock_optimizer.minimize.call_count)
summaries = summary_hook.summaries()
self.assertEqual(num_steps, len(summaries))
for summary in summaries:
_assert_simple_summary(
self, {
metric_keys.MetricKeys.LOSS_MEAN: expected_loss,
'dnn/dnn/hiddenlayer_0_fraction_of_zero_values': 0.,
'dnn/dnn/hiddenlayer_1_fraction_of_zero_values': .5,
'dnn/dnn/logits_fraction_of_zero_values': 0.,
metric_keys.MetricKeys.LOSS: expected_loss,
}, summary)
_assert_checkpoint(self,
base_global_step + num_steps,
input_units=1,
hidden_units=hidden_units,
output_units=n_classes,
model_dir=self._model_dir)
def test_one_dim(self):
"""Asserts train loss for one-dimensional input and logits."""
base_global_step = 100
hidden_units = (2, 2)
dnn_testing_utils.create_checkpoint((
([[.6, .5]], [.1, -.1]),
([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1.], [1.]], [.3]),
), base_global_step, self._model_dir)
# Uses identical numbers as DNNModelFnTest.test_one_dim_logits.
# See that test for calculation of logits.
# logits = [-2.08] => predictions = [-2.08]
# loss = (1 + 2.08)^2 = 9.4864
expected_loss = 9.4864
mock_optimizer = dnn_testing_utils.mock_optimizer(
self, hidden_units=hidden_units, expected_loss=expected_loss)
dnn_regressor = dnn.DNNRegressor(
hidden_units=hidden_units,
feature_columns=(feature_column.numeric_column('age'), ),
optimizer=mock_optimizer,
model_dir=self._model_dir)
self.assertEqual(0, mock_optimizer.minimize.call_count)
# Train for a few steps, then validate optimizer, summaries, and
# checkpoint.
num_steps = 5
summary_hook = _SummaryHook()
dnn_regressor.train(input_fn=lambda: ({
'age': [[10.]]
}, [[1.]]),
steps=num_steps,
hooks=(summary_hook, ))
self.assertEqual(1, mock_optimizer.minimize.call_count)
summaries = summary_hook.summaries()
self.assertEqual(num_steps, len(summaries))
for summary in summaries:
_assert_simple_summary(
self, {
metric_keys.MetricKeys.LOSS_MEAN: expected_loss,
'dnn/dnn/hiddenlayer_0_fraction_of_zero_values': 0.,
'dnn/dnn/hiddenlayer_1_fraction_of_zero_values': 0.5,
'dnn/dnn/logits_fraction_of_zero_values': 0.,
metric_keys.MetricKeys.LOSS: expected_loss,
}, summary)
_assert_checkpoint(self,
base_global_step + num_steps,
input_units=1,
hidden_units=hidden_units,
output_units=1,
model_dir=self._model_dir)
def test_multi_class(self):
n_classes = 3
base_global_step = 100
hidden_units = (2, 2)
dnn_testing_utils.create_checkpoint(
(([[.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)
# Uses identical numbers as DNNModelFnTest.test_multi_dim_logits.
# See that test for calculation of logits.
# logits = [-2.08, 2.08, 1.19] => probabilities = [0.0109, 0.7011, 0.2879]
# loss = -1. * log(0.7011) = 0.35505795
expected_loss = 0.35505795
mock_optimizer = dnn_testing_utils.mock_optimizer(
self, hidden_units=hidden_units, expected_loss=expected_loss)
dnn_classifier = dnn.DNNClassifier(
n_classes=n_classes,
hidden_units=hidden_units,
feature_columns=(feature_column.numeric_column('age'),),
optimizer=mock_optimizer,
model_dir=self._model_dir)
self.assertEqual(0, mock_optimizer.minimize.call_count)
# Train for a few steps, then validate optimizer, summaries, and
# checkpoint.
num_steps = 5
summary_hook = _SummaryHook()
dnn_classifier.train(
input_fn=lambda: ({'age': [[10.]]}, [[1]]), steps=num_steps,
hooks=(summary_hook,))
self.assertEqual(1, mock_optimizer.minimize.call_count)
summaries = summary_hook.summaries()
self.assertEqual(num_steps, len(summaries))
for summary in summaries:
_assert_simple_summary(
self,
{
metric_keys.MetricKeys.LOSS_MEAN: expected_loss,
'dnn/dnn/hiddenlayer_0_fraction_of_zero_values': 0.,
'dnn/dnn/hiddenlayer_1_fraction_of_zero_values': .5,
'dnn/dnn/logits_fraction_of_zero_values': 0.,
metric_keys.MetricKeys.LOSS: expected_loss,
},
summary)
_assert_checkpoint(
self, base_global_step + num_steps, input_units=1,
hidden_units=hidden_units, output_units=n_classes,
model_dir=self._model_dir)
def test_one_dim(self):
"""Asserts train loss for one-dimensional input and logits."""
base_global_step = 100
hidden_units = (2, 2)
dnn_testing_utils.create_checkpoint(
(([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1.], [1.]], [.3]),), base_global_step, self._model_dir)
# Uses identical numbers as DNNModelFnTest.test_one_dim_logits.
# See that test for calculation of logits.
# logits = [-2.08] => predictions = [-2.08]
# loss = (1 + 2.08)^2 = 9.4864
expected_loss = 9.4864
mock_optimizer = dnn_testing_utils.mock_optimizer(
self, hidden_units=hidden_units, expected_loss=expected_loss)
dnn_regressor = dnn.DNNRegressor(
hidden_units=hidden_units,
feature_columns=(feature_column.numeric_column('age'),),
optimizer=mock_optimizer,
model_dir=self._model_dir)
self.assertEqual(0, mock_optimizer.minimize.call_count)
# Train for a few steps, then validate optimizer, summaries, and
# checkpoint.
num_steps = 5
summary_hook = _SummaryHook()
dnn_regressor.train(
input_fn=lambda: ({'age': [[10.]]}, [[1.]]), steps=num_steps,
hooks=(summary_hook,))
self.assertEqual(1, mock_optimizer.minimize.call_count)
summaries = summary_hook.summaries()
self.assertEqual(num_steps, len(summaries))
for summary in summaries:
_assert_simple_summary(
self,
{
metric_keys.MetricKeys.LOSS_MEAN: expected_loss,
'dnn/dnn/hiddenlayer_0_fraction_of_zero_values': 0.,
'dnn/dnn/hiddenlayer_1_fraction_of_zero_values': 0.5,
'dnn/dnn/logits_fraction_of_zero_values': 0.,
metric_keys.MetricKeys.LOSS: expected_loss,
},
summary)
_assert_checkpoint(
self, base_global_step + num_steps, input_units=1,
hidden_units=hidden_units, output_units=1, model_dir=self._model_dir)
