Python SdcaModel.minimize方法代码示例

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
 def testInstancesOfOneClassOnly(self):
   # Setup test data with 1 positive (ignored), and 1 negative example.
   example_protos = [
       make_example_proto(
           {'age': [0],
            'gender': [0]}, 0),
       make_example_proto(
           {'age': [1],
            'gender': [0]}, 1),  # Shares gender with the instance above.
   ]
   example_weights = [1.0, 0.0]  # Second example "omitted" from training.
   with self._single_threaded_test_session():
     examples = make_example_dict(example_protos, example_weights)
     variables = make_variable_dict(1, 1)
     options = dict(symmetric_l2_regularization=0.25,
                    symmetric_l1_regularization=0,
                    loss_type='logistic_loss')
     tf.initialize_all_variables().run()
     lr = SdcaModel(CONTAINER, examples, variables, options)
     unregularized_loss = lr.unregularized_loss(examples)
     loss = lr.regularized_loss(examples)
     prediction = lr.predictions(examples)
     lr.minimize().run()
     self.assertAllClose(0.395226,
                         unregularized_loss.eval(),
                         rtol=3e-2,
                         atol=3e-2)
     self.assertAllClose(0.460781, loss.eval(), rtol=3e-2, atol=3e-2)
     predicted_labels = tf.cast(
         tf.greater_equal(prediction,
                          tf.ones_like(prediction) * 0.5), tf.float32)
     self.assertAllEqual([0, 0], predicted_labels.eval())

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
  def testDenseFeatures(self):
    with self._single_threaded_test_session():
      examples = make_dense_examples_dict(
          dense_feature_values=[[-2.0, 0.0], [0.0, 2.0]],
          weights=[1.0, 1.0],
          labels=[-10.0, 14.0])
      variables = make_dense_variable_dict(2, 2)
      options = dict(symmetric_l2_regularization=1,
                     symmetric_l1_regularization=0,
                     loss_type='squared_loss')
      lr = SdcaModel(CONTAINER, examples, variables, options)
      tf.initialize_all_variables().run()
      predictions = lr.predictions(examples)

      for _ in xrange(20):
        lr.minimize().run()

      # Predictions should be 4/5 of label due to minimizing regularized loss:
      #   (label - 2 * weight)^2 / 2 + L2 * weight^2
      self.assertAllClose([-10.0 * 4 / 5, 14.0 * 4 / 5],
                          predictions.eval(),
                          rtol=0.01)

      loss = lr.regularized_loss(examples)
      self.assertAllClose(148.0 / 10.0, loss.eval(), atol=0.01)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
  def testDenseFeaturesPerfectlySeparable(self):
    with self._single_threaded_test_session():
      examples = make_dense_examples_dict(
          dense_feature_values=[[1.0, 1.0], [1.0, -1.0]],
          weights=[1.0, 1.0],
          labels=[1.0, 0.0])
      variables = make_dense_variable_dict(2, 2)
      options = dict(symmetric_l2_regularization=1.0,
                     symmetric_l1_regularization=0,
                     loss_type='hinge_loss')
      model = SdcaModel(CONTAINER, examples, variables, options)
      tf.initialize_all_variables().run()
      predictions = model.predictions(examples)
      binary_predictions = get_binary_predictions_for_hinge(predictions)

      for _ in xrange(5):
        model.minimize().run()

      self.assertAllClose([1.0, -1.0], predictions.eval(), atol=0.05)
      self.assertAllClose([1.0, 0.0], binary_predictions.eval())

      # (1.0, 1.0) and (1.0, -1.0) are perfectly separable by x-axis (that is,
      # the SVM's functional margin >=1), so the unregularized loss is ~0.0.
      # There is only loss due to l2-regularization. For these datapoints, it
      # turns out that w_1~=0.0 and w_2~=1.0 which means that l2 loss is ~0.25.
      unregularized_loss = model.unregularized_loss(examples)
      regularized_loss = model.regularized_loss(examples)
      self.assertAllClose(0.0, unregularized_loss.eval(), atol=0.02)
      self.assertAllClose(0.25, regularized_loss.eval(), atol=0.02)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
  def testSimple(self):
    # Setup test data
    example_protos = [
        make_example_proto(
            {'age': [0],
             'gender': [0]}, -10.0),
        make_example_proto(
            {'age': [1],
             'gender': [1]}, 14.0),
    ]
    example_weights = [1.0, 1.0]
    with self._single_threaded_test_session():
      examples = make_example_dict(example_protos, example_weights)
      variables = make_variable_dict(1, 1)
      options = dict(symmetric_l2_regularization=1,
                     symmetric_l1_regularization=0,
                     loss_type='squared_loss')

      lr = SdcaModel(CONTAINER, examples, variables, options)
      tf.initialize_all_variables().run()
      predictions = lr.predictions(examples)

      for _ in xrange(20):
        lr.minimize().run()

      # Predictions should be 2/3 of label due to minimizing regularized loss:
      #   (label - 2 * weight)^2 / 2 + L2 * 2 * weight^2
      self.assertAllClose([-20.0 / 3.0, 28.0 / 3.0],
                          predictions.eval(),
                          rtol=0.005)
      self.assertAllClose(0.01,
                          lr.approximate_duality_gap().eval(),
                          rtol=1e-2,
                          atol=1e-2)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
  def testL1Regularization(self):
    # Setup test data
    example_protos = [
        make_example_proto(
            {'age': [0],
             'gender': [0]}, -10.0),
        make_example_proto(
            {'age': [1],
             'gender': [1]}, 14.0),
    ]
    example_weights = [1.0, 1.0]
    with self._single_threaded_test_session():
      examples = make_example_dict(example_protos, example_weights)
      variables = make_variable_dict(1, 1)
      options = dict(symmetric_l2_regularization=1.0,
                     symmetric_l1_regularization=4.0,
                     loss_type='squared_loss')
      lr = SdcaModel(CONTAINER, examples, variables, options)
      tf.initialize_all_variables().run()
      prediction = lr.predictions(examples)
      loss = lr.regularized_loss(examples)

      for _ in xrange(5):
        lr.minimize().run()

      # Predictions should be -4.0, 48/5 due to minimizing regularized loss:
      #   (label - 2 * weight)^2 / 2 + L2 * 2 * weight^2 + L1 * 4 * weight
      self.assertAllClose([-4.0, 20.0 / 3.0], prediction.eval(), rtol=0.08)

      # Loss should be the sum of the regularized loss value from above per
      # example after plugging in the optimal weights.
      self.assertAllClose(308.0 / 6.0, loss.eval(), atol=0.01)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
 def testNoWeightedExamples(self):
   # Setup test data with 1 positive, and 1 negative example.
   example_protos = [
       make_example_proto(
           {'age': [0],
            'gender': [0]}, 0),
       make_example_proto(
           {'age': [1],
            'gender': [1]}, 1),
   ]
   # Zeroed out example weights.
   example_weights = [0.0, 0.0]
   with self._single_threaded_test_session():
     examples = make_example_dict(example_protos, example_weights)
     variables = make_variable_dict(1, 1)
     options = dict(symmetric_l2_regularization=0.5,
                    symmetric_l1_regularization=0,
                    loss_type='logistic_loss')
     tf.initialize_all_variables().run()
     lr = SdcaModel(CONTAINER, examples, variables, options)
     self.assertAllClose([0.5, 0.5], lr.predictions(examples).eval())
     with self.assertRaisesOpError(
         'No weighted examples in 2 training examples'):
       lr.minimize().run()
     self.assertAllClose([0.5, 0.5], lr.predictions(examples).eval())

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
  def testLinearFeatureValues(self):
    # Setup test data
    example_protos = [
        make_example_proto(
            {'age': [0],
             'gender': [0]}, -10.0, -2.0),
        make_example_proto(
            {'age': [1],
             'gender': [1]}, 14.0, 2.0),
    ]
    example_weights = [1.0, 1.0]
    with self._single_threaded_test_session():
      examples = make_example_dict(example_protos, example_weights)

      variables = make_variable_dict(1, 1)
      options = dict(symmetric_l2_regularization=0.5,
                     symmetric_l1_regularization=0,
                     loss_type='squared_loss',
                     prior=0.0)
      tf.initialize_all_variables().run()
      lr = SdcaModel(CONTAINER, examples, variables, options)
      prediction = lr.predictions(examples)

      lr.minimize().run()

      # Predictions should be 8/9 of label due to minimizing regularized loss:
      #   (label - 2 * 2 * weight)^2 / 2 + L2 * 2 * weight^2
      self.assertAllClose([-10.0 * 8 / 9, 14.0 * 8 / 9],
                          prediction.eval(),
                          rtol=0.07)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
 def testImbalanced(self):
     # Setup test data with 1 positive, and 3 negative examples.
     example_protos = [
         make_example_proto({"age": [0], "gender": [0]}, 0),
         make_example_proto({"age": [2], "gender": [0]}, 0),
         make_example_proto({"age": [3], "gender": [0]}, 0),
         make_example_proto({"age": [1], "gender": [1]}, 1),
     ]
     example_weights = [1.0, 1.0, 1.0, 1.0]
     with self._single_threaded_test_session():
         examples = make_example_dict(example_protos, example_weights)
         variables = make_variable_dict(3, 1)
         options = dict(
             symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss", prior=-1.09861
         )
         tf.initialize_all_variables().run()
         lr = SdcaModel(CONTAINER, examples, variables, options)
         unregularized_loss = lr.unregularized_loss(examples)
         loss = lr.regularized_loss(examples)
         prediction = lr.predictions(examples)
         lr.minimize().run()
         self.assertAllClose(0.331710, unregularized_loss.eval(), rtol=3e-2, atol=3e-2)
         self.assertAllClose(0.591295, loss.eval(), rtol=3e-2, atol=3e-2)
         predicted_labels = tf.cast(tf.greater_equal(prediction, tf.ones_like(prediction) * 0.5), tf.float32)
         self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval())

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
    def testLinearRegularization(self):
        # Setup test data
        example_protos = [
            # 2 identical examples
            make_example_proto({"age": [0], "gender": [0]}, -10.0),
            make_example_proto({"age": [0], "gender": [0]}, -10.0),
            # 2 more identical examples
            make_example_proto({"age": [1], "gender": [1]}, 14.0),
            make_example_proto({"age": [1], "gender": [1]}, 14.0),
        ]
        example_weights = [1.0, 1.0, 1.0, 1.0]
        with self._single_threaded_test_session():
            examples = make_example_dict(example_protos, example_weights)
            variables = make_variable_dict(1, 1)
            options = dict(
                symmetric_l2_regularization=16, symmetric_l1_regularization=0, loss_type="squared_loss", prior=0.0
            )
            tf.initialize_all_variables().run()
            lr = SdcaModel(CONTAINER, examples, variables, options)
            prediction = lr.predictions(examples)

            lr.minimize().run()

            # Predictions should be 1/5 of label due to minimizing regularized loss:
            #   (label - 2 * weight)^2 + L2 * 16 * weight^2
            optimal1 = -10.0 / 5.0
            optimal2 = 14.0 / 5.0
            self.assertAllClose([optimal1, optimal1, optimal2, optimal2], prediction.eval(), rtol=0.01)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
    def testImbalanced(self):
        # Setup test data with 1 positive, and 3 negative examples.
        example_protos = [
            make_example_proto({"age": [0], "gender": [0]}, 0),
            make_example_proto({"age": [2], "gender": [0]}, 0),
            make_example_proto({"age": [3], "gender": [0]}, 0),
            make_example_proto({"age": [1], "gender": [1]}, 1),
        ]
        example_weights = [1.0, 1.0, 1.0, 1.0]
        with self._single_threaded_test_session():
            examples = make_example_dict(example_protos, example_weights)
            variables = make_variable_dict(3, 1)
            options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss")

            lr = SdcaModel(CONTAINER, examples, variables, options)
            tf.initialize_all_variables().run()
            unregularized_loss = lr.unregularized_loss(examples)
            loss = lr.regularized_loss(examples)
            predictions = lr.predictions(examples)
            for _ in xrange(5):
                lr.minimize().run()
            self.assertAllClose(0.226487 + 0.102902, unregularized_loss.eval(), rtol=0.08)
            self.assertAllClose(0.328394 + 0.131364, loss.eval(), atol=0.01)
            predicted_labels = get_binary_predictions_for_logistic(predictions)
            self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval())
            self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
 def testSomeUnweightedExamples(self):
     # Setup test data with 4 examples, but should produce the same
     # results as testSimple.
     example_protos = [
         # Will be used.
         make_example_proto({"age": [0], "gender": [0]}, 0),
         # Will be ignored.
         make_example_proto({"age": [1], "gender": [0]}, 0),
         # Will be used.
         make_example_proto({"age": [1], "gender": [1]}, 1),
         # Will be ignored.
         make_example_proto({"age": [1], "gender": [0]}, 1),
     ]
     example_weights = [1.0, 0.0, 1.0, 0.0]
     with self._single_threaded_test_session():
         # Only use examples 0 and 2
         examples = make_example_dict(example_protos, example_weights)
         variables = make_variable_dict(1, 1)
         options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss")
         tf.initialize_all_variables().run()
         lr = SdcaModel(CONTAINER, examples, variables, options)
         unregularized_loss = lr.unregularized_loss(examples)
         loss = lr.regularized_loss(examples)
         prediction = lr.predictions(examples)
         lr.minimize().run()
         self.assertAllClose(0.395226, unregularized_loss.eval(), rtol=3e-2, atol=3e-2)
         self.assertAllClose(0.657446, loss.eval(), rtol=3e-2, atol=3e-2)
         predicted_labels = tf.cast(tf.greater_equal(prediction, tf.ones_like(prediction) * 0.5), tf.float32)
         self.assertAllClose([0, 1, 1, 1], predicted_labels.eval())

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
    def testSomeUnweightedExamples(self):
        # Setup test data with 4 examples, but should produce the same
        # results as testSimple.
        example_protos = [
            # Will be used.
            make_example_proto({"age": [0], "gender": [0]}, 0),
            # Will be ignored.
            make_example_proto({"age": [1], "gender": [0]}, 0),
            # Will be used.
            make_example_proto({"age": [1], "gender": [1]}, 1),
            # Will be ignored.
            make_example_proto({"age": [1], "gender": [0]}, 1),
        ]
        example_weights = [1.0, 0.0, 1.0, 0.0]
        with self._single_threaded_test_session():
            # Only use examples 0 and 2
            examples = make_example_dict(example_protos, example_weights)
            variables = make_variable_dict(1, 1)
            options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss")

            lr = SdcaModel(CONTAINER, examples, variables, options)
            tf.initialize_all_variables().run()
            unregularized_loss = lr.unregularized_loss(examples)
            loss = lr.regularized_loss(examples)
            predictions = lr.predictions(examples)
            for _ in xrange(5):
                lr.minimize().run()
            self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.12)
            self.assertAllClose(0.525457, loss.eval(), atol=0.01)
            predicted_labels = get_binary_predictions_for_logistic(predictions)
            self.assertAllClose([0, 1, 1, 1], predicted_labels.eval())
            self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
    def testSimpleNoL2(self):
        # Same as test above (so comments from above apply) but without an L2.
        # The algorithm should behave as if we have an L2 of 1 in optimization but
        # 0 in regularized_loss.
        example_protos = [
            make_example_proto({"age": [0], "gender": [0]}, 0),
            make_example_proto({"age": [1], "gender": [1]}, 1),
        ]
        example_weights = [1.0, 1.0]
        with self._single_threaded_test_session():
            examples = make_example_dict(example_protos, example_weights)
            variables = make_variable_dict(1, 1)
            options = dict(symmetric_l2_regularization=0, symmetric_l1_regularization=0, loss_type="logistic_loss")

            lr = SdcaModel(CONTAINER, examples, variables, options)
            tf.initialize_all_variables().run()
            unregularized_loss = lr.unregularized_loss(examples)
            loss = lr.regularized_loss(examples)
            predictions = lr.predictions(examples)
            self.assertAllClose(0.693147, unregularized_loss.eval())
            self.assertAllClose(0.693147, loss.eval())
            for _ in xrange(5):
                lr.minimize().run()
            self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11)
            self.assertAllClose(0.371705, loss.eval(), atol=0.01)
            predicted_labels = get_binary_predictions_for_logistic(predictions)
            self.assertAllEqual([0, 1], predicted_labels.eval())
            self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
    def testSimple(self):
        # Setup test data
        example_protos = [
            make_example_proto({"age": [0], "gender": [0]}, 0),
            make_example_proto({"age": [1], "gender": [1]}, 1),
        ]
        example_weights = [1.0, 1.0]
        with self._single_threaded_test_session():
            examples = make_example_dict(example_protos, example_weights)
            variables = make_variable_dict(1, 1)
            options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss")

            lr = SdcaModel(CONTAINER, examples, variables, options)
            tf.initialize_all_variables().run()
            unregularized_loss = lr.unregularized_loss(examples)
            loss = lr.regularized_loss(examples)
            predictions = lr.predictions(examples)
            self.assertAllClose(0.693147, unregularized_loss.eval())
            self.assertAllClose(0.693147, loss.eval())
            for _ in xrange(5):
                lr.minimize().run()
            # The high tolerance in unregularized_loss comparisons is due to the
            # fact that it's possible to trade off unregularized_loss vs.
            # regularization and still have a sum that is quite close to the
            # optimal regularized_loss value.  SDCA's duality gap only ensures that
            # the regularized_loss is within 0.01 of optimal.
            # 0.525457 is the optimal regularized_loss.
            # 0.411608 is the unregularized_loss at that optimum.
            self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11)
            self.assertAllClose(0.525457, loss.eval(), atol=0.01)
            predicted_labels = get_binary_predictions_for_logistic(predictions)
            self.assertAllEqual([0, 1], predicted_labels.eval())
            self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)

# 需要导入模块: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops import SdcaModel [as 别名]
# 或者: from tensorflow.contrib.linear_optimizer.python.ops.sdca_ops.SdcaModel import minimize [as 别名]
  def testDenseFeaturesWeightedExamples(self):
    with self._single_threaded_test_session():
      examples = make_dense_examples_dict(
          dense_feature_values=[[1.0, 1.0], [0.5, -0.5]],
          weights=[3.0, 1.0],
          labels=[1.0, 0.0])
      variables = make_dense_variable_dict(2, 2)
      options = dict(symmetric_l2_regularization=1.0,
                     symmetric_l1_regularization=0,
                     loss_type='hinge_loss')
      model = SdcaModel(CONTAINER, examples, variables, options)
      tf.initialize_all_variables().run()
      predictions = model.predictions(examples)
      binary_predictions = get_binary_predictions_for_hinge(predictions)
      for _ in xrange(5):
        model.minimize().run()

      # Point (1.0, 0.5) has higher weight than (1.0, -0.5) so the model will
      # try to increase the margin from (1.0, 0.5). Due to regularization,
      # (1.0, -0.5) will be within the margin. For these points and example
      # weights, the optimal weights are w_1~=0.4 and w_2~=1.2 which give an L2
      # loss of 0.5 * 0.25 * 0.25 * 1.6 = 0.2. The binary predictions will be
      # correct, but the boundary will be much closer to the 2nd point than the
      # first one.
      self.assertAllClose([1.0, -0.2], predictions.eval(), atol=0.05)
      self.assertAllClose([1.0, 0.0], binary_predictions.eval(), atol=0.05)
      unregularized_loss = model.unregularized_loss(examples)
      regularized_loss = model.regularized_loss(examples)
      self.assertAllClose(0.2, unregularized_loss.eval(), atol=0.02)
      self.assertAllClose(0.4, regularized_loss.eval(), atol=0.02)