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https://github.com/zebrajr/tensorflow.git
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Add training test for multi classes (n>2) linear classifier.
PiperOrigin-RevId: 157896002
This commit is contained in:
Jianwei Xie
TensorFlower Gardener
parent
675d36be0d
commit
1c70fb6869
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@ -18,6 +18,7 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import os
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import shutil
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import tempfile
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@ -648,7 +649,7 @@ class LinearRegressorTrainingTest(test.TestCase):
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if self._model_dir:
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shutil.rmtree(self._model_dir)
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def _mockOptimizer(self, expected_loss=None):
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def _mock_optimizer(self, expected_loss=None):
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expected_var_names = [
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'%s/part_0:0' % _AGE_WEIGHT_NAME,
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'%s/part_0:0' % _BIAS_NAME
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@ -680,7 +681,7 @@ class LinearRegressorTrainingTest(test.TestCase):
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mock_optimizer.__deepcopy__ = lambda _: mock_optimizer
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return mock_optimizer
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def _assertCheckpoint(
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def _assert_checkpoint(
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self, expected_global_step, expected_age_weight=None, expected_bias=None):
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shapes = {
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name: shape for (name, shape) in
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@ -717,7 +718,7 @@ class LinearRegressorTrainingTest(test.TestCase):
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num_steps = 10
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linear_regressor.train(
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input_fn=lambda: ({'age': ((age,),)}, ((label,),)), steps=num_steps)
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self._assertCheckpoint(num_steps)
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self._assert_checkpoint(num_steps)
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def testTrainWithOneDimLabel(self):
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label_dimension = 1
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@ -736,7 +737,7 @@ class LinearRegressorTrainingTest(test.TestCase):
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batch_size=batch_size, num_epochs=None,
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shuffle=True)
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est.train(train_input_fn, steps=200)
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self._assertCheckpoint(200)
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self._assert_checkpoint(200)
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def testTrainWithOneDimWeight(self):
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label_dimension = 1
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@ -757,14 +758,14 @@ class LinearRegressorTrainingTest(test.TestCase):
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batch_size=batch_size, num_epochs=None,
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shuffle=True)
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est.train(train_input_fn, steps=200)
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self._assertCheckpoint(200)
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self._assert_checkpoint(200)
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def testFromScratch(self):
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# Create LinearRegressor.
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label = 5.
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age = 17
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# loss = (logits - label)^2 = (0 - 5.)^2 = 25.
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mock_optimizer = self._mockOptimizer(expected_loss=25.)
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mock_optimizer = self._mock_optimizer(expected_loss=25.)
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linear_regressor = linear.LinearRegressor(
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feature_columns=(feature_column_lib.numeric_column('age'),),
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model_dir=self._model_dir, optimizer=mock_optimizer)
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@ -775,7 +776,7 @@ class LinearRegressorTrainingTest(test.TestCase):
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linear_regressor.train(
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input_fn=lambda: ({'age': ((age,),)}, ((label,),)), steps=num_steps)
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self.assertEqual(1, mock_optimizer.minimize.call_count)
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self._assertCheckpoint(
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self._assert_checkpoint(
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expected_global_step=num_steps,
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expected_age_weight=0.,
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expected_bias=0.)
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@ -795,7 +796,7 @@ class LinearRegressorTrainingTest(test.TestCase):
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# logits = age * age_weight + bias = 17 * 10. + 5. = 175
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# loss = (logits - label)^2 = (175 - 5)^2 = 28900
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mock_optimizer = self._mockOptimizer(expected_loss=28900.)
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mock_optimizer = self._mock_optimizer(expected_loss=28900.)
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linear_regressor = linear.LinearRegressor(
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feature_columns=(feature_column_lib.numeric_column('age'),),
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model_dir=self._model_dir, optimizer=mock_optimizer)
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@ -806,7 +807,7 @@ class LinearRegressorTrainingTest(test.TestCase):
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linear_regressor.train(
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input_fn=lambda: ({'age': ((17,),)}, ((5.,),)), steps=num_steps)
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self.assertEqual(1, mock_optimizer.minimize.call_count)
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self._assertCheckpoint(
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self._assert_checkpoint(
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expected_global_step=initial_global_step + num_steps,
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expected_age_weight=age_weight,
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expected_bias=bias)
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@ -828,7 +829,7 @@ class LinearRegressorTrainingTest(test.TestCase):
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# logits[0] = 17 * 10. + 5. = 175
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# logits[1] = 15 * 10. + 5. = 155
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# loss = sum(logits - label)^2 = (175 - 5)^2 + (155 - 3)^2 = 52004
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mock_optimizer = self._mockOptimizer(expected_loss=52004.)
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mock_optimizer = self._mock_optimizer(expected_loss=52004.)
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linear_regressor = linear.LinearRegressor(
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feature_columns=(feature_column_lib.numeric_column('age'),),
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model_dir=self._model_dir, optimizer=mock_optimizer)
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@ -840,13 +841,18 @@ class LinearRegressorTrainingTest(test.TestCase):
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input_fn=lambda: ({'age': ((17,), (15,))}, ((5.,), (3.,))),
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steps=num_steps)
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self.assertEqual(1, mock_optimizer.minimize.call_count)
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self._assertCheckpoint(
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self._assert_checkpoint(
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expected_global_step=initial_global_step + num_steps,
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expected_age_weight=age_weight,
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expected_bias=bias)
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class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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class _BaseLinearClassiferTrainingTest(object):
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def __init__(self, n_classes):
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self._n_classes = n_classes
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self._logits_dimensions = (
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self._n_classes if self._n_classes > 2 else 1)
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def setUp(self):
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self._model_dir = tempfile.mkdtemp()
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@ -855,7 +861,7 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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if self._model_dir:
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shutil.rmtree(self._model_dir)
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def _mockOptimizer(self, expected_loss=None):
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def _mock_optimizer(self, expected_loss=None):
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expected_var_names = [
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'%s/part_0:0' % _AGE_WEIGHT_NAME,
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'%s/part_0:0' % _BIAS_NAME
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@ -887,8 +893,10 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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mock_optimizer.__deepcopy__ = lambda _: mock_optimizer
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return mock_optimizer
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def _assertCheckpoint(
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def _assert_checkpoint(
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self, expected_global_step, expected_age_weight=None, expected_bias=None):
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logits_dimension = self._logits_dimensions
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shapes = {
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name: shape for (name, shape) in
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checkpoint_utils.list_variables(self._model_dir)
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@ -900,20 +908,20 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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checkpoint_utils.load_variable(
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self._model_dir, ops.GraphKeys.GLOBAL_STEP))
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self.assertEqual([1, 1], shapes[_AGE_WEIGHT_NAME])
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self.assertEqual([1, logits_dimension], shapes[_AGE_WEIGHT_NAME])
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if expected_age_weight is not None:
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self.assertEqual(
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self.assertAllEqual(
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expected_age_weight,
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checkpoint_utils.load_variable(self._model_dir, _AGE_WEIGHT_NAME))
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self.assertEqual([1], shapes[_BIAS_NAME])
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self.assertEqual([logits_dimension], shapes[_BIAS_NAME])
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if expected_bias is not None:
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self.assertEqual(
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self.assertAllEqual(
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expected_bias,
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checkpoint_utils.load_variable(self._model_dir, _BIAS_NAME))
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def testFromScratchWithDefaultOptimizer(self):
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n_classes = 2
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n_classes = self._n_classes
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label = 0
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age = 17
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est = linear.LinearClassifier(
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@ -925,10 +933,10 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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num_steps = 10
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est.train(
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input_fn=lambda: ({'age': ((age,),)}, ((label,),)), steps=num_steps)
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self._assertCheckpoint(num_steps)
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self._assert_checkpoint(num_steps)
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def testTrainWithTwoDimsLabel(self):
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n_classes = 2
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n_classes = self._n_classes
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batch_size = 20
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est = linear.LinearClassifier(
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@ -947,10 +955,10 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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num_epochs=None,
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shuffle=True)
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est.train(train_input_fn, steps=200)
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self._assertCheckpoint(200)
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self._assert_checkpoint(200)
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def testTrainWithOneDimLabel(self):
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n_classes = 2
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n_classes = self._n_classes
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batch_size = 20
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est = linear.LinearClassifier(
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@ -967,10 +975,10 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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num_epochs=None,
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shuffle=True)
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est.train(train_input_fn, steps=200)
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self._assertCheckpoint(200)
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self._assert_checkpoint(200)
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def testTrainWithTwoDimsWeight(self):
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n_classes = 2
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n_classes = self._n_classes
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batch_size = 20
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est = linear.LinearClassifier(
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@ -988,10 +996,10 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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batch_size=batch_size, num_epochs=None,
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shuffle=True)
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est.train(train_input_fn, steps=200)
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self._assertCheckpoint(200)
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self._assert_checkpoint(200)
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def testTrainWithOneDimWeight(self):
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n_classes = 2
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n_classes = self._n_classes
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batch_size = 20
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est = linear.LinearClassifier(
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@ -1007,16 +1015,24 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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batch_size=batch_size, num_epochs=None,
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shuffle=True)
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est.train(train_input_fn, steps=200)
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self._assertCheckpoint(200)
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self._assert_checkpoint(200)
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def testFromScratch(self):
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n_classes = 2
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n_classes = self._n_classes
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label = 1
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age = 17
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# loss = sigmoid_cross_entropy(logits, label) where logits = 0 (weights are
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# For binary classifer:
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# loss = sigmoid_cross_entropy(logits, label) where logits=0 (weights are
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# all zero initially) and label = 1 so,
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# loss = 1 * -log ( sigmoid(logits) ) = 0.69315
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mock_optimizer = self._mockOptimizer(expected_loss=0.69315)
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# For multi class classifer:
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# loss = cross_entropy(logits, label) where logits are all 0s (weights are
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# all zero initially) and label = 1 so,
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# loss = 1 * -log ( 1.0 / n_classes )
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# For this particular test case, as logits are same, the formular
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# 1 * -log ( 1.0 / n_classes ) covers both binary and multi class cases.
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mock_optimizer = self._mock_optimizer(
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expected_loss=-1 * math.log(1.0/n_classes))
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est = linear.LinearClassifier(
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feature_columns=(feature_column_lib.numeric_column('age'),),
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@ -1030,31 +1046,49 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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est.train(
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input_fn=lambda: ({'age': ((age,),)}, ((label,),)), steps=num_steps)
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self.assertEqual(1, mock_optimizer.minimize.call_count)
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self._assertCheckpoint(
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self._assert_checkpoint(
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expected_global_step=num_steps,
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expected_age_weight=0.,
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expected_bias=0.)
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expected_age_weight=[[0.]] if n_classes == 2 else [[0.] * n_classes],
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expected_bias=[0.] if n_classes == 2 else [.0] * n_classes)
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def testFromCheckpoint(self):
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# Create initial checkpoint.
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n_classes = 2
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n_classes = self._n_classes
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label = 1
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age = 17
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age_weight = 2.0
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bias = -35.0
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# For binary case, the expected weight has shape (1,1). For multi class
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# case, the shape is (1, n_classes). In order to test the weights, set
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# weights as 2.0 * range(n_classes).
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age_weight = [[2.0]] if n_classes == 2 else (
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np.reshape(2.0 * np.array(list(range(n_classes)), dtype=np.float32),
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(1, n_classes)))
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bias = [-35.0] if n_classes == 2 else [-35.0] * n_classes
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initial_global_step = 100
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with ops.Graph().as_default():
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variables.Variable([[age_weight]], name=_AGE_WEIGHT_NAME)
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variables.Variable([bias], name=_BIAS_NAME)
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variables.Variable(age_weight, name=_AGE_WEIGHT_NAME)
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variables.Variable(bias, name=_BIAS_NAME)
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variables.Variable(
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initial_global_step, name=ops.GraphKeys.GLOBAL_STEP,
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dtype=dtypes.int64)
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_save_variables_to_ckpt(self._model_dir)
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# For binary classifer:
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# logits = age * age_weight + bias = 17 * 2. - 35. = -1.
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# loss = sigmoid_cross_entropy(logits, label)
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# so, loss = 1 * -log ( sigmoid(-1) ) = 1.3133
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mock_optimizer = self._mockOptimizer(expected_loss=1.3133)
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# For multi class classifer:
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# loss = cross_entropy(logits, label)
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# where logits = 17 * age_weight + bias and label = 1
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# so, loss = 1 * -log ( soft_max(logits)[1] )
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if n_classes == 2:
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expected_loss = 1.3133
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else:
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logits = age_weight * age + bias
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logits_exp = np.exp(logits)
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softmax = logits_exp / logits_exp.sum()
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expected_loss = -1 * math.log(softmax[0, label])
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mock_optimizer = self._mock_optimizer(expected_loss=expected_loss)
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est = linear.LinearClassifier(
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feature_columns=(feature_column_lib.numeric_column('age'),),
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@ -1068,34 +1102,55 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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est.train(
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input_fn=lambda: ({'age': ((age,),)}, ((label,),)), steps=num_steps)
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self.assertEqual(1, mock_optimizer.minimize.call_count)
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self._assertCheckpoint(
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self._assert_checkpoint(
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expected_global_step=initial_global_step + num_steps,
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expected_age_weight=age_weight,
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expected_bias=bias)
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def testFromCheckpointMultiBatch(self):
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# Create initial checkpoint.
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n_classes = 2
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n_classes = self._n_classes
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label = [1, 0]
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age = [17, 18.5]
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age_weight = 2.0
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bias = -35.0
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# For binary case, the expected weight has shape (1,1). For multi class
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# case, the shape is (1, n_classes). In order to test the weights, set
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# weights as 2.0 * range(n_classes).
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age_weight = [[2.0]] if n_classes == 2 else (
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np.reshape(2.0 * np.array(list(range(n_classes)), dtype=np.float32),
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(1, n_classes)))
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bias = [-35.0] if n_classes == 2 else [-35.0] * n_classes
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initial_global_step = 100
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with ops.Graph().as_default():
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variables.Variable([[age_weight]], name=_AGE_WEIGHT_NAME)
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variables.Variable([bias], name=_BIAS_NAME)
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variables.Variable(age_weight, name=_AGE_WEIGHT_NAME)
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variables.Variable(bias, name=_BIAS_NAME)
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variables.Variable(
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initial_global_step, name=ops.GraphKeys.GLOBAL_STEP,
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dtype=dtypes.int64)
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_save_variables_to_ckpt(self._model_dir)
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# For binary classifer:
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# logits = age * age_weight + bias
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# logits[0] = 17 * 2. - 35. = -1.
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# logits[1] = 18.5 * 2. - 35. = 2.
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# loss = sigmoid_cross_entropy(logits, label)
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# so, loss[0] = 1 * -log ( sigmoid(-1) ) = 1.3133
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# loss[1] = (1 - 0) * -log ( 1- sigmoid(2) ) = 2.1269
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mock_optimizer = self._mockOptimizer(expected_loss=1.3133 + 2.1269)
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# For multi class classifer:
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# loss = cross_entropy(logits, label)
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# where logits = [17, 18.5] * age_weight + bias and label = [1, 0]
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# so, loss = 1 * -log ( soft_max(logits)[label] )
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if n_classes == 2:
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expected_loss = (1.3133 + 2.1269)
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else:
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logits = age_weight * np.reshape(age, (2, 1)) + bias
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logits_exp = np.exp(logits)
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softmax_row_0 = logits_exp[0] / logits_exp[0].sum()
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softmax_row_1 = logits_exp[1] / logits_exp[1].sum()
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expected_loss_0 = -1 * math.log(softmax_row_0[label[0]])
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expected_loss_1 = -1 * math.log(softmax_row_1[label[1]])
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expected_loss = expected_loss_0 + expected_loss_1
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mock_optimizer = self._mock_optimizer(expected_loss=expected_loss)
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est = linear.LinearClassifier(
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feature_columns=(feature_column_lib.numeric_column('age'),),
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@ -1110,10 +1165,27 @@ class LinearClassiferWithBinaryClassesTrainingTest(test.TestCase):
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input_fn=lambda: ({'age': (age)}, (label)),
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steps=num_steps)
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self.assertEqual(1, mock_optimizer.minimize.call_count)
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self._assertCheckpoint(
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self._assert_checkpoint(
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expected_global_step=initial_global_step + num_steps,
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expected_age_weight=age_weight,
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expected_bias=bias)
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class LinearClassiferWithBinaryClassesTrainingTest(
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_BaseLinearClassiferTrainingTest, test.TestCase):
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def __init__(self, methodName='runTest'):
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test.TestCase.__init__(self, methodName)
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_BaseLinearClassiferTrainingTest.__init__(self, n_classes=2)
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class LinearClassiferWithMultiClassesTrainingTest(
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_BaseLinearClassiferTrainingTest, test.TestCase):
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def __init__(self, methodName='runTest'):
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test.TestCase.__init__(self, methodName)
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_BaseLinearClassiferTrainingTest.__init__(self, n_classes=4)
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||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test.main()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user