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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for TargetColumn."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.layers.python.layers import target_column as target_column_lib
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
class RegressionTargetColumnTest(test.TestCase):
# TODO(zakaria): test multilabel regression.
def testRegression(self):
target_column = target_column_lib.regression_target()
with ops.Graph().as_default(), session.Session() as sess:
prediction = constant_op.constant([[1.], [1.], [3.]])
labels = constant_op.constant([[0.], [1.], [1.]])
self.assertAlmostEqual(
5. / 3, sess.run(target_column.loss(prediction, labels, {})))
def testRegressionWithWeights(self):
target_column = target_column_lib.regression_target(
weight_column_name="label_weight")
with ops.Graph().as_default(), session.Session() as sess:
features = {"label_weight": constant_op.constant([[2.], [5.], [0.]])}
prediction = constant_op.constant([[1.], [1.], [3.]])
labels = constant_op.constant([[0.], [1.], [1.]])
self.assertAlmostEqual(
2. / 7,
sess.run(target_column.loss(prediction, labels, features)),
places=3)
self.assertAlmostEqual(
2. / 3,
sess.run(target_column.training_loss(prediction, labels, features)),
places=3)
class MultiClassTargetColumnTest(test.TestCase):
def testBinaryClassification(self):
target_column = target_column_lib.multi_class_target(n_classes=2)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1.], [1.]])
labels = constant_op.constant([[1.], [0.]])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
0.81326175,
sess.run(target_column.loss(logits, labels, {})),
delta=1e-6)
def testBinaryClassificationWithWeights(self):
target_column = target_column_lib.multi_class_target(
n_classes=2, weight_column_name="label_weight")
with ops.Graph().as_default(), session.Session() as sess:
features = {"label_weight": constant_op.constant([[1.], [0.]])}
logits = constant_op.constant([[1.], [1.]])
labels = constant_op.constant([[1.], [0.]])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
.31326166,
sess.run(target_column.loss(logits, labels, features)),
delta=1e-6)
def testBinaryEvalMetrics(self):
target_column = target_column_lib.multi_class_target(n_classes=2)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1.], [1.], [-1.]])
labels = constant_op.constant([[1.], [0.], [1.]])
eval_dict = target_column.get_eval_ops({}, logits, labels)
# TODO(zakaria): test all metrics
accuracy_op, update_op = eval_dict["accuracy/threshold_0.500000_mean"]
sess.run(variables.global_variables_initializer())
sess.run(variables.local_variables_initializer())
sess.run(update_op)
self.assertAlmostEqual(1.0 / 3, sess.run(accuracy_op))
def testMultiClass(self):
target_column = target_column_lib.multi_class_target(n_classes=3)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(1.5514446,
sess.run(target_column.loss(logits, labels, {})))
def testMultiClassWithWeight(self):
target_column = target_column_lib.multi_class_target(
n_classes=3, weight_column_name="label_weight")
with ops.Graph().as_default(), session.Session() as sess:
features = {"label_weight": constant_op.constant([0.1])}
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
1.5514446, sess.run(target_column.loss(logits, labels, features)))
def testMultiClassWithInvalidNClass(self):
try:
target_column_lib.multi_class_target(n_classes=1)
self.fail("Softmax with no n_classes did not raise error.")
except ValueError:
# Expected
pass
def testMultiClassEvalMetrics(self):
target_column = target_column_lib.multi_class_target(n_classes=3)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
eval_dict = target_column.get_eval_ops({}, logits, labels)
loss_op, update_op = eval_dict["loss"]
sess.run(variables.global_variables_initializer())
sess.run(variables.local_variables_initializer())
sess.run(update_op)
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(1.5514446, sess.run(loss_op))
def testBinarySVMDefaultWeights(self):
target_column = target_column_lib.binary_svm_target()
predictions = constant_op.constant([[-0.5], [1.2]])
labels = constant_op.constant([0, 1])
loss = target_column.loss(predictions, labels, {})
# Prediction for first example is in the right side of the hyperplane (i.e.,
# < 0) but it is within the [-1,1] margin. There is a 0.5 loss incurred by
# this example. The 2nd prediction is outside the margin so it incurs no
# loss at all. The overall (normalized) loss is therefore 0.5/(1+1) = 0.25.
with session.Session() as sess:
self.assertAlmostEqual(0.25, sess.run(loss))
def testBinarySVMWithWeights(self):
target_column = target_column_lib.binary_svm_target(
weight_column_name="weights")
predictions = constant_op.constant([[-0.7], [0.2]])
labels = constant_op.constant([0, 1])
features = {"weights": constant_op.constant([2.0, 10.0])}
loss = target_column.loss(predictions, labels, features)
training_loss = target_column.training_loss(predictions, labels, features)
# Prediction for both examples are in the right side of the hyperplane but
# within the margin. The (weighted) loss incurred is 2*0.3=0.6 and 10*0.8=8
# respectively. The overall (normalized) loss is therefore 8.6/12.
with session.Session() as sess:
self.assertAlmostEqual(8.6 / 12, sess.run(loss), places=3)
self.assertAlmostEqual(8.6 / 2, sess.run(training_loss), places=3)
if __name__ == "__main__":
test.main()
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