path: root/keras-auc-optimizer.patch

From 901159da45695da24a5206125910f02fc50169ce Mon Sep 17 00:00:00 2001
From: Efraim Flashner <efraim@flashner.co.il>
Date: Thu, 23 Apr 2020 15:50:37 +0300
Subject: [PATCH] add keras metrics

---
 keras/backend/tensorflow_backend.py |  12 +
 keras/metrics.py                    | 584 ++++++++++++++++++++++++++++
 keras/utils/__init__.py             |   2 +
 keras/utils/losses_utils.py         | 177 +++++++++
 keras/utils/metrics_utils.py        | 278 +++++++++++++
 5 files changed, 1053 insertions(+)
 create mode 100644 keras/utils/losses_utils.py
 create mode 100644 keras/utils/metrics_utils.py

diff --git a/keras/backend/tensorflow_backend.py b/keras/backend/tensorflow_backend.py
index bcb8be0..a2870f5 100644
--- a/keras/backend/tensorflow_backend.py
+++ b/keras/backend/tensorflow_backend.py
@@ -4453,3 +4453,15 @@ def local_conv2d(inputs, kernel, kernel_size, strides, output_shape, data_format
     else:
         output = permute_dimensions(output, (2, 0, 1, 3))
     return output
+
+#get_graph = tf_keras_backend.get_graph
+
+#def is_symbolic(x):
+#    return isinstance(x, tf.Tensor) and hasattr(x, 'op')
+
+def size(x, name=None):
+#    if is_symbolic(x):
+#        with get_graph().as_default():
+#            return tf.size(x)
+    return tf.size(x, name=name)
+
diff --git a/keras/metrics.py b/keras/metrics.py
index 8e3df1f..8f57910 100644
--- a/keras/metrics.py
+++ b/keras/metrics.py
@@ -4,8 +4,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import abc
 import six
+import types
+
 from . import backend as K
+from .engine.base_layer import Layer
 from .losses import mean_squared_error
 from .losses import mean_absolute_error
 from .losses import mean_absolute_percentage_error
@@ -19,10 +23,201 @@ from .losses import binary_crossentropy
 from .losses import kullback_leibler_divergence
 from .losses import poisson
 from .losses import cosine_proximity
+from .utils import losses_utils
+from .utils import metrics_utils
 from .utils.generic_utils import deserialize_keras_object
 from .utils.generic_utils import serialize_keras_object
 
 
+@six.add_metaclass(abc.ABCMeta)
+class Metric(Layer):
+    """Encapsulates metric logic and state.
+
+    Standalone usage:
+    ```python
+    m = SomeMetric(...)
+    for input in ...:
+        m.update_state(input)
+    m.result()
+    ```
+
+    Usage with the `compile` API:
+    ```python
+    model.compile(optimizer='rmsprop',
+                  loss=keras.losses.categorical_crossentropy,
+                  metrics=[keras.metrics.CategoricalAccuracy()])
+    ```
+
+    To be implemented by subclasses:
+    * `__init__()`: All state variables should be created in this method by
+        calling `self.add_weight()` like: `self.var = self.add_weight(...)`
+    * `update_state()`: Has all updates to the state variables like:
+        self.var.assign_add(...).
+    * `result()`: Computes and returns a value for the metric
+        from the state variables.
+    """
+
+    def __init__(self, name=None, dtype=None, **kwargs):
+        super(Metric, self).__init__(name=name, dtype=dtype, **kwargs)
+        self.stateful = True  # All metric layers are stateful.
+        self.built = True
+        self.dtype = K.floatx() if dtype is None else dtype
+
+    def __new__(cls, *args, **kwargs):
+        obj = super(Metric, cls).__new__(cls)
+        update_state_fn = obj.update_state
+
+        obj.update_state = types.MethodType(
+            metrics_utils.update_state_wrapper(update_state_fn), obj)
+        return obj
+
+    def __call__(self, *args, **kwargs):
+        """Accumulates statistics and then computes metric result value."""
+        update_op = self.update_state(*args, **kwargs)
+        return self.result()
+
+    def get_config(self):
+        """Returns the serializable config of the metric."""
+        return {'name': self.name, 'dtype': self.dtype}
+
+    def reset_states(self):
+        """Resets all of the metric state variables.
+        This function is called between epochs/steps,
+        when a metric is evaluated during training.
+        """
+        K.batch_set_value([(v, 0) for v in self.weights])
+
+    @abc.abstractmethod
+    def update_state(self, *args, **kwargs):
+        """Accumulates statistics for the metric. """
+        raise NotImplementedError('Must be implemented in subclasses.')
+
+    @abc.abstractmethod
+    def result(self):
+        """Computes and returns the metric value tensor.
+        Result computation is an idempotent operation that simply calculates the
+        metric value using the state variables.
+        """
+        raise NotImplementedError('Must be implemented in subclasses.')
+
+    # For use by subclasses #
+    def add_weight(self,
+                   name,
+                   shape=(),
+                   initializer=None,
+                   dtype=None):
+        """Adds state variable. Only for use by subclasses."""
+        return super(Metric, self).add_weight(
+            name=name,
+            shape=shape,
+            dtype=self.dtype if dtype is None else dtype,
+            trainable=False,
+            initializer=initializer)
+
+    # End: For use by subclasses ###
+
+
+class Reduce(Metric):
+    """Encapsulates metrics that perform a reduce operation on the values."""
+
+    def __init__(self, reduction, name, dtype=None):
+        """Creates a `Reduce` instance.
+        # Arguments
+            reduction: a metrics `Reduction` enum value.
+            name: string name of the metric instance.
+            dtype: (Optional) data type of the metric result.
+        """
+        super(Reduce, self).__init__(name=name, dtype=dtype)
+        self.reduction = reduction
+        self.total = self.add_weight('total', initializer='zeros')
+        if reduction in [metrics_utils.Reduction.SUM_OVER_BATCH_SIZE,
+                         metrics_utils.Reduction.WEIGHTED_MEAN]:
+            self.count = self.add_weight('count', initializer='zeros')
+
+    def update_state(self, values, sample_weight=None):
+        """Accumulates statistics for computing the reduction metric.
+        For example, if `values` is [1, 3, 5, 7] and reduction=SUM_OVER_BATCH_SIZE,
+        then the value of `result()` is 4. If the `sample_weight` is specified as
+        [1, 1, 0, 0] then value of `result()` would be 2.
+        # Arguments
+            values: Per-example value.
+            sample_weight: Optional weighting of each example. Defaults to 1.
+        """
+        values = K.cast(values, self.dtype)
+        if sample_weight is not None:
+            sample_weight = K.cast(sample_weight, self.dtype)
+            # Update dimensions of weights to match with values if possible.
+            values, _, sample_weight = losses_utils.squeeze_or_expand_dimensions(
+                values, sample_weight=sample_weight)
+
+            # Broadcast weights if possible.
+            sample_weight = losses_utils.broadcast_weights(sample_weight, values)
+            values = values * sample_weight
+
+        value_sum = K.sum(values)
+        update_total_op = K.update_add(self.total, value_sum)
+
+        # Exit early if the reduction doesn't have a denominator.
+        if self.reduction == metrics_utils.Reduction.SUM:
+            return update_total_op
+
+        # Update `count` for reductions that require a denominator.
+        if self.reduction == metrics_utils.Reduction.SUM_OVER_BATCH_SIZE:
+            num_values = K.cast(K.size(values), self.dtype)
+        elif self.reduction == metrics_utils.Reduction.WEIGHTED_MEAN:
+            if sample_weight is None:
+                num_values = K.cast(K.size(values), self.dtype)
+            else:
+                num_values = K.sum(sample_weight)
+        else:
+            raise NotImplementedError(
+                'reduction [%s] not implemented' % self.reduction)
+
+        with K.control_dependencies([update_total_op]):
+            return K.update_add(self.count, num_values)
+
+    def result(self):
+        if self.reduction == metrics_utils.Reduction.SUM:
+            return self.total
+        elif self.reduction in [
+            metrics_utils.Reduction.WEIGHTED_MEAN,
+            metrics_utils.Reduction.SUM_OVER_BATCH_SIZE
+        ]:
+            return self.total / self.count
+        else:
+            raise NotImplementedError(
+                'reduction [%s] not implemented' % self.reduction)
+
+
+class Sum(Reduce):
+    """Computes the (weighted) sum of the given values.
+
+    For example, if values is [1, 3, 5, 7] then the sum is 16.
+    If the weights were specified as [1, 1, 0, 0] then the sum would be 4.
+
+    This metric creates one variable, `total`, that is used to compute the sum of
+    `values`. This is ultimately returned as `sum`.
+    If `sample_weight` is `None`, weights default to 1.  Use `sample_weight` of 0
+    to mask values.
+
+    Standalone usage:
+    ```python
+    m = keras.metrics.Sum()
+    m.update_state([1, 3, 5, 7])
+    m.result()
+    ```
+    """
+
+    def __init__(self, name='sum', dtype=None):
+        """Creates a `Sum` instance.
+        # Arguments
+            name: (Optional) string name of the metric instance.
+            dtype: (Optional) data type of the metric result.
+        """
+        super(Sum, self).__init__(reduction=metrics_utils.Reduction.SUM,
+                                  name=name, dtype=dtype)
+
+
 def binary_accuracy(y_true, y_pred):
     return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)
 
@@ -49,6 +244,395 @@ def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
     return K.mean(K.in_top_k(y_pred, K.cast(K.flatten(y_true), 'int32'), k),
                   axis=-1)
 
+class SensitivitySpecificityBase(Metric):
+    """Abstract base class for computing sensitivity and specificity.
+
+    For additional information about specificity and sensitivity, see the
+    following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity
+    """
+
+    def __init__(self, value, num_thresholds=200, name=None, dtype=None):
+        super(SensitivitySpecificityBase, self).__init__(name=name, dtype=dtype)
+        if num_thresholds <= 0:
+            raise ValueError('`num_thresholds` must be > 0.')
+        self.value = value
+        self.true_positives = self.add_weight(
+            'true_positives',
+            shape=(num_thresholds,),
+            initializer='zeros')
+        self.true_negatives = self.add_weight(
+            'true_negatives',
+            shape=(num_thresholds,),
+            initializer='zeros')
+        self.false_positives = self.add_weight(
+            'false_positives',
+            shape=(num_thresholds,),
+            initializer='zeros')
+        self.false_negatives = self.add_weight(
+            'false_negatives',
+            shape=(num_thresholds,),
+            initializer='zeros')
+
+        # Compute `num_thresholds` thresholds in [0, 1]
+        if num_thresholds == 1:
+            self.thresholds = [0.5]
+        else:
+            thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
+                          for i in range(num_thresholds - 2)]
+            self.thresholds = [0.0] + thresholds + [1.0]
+
+    def update_state(self, y_true, y_pred, sample_weight=None):
+        return metrics_utils.update_confusion_matrix_variables(
+            {
+                metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
+                metrics_utils.ConfusionMatrix.TRUE_NEGATIVES: self.true_negatives,
+                metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives,
+                metrics_utils.ConfusionMatrix.FALSE_NEGATIVES: self.false_negatives,
+            },
+            y_true,
+            y_pred,
+            thresholds=self.thresholds,
+            sample_weight=sample_weight)
+
+    def reset_states(self):
+        num_thresholds = len(self.thresholds)
+        K.batch_set_value(
+            [(v, np.zeros((num_thresholds,))) for v in self.variables])
+
+
+class SensitivityAtSpecificity(SensitivitySpecificityBase):
+    """Computes the sensitivity at a given specificity.
+
+    `Sensitivity` measures the proportion of actual positives that are correctly
+    identified as such (tp / (tp + fn)).
+    `Specificity` measures the proportion of actual negatives that are correctly
+    identified as such (tn / (tn + fp)).
+
+    This metric creates four local variables, `true_positives`, `true_negatives`,
+    `false_positives` and `false_negatives` that are used to compute the
+    sensitivity at the given specificity. The threshold for the given specificity
+    value is computed and used to evaluate the corresponding sensitivity.
+
+    If `sample_weight` is `None`, weights default to 1.
+    Use `sample_weight` of 0 to mask values.
+
+    For additional information about specificity and sensitivity, see the
+    following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity
+
+    Usage with the compile API:
+
+    ```python
+    model = keras.Model(inputs, outputs)
+    model.compile(
+        'sgd',
+        loss='mse',
+        metrics=[keras.metrics.SensitivityAtSpecificity()])
+    ```
+
+    # Arguments
+        specificity: A scalar value in range `[0, 1]`.
+        num_thresholds: (Optional) Defaults to 200. The number of thresholds to
+            use for matching the given specificity.
+        name: (Optional) string name of the metric instance.
+        dtype: (Optional) data type of the metric result.
+    """
+
+    def __init__(self, specificity, num_thresholds=200, name=None, dtype=None):
+        if specificity < 0 or specificity > 1:
+            raise ValueError('`specificity` must be in the range [0, 1].')
+        self.specificity = specificity
+        self.num_thresholds = num_thresholds
+        super(SensitivityAtSpecificity, self).__init__(
+            specificity, num_thresholds=num_thresholds, name=name, dtype=dtype)
+
+    def result(self):
+        # Calculate specificities at all the thresholds.
+        specificities = K.switch(
+            K.greater(self.true_negatives + self.false_positives, 0),
+            (self.true_negatives / (self.true_negatives + self.false_positives)),
+            K.zeros_like(self.thresholds))
+
+        # Find the index of the threshold where the specificity is closest to the
+        # given specificity.
+        min_index = K.argmin(
+            K.abs(specificities - self.value), axis=0)
+        min_index = K.cast(min_index, 'int32')
+
+        # Compute sensitivity at that index.
+        return K.switch(
+            K.greater((self.true_positives[min_index] +
+                       self.false_negatives[min_index]), 0),
+            (self.true_positives[min_index] /
+                (self.true_positives[min_index] + self.false_negatives[min_index])),
+            K.zeros_like(self.true_positives[min_index]))
+
+    def get_config(self):
+        config = {
+            'num_thresholds': self.num_thresholds,
+            'specificity': self.specificity
+        }
+        base_config = super(SensitivityAtSpecificity, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+
+class AUC(Metric):
+    """Computes the approximate AUC (Area under the curve) via a Riemann sum.
+
+    This metric creates four local variables, `true_positives`, `true_negatives`,
+    `false_positives` and `false_negatives` that are used to compute the AUC.
+    To discretize the AUC curve, a linearly spaced set of thresholds is used to
+    compute pairs of recall and precision values. The area under the ROC-curve is
+    therefore computed using the height of the recall values by the false positive
+    rate, while the area under the PR-curve is the computed using the height of
+    the precision values by the recall.
+
+    This value is ultimately returned as `auc`, an idempotent operation that
+    computes the area under a discretized curve of precision versus recall values
+    (computed using the aforementioned variables). The `num_thresholds` variable
+    controls the degree of discretization with larger numbers of thresholds more
+    closely approximating the true AUC. The quality of the approximation may vary
+    dramatically depending on `num_thresholds`. The `thresholds` parameter can be
+    used to manually specify thresholds which split the predictions more evenly.
+
+    For best results, `predictions` should be distributed approximately uniformly
+    in the range [0, 1] and not peaked around 0 or 1. The quality of the AUC
+    approximation may be poor if this is not the case. Setting `summation_method`
+    to 'minoring' or 'majoring' can help quantify the error in the approximation
+    by providing lower or upper bound estimate of the AUC.
+
+    If `sample_weight` is `None`, weights default to 1.
+    Use `sample_weight` of 0 to mask values.
+
+    Usage with the compile API:
+
+    ```python
+    model = keras.Model(inputs, outputs)
+    model.compile('sgd', loss='mse', metrics=[keras.metrics.AUC()])
+    ```
+
+    # Arguments
+        num_thresholds: (Optional) Defaults to 200. The number of thresholds to
+            use when discretizing the roc curve. Values must be > 1.
+            curve: (Optional) Specifies the name of the curve to be computed, 'ROC'
+            [default] or 'PR' for the Precision-Recall-curve.
+        summation_method: (Optional) Specifies the Riemann summation method used
+            (https://en.wikipedia.org/wiki/Riemann_sum): 'interpolation' [default],
+              applies mid-point summation scheme for `ROC`. For PR-AUC, interpolates
+              (true/false) positives but not the ratio that is precision (see Davis
+              & Goadrich 2006 for details); 'minoring' that applies left summation
+              for increasing intervals and right summation for decreasing intervals;
+              'majoring' that does the opposite.
+        name: (Optional) string name of the metric instance.
+        dtype: (Optional) data type of the metric result.
+        thresholds: (Optional) A list of floating point values to use as the
+            thresholds for discretizing the curve. If set, the `num_thresholds`
+            parameter is ignored. Values should be in [0, 1]. Endpoint thresholds
+            equal to {-epsilon, 1+epsilon} for a small positive epsilon value will
+            be automatically included with these to correctly handle predictions
+            equal to exactly 0 or 1.
+    """
+
+    def __init__(self,
+                 num_thresholds=200,
+                 curve='ROC',
+                 summation_method='interpolation',
+                 name=None,
+                 dtype=None,
+                 thresholds=None):
+        # Validate configurations.
+        if (isinstance(curve, metrics_utils.AUCCurve) and
+                curve not in list(metrics_utils.AUCCurve)):
+            raise ValueError('Invalid curve: "{}". Valid options are: "{}"'.format(
+                curve, list(metrics_utils.AUCCurve)))
+        if isinstance(
+            summation_method,
+            metrics_utils.AUCSummationMethod) and summation_method not in list(
+                metrics_utils.AUCSummationMethod):
+            raise ValueError(
+                'Invalid summation method: "{}". Valid options are: "{}"'.format(
+                    summation_method, list(metrics_utils.AUCSummationMethod)))
+
+        # Update properties.
+        if thresholds is not None:
+            # If specified, use the supplied thresholds.
+            self.num_thresholds = len(thresholds) + 2
+            thresholds = sorted(thresholds)
+        else:
+            if num_thresholds <= 1:
+                raise ValueError('`num_thresholds` must be > 1.')
+
+            # Otherwise, linearly interpolate (num_thresholds - 2) thresholds in
+            # (0, 1).
+            self.num_thresholds = num_thresholds
+            thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
+                          for i in range(num_thresholds - 2)]
+
+        # Add an endpoint "threshold" below zero and above one for either
+        # threshold method to account for floating point imprecisions.
+        self.thresholds = [0.0 - K.epsilon()] + thresholds + [1.0 + K.epsilon()]
+
+        if isinstance(curve, metrics_utils.AUCCurve):
+            self.curve = curve
+        else:
+            self.curve = metrics_utils.AUCCurve.from_str(curve)
+        if isinstance(summation_method, metrics_utils.AUCSummationMethod):
+            self.summation_method = summation_method
+        else:
+            self.summation_method = metrics_utils.AUCSummationMethod.from_str(
+                summation_method)
+        super(AUC, self).__init__(name=name, dtype=dtype)
+
+        # Create metric variables
+        self.true_positives = self.add_weight(
+            'true_positives',
+            shape=(self.num_thresholds,),
+            initializer='zeros')
+        self.true_negatives = self.add_weight(
+            'true_negatives',
+            shape=(self.num_thresholds,),
+            initializer='zeros')
+        self.false_positives = self.add_weight(
+            'false_positives',
+            shape=(self.num_thresholds,),
+            initializer='zeros')
+        self.false_negatives = self.add_weight(
+            'false_negatives',
+            shape=(self.num_thresholds,),
+            initializer='zeros')
+
+    def update_state(self, y_true, y_pred, sample_weight=None):
+        return metrics_utils.update_confusion_matrix_variables({
+            metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
+            metrics_utils.ConfusionMatrix.TRUE_NEGATIVES: self.true_negatives,
+            metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives,
+            metrics_utils.ConfusionMatrix.FALSE_NEGATIVES: self.false_negatives,
+        }, y_true, y_pred, self.thresholds, sample_weight=sample_weight)
+
+    def interpolate_pr_auc(self):
+        """Interpolation formula inspired by section 4 of Davis & Goadrich 2006.
+
+        https://www.biostat.wisc.edu/~page/rocpr.pdf
+
+        Note here we derive & use a closed formula not present in the paper
+        as follows:
+
+          Precision = TP / (TP + FP) = TP / P
+
+        Modeling all of TP (true positive), FP (false positive) and their sum
+        P = TP + FP (predicted positive) as varying linearly within each interval
+        [A, B] between successive thresholds, we get
+
+          Precision slope = dTP / dP
+                          = (TP_B - TP_A) / (P_B - P_A)
+                          = (TP - TP_A) / (P - P_A)
+          Precision = (TP_A + slope * (P - P_A)) / P
+
+        The area within the interval is (slope / total_pos_weight) times
+
+          int_A^B{Precision.dP} = int_A^B{(TP_A + slope * (P - P_A)) * dP / P}
+          int_A^B{Precision.dP} = int_A^B{slope * dP + intercept * dP / P}
+
+        where intercept = TP_A - slope * P_A = TP_B - slope * P_B, resulting in
+
+          int_A^B{Precision.dP} = TP_B - TP_A + intercept * log(P_B / P_A)
+
+        Bringing back the factor (slope / total_pos_weight) we'd put aside, we get
+
+          slope * [dTP + intercept *  log(P_B / P_A)] / total_pos_weight
+
+        where dTP == TP_B - TP_A.
+
+        Note that when P_A == 0 the above calculation simplifies into
+
+          int_A^B{Precision.dTP} = int_A^B{slope * dTP} = slope * (TP_B - TP_A)
+
+        which is really equivalent to imputing constant precision throughout the
+        first bucket having >0 true positives.
+
+        # Returns
+            pr_auc: an approximation of the area under the P-R curve.
+        """
+        dtp = self.true_positives[:self.num_thresholds -
+                                  1] - self.true_positives[1:]
+        p = self.true_positives + self.false_positives
+        dp = p[:self.num_thresholds - 1] - p[1:]
+
+        prec_slope = dtp / K.maximum(dp, 0)
+        intercept = self.true_positives[1:] - (prec_slope * p[1:])
+
+        # Logical and
+        pMin = K.expand_dims(p[:self.num_thresholds - 1] > 0, 0)
+        pMax = K.expand_dims(p[1:] > 0, 0)
+        are_different = K.concatenate([pMin, pMax], axis=0)
+        switch_condition = K.all(are_different, axis=0)
+
+        safe_p_ratio = K.switch(
+            switch_condition,
+            p[:self.num_thresholds - 1] / K.maximum(p[1:], 0),
+            K.ones_like(p[1:]))
+
+        numer = prec_slope * (dtp + intercept * K.log(safe_p_ratio))
+        denom = K.maximum(self.true_positives[1:] + self.false_negatives[1:], 0)
+        return K.sum((numer / denom))
+
+    def result(self):
+        if (self.curve == metrics_utils.AUCCurve.PR and
+                (self.summation_method ==
+                 metrics_utils.AUCSummationMethod.INTERPOLATION)):
+            # This use case is different and is handled separately.
+            return self.interpolate_pr_auc()
+
+        # Set `x` and `y` values for the curves based on `curve` config.
+        recall = K.switch(
+            K.greater((self.true_positives), 0),
+            (self.true_positives /
+                (self.true_positives + self.false_negatives)),
+            K.zeros_like(self.true_positives))
+        if self.curve == metrics_utils.AUCCurve.ROC:
+            fp_rate = K.switch(
+                K.greater((self.false_positives), 0),
+                (self.false_positives /
+                    (self.false_positives + self.true_negatives)),
+                K.zeros_like(self.false_positives))
+            x = fp_rate
+            y = recall
+        else:  # curve == 'PR'.
+            precision = K.switch(
+                K.greater((self.true_positives), 0),
+                (self.true_positives / (self.true_positives + self.false_positives)),
+                K.zeros_like(self.true_positives))
+            x = recall
+            y = precision
+
+        # Find the rectangle heights based on `summation_method`.
+        if self.summation_method == metrics_utils.AUCSummationMethod.INTERPOLATION:
+            # Note: the case ('PR', 'interpolation') has been handled above.
+            heights = (y[:self.num_thresholds - 1] + y[1:]) / 2.
+        elif self.summation_method == metrics_utils.AUCSummationMethod.MINORING:
+            heights = K.minimum(y[:self.num_thresholds - 1], y[1:])
+        else:  # self.summation_method = metrics_utils.AUCSummationMethod.MAJORING:
+            heights = K.maximum(y[:self.num_thresholds - 1], y[1:])
+
+        # Sum up the areas of all the rectangles.
+        return K.sum((x[:self.num_thresholds - 1] - x[1:]) * heights)
+
+    def reset_states(self):
+        K.batch_set_value(
+            [(v, np.zeros((self.num_thresholds,))) for v in self.variables])
+
+    def get_config(self):
+        config = {
+            'num_thresholds': self.num_thresholds,
+            'curve': self.curve.value,
+            'summation_method': self.summation_method.value,
+            # We remove the endpoint thresholds as an inverse of how the thresholds
+            # were initialized. This ensures that a metric initialized from this
+            # config has the same thresholds.
+            'thresholds': self.thresholds[1:-1],
+        }
+        base_config = super(AUC, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
 
 # Aliases
 
diff --git a/keras/utils/__init__.py b/keras/utils/__init__.py
index 8cc39d5..65af329 100644
--- a/keras/utils/__init__.py
+++ b/keras/utils/__init__.py
@@ -4,6 +4,8 @@ from . import generic_utils
 from . import data_utils
 from . import io_utils
 from . import conv_utils
+from . import losses_utils
+from . import metrics_utils
 
 # Globally-importable utils.
 from .io_utils import HDF5Matrix
diff --git a/keras/utils/losses_utils.py b/keras/utils/losses_utils.py
new file mode 100644
index 0000000..617ebb7
--- /dev/null
+++ b/keras/utils/losses_utils.py
@@ -0,0 +1,177 @@
+"""Utilities related to losses."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from .. import backend as K
+
+
+class Reduction(object):
+    """Types of loss reduction.
+
+    Contains the following values:
+
+    * `NONE`: Un-reduced weighted losses with the same shape as input. When this
+        reduction type used with built-in Keras training loops like
+        `fit`/`evaluate`, the unreduced vector loss is passed to the optimizer but
+        the reported loss will be a scalar value.
+    * `SUM`: Scalar sum of weighted losses.
+    * `SUM_OVER_BATCH_SIZE`: Scalar `SUM` divided by number of elements in losses.
+    """
+
+    NONE = 'none'
+    SUM = 'sum'
+    SUM_OVER_BATCH_SIZE = 'sum_over_batch_size'
+
+    @classmethod
+    def all(cls):
+        return (cls.NONE, cls.SUM, cls.SUM_OVER_BATCH_SIZE)
+
+    @classmethod
+    def validate(cls, key):
+        if key not in cls.all():
+            raise ValueError('Invalid Reduction Key %s.' % key)
+
+
+def squeeze_or_expand_dimensions(y_pred, y_true=None, sample_weight=None):
+    """Squeeze or expand last dimension if needed.
+
+    1. Squeezes last dim of `y_pred` or `y_true` if their rank differs by 1.
+    2. Squeezes or expands last dim of `sample_weight` if its rank differs by 1
+    from the new rank of `y_pred`.
+    If `sample_weight` is scalar, it is kept scalar.
+
+    # Arguments
+    y_pred: Predicted values, a `Tensor` of arbitrary dimensions.
+    y_true: Optional label `Tensor` whose dimensions match `y_pred`.
+    sample_weight: Optional weight scalar or `Tensor` whose dimensions match
+    `y_pred`.
+
+    # Returns
+    Tuple of `y_pred`, `y_true` and `sample_weight`. Each of them possibly has
+    the last dimension squeezed, `sample_weight` could be extended by one
+    dimension.
+    """
+    if y_true is not None:
+        y_pred_rank = K.ndim(y_pred)
+        y_pred_shape = K.int_shape(y_pred)
+        y_true_rank = K.ndim(y_true)
+        y_true_shape = K.int_shape(y_true)
+
+        if (y_pred_rank - y_true_rank == 1) and (y_pred_shape[-1] == 1):
+            y_pred = K.squeeze(y_pred, -1)
+        elif (y_true_rank - y_pred_rank == 1) and (y_true_shape[-1] == 1):
+            y_true = K.squeeze(y_true, -1)
+
+    if sample_weight is None:
+        return y_pred, y_true
+
+    y_pred_rank = K.ndim(y_pred)
+    weights_rank = K.ndim(sample_weight)
+    if weights_rank != 0:
+        if weights_rank - y_pred_rank == 1:
+            sample_weight = K.squeeze(sample_weight, -1)
+        elif y_pred_rank - weights_rank == 1:
+           sample_weight = K.expand_dims(sample_weight, -1)
+    return y_pred, y_true, sample_weight
+
+
+def _num_elements(losses):
+    """Computes the number of elements in `losses` tensor."""
+    with K.name_scope('num_elements') as scope:
+        return K.cast(K.size(losses, name=scope), losses.dtype)
+
+
+def reduce_weighted_loss(weighted_losses, reduction=Reduction.SUM_OVER_BATCH_SIZE):
+    """Reduces the individual weighted loss measurements."""
+    if reduction == Reduction.NONE:
+        loss = weighted_losses
+    else:
+        loss = K.sum(weighted_losses)
+        if reduction == Reduction.SUM_OVER_BATCH_SIZE:
+            loss = loss / _num_elements(weighted_losses)
+    return loss
+
+
+def broadcast_weights(values, sample_weight):
+    # Broadcast weights if possible.
+    weights_shape = K.int_shape(sample_weight)
+    values_shape = K.int_shape(values)
+
+    if values_shape != weights_shape:
+        weights_rank = K.ndim(sample_weight)
+        values_rank = K.ndim(values)
+
+        # Raise error if ndim of weights is > values.
+        if weights_rank > values_rank:
+            raise ValueError(
+                'Incompatible shapes: `values` {} vs `sample_weight` {}'.format(
+                    values_shape, weights_shape))
+
+        # Expand dim of weights to match ndim of values, if required.
+            for i in range(weights_rank, values_rank):
+                sample_weight = K.expand_dims(sample_weight, axis=i)
+
+        if weights_shape is not None and values_shape is not None:
+            for i in range(weights_rank):
+                if (weights_shape[i] is not None and
+                   values_shape[i] is not None and
+                       weights_shape[i] != values_shape[i]):
+                   # Cannot be broadcasted.
+                   if weights_shape[i] != 1:
+                       raise ValueError(
+                           'Incompatible shapes: `values` {} vs '
+                           '`sample_weight` {}'.format(
+                               values_shape, weights_shape))
+                   sample_weight = K.repeat_elements(
+                       sample_weight, values_shape[i], axis=i)
+    return sample_weight
+
+
+def compute_weighted_loss(losses,
+                          sample_weight=None,
+                          reduction=Reduction.SUM_OVER_BATCH_SIZE,
+                          name=None):
+    """Computes the weighted loss.
+
+    # Arguments
+        losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
+        sample_weight: Optional `Tensor` whose rank is either 0, or the same rank as
+        `   losses`, or be broadcastable to `losses`.
+        reduction: (Optional) Type of Reduction to apply to loss.
+            Default value is `SUM_OVER_BATCH_SIZE`.
+        name: Optional name for the op.
+
+    # Raises
+        ValueError: If the shape of `sample_weight` is not compatible with `losses`.
+
+    # Returns
+        Weighted loss `Tensor` of the same type as `losses`. If `reduction` is
+            `NONE`, this has the same shape as `losses`; otherwise, it is scalar.
+    """
+    Reduction.validate(reduction)
+    if sample_weight is None:
+        sample_weight = 1.0
+    with K.name_scope(name or 'weighted_loss'):
+        input_dtype = K.dtype(losses)
+        losses = K.cast(losses, K.floatx())
+        sample_weight = K.cast(sample_weight, K.floatx())
+
+        # Update dimensions of `sample_weight` to match with `losses` if possible.
+        losses, _, sample_weight = squeeze_or_expand_dimensions(
+            losses, None, sample_weight)
+
+        # Broadcast weights if possible.
+        sample_weight = broadcast_weights(losses, sample_weight)
+
+        # Apply weights to losses.
+        weighted_losses = sample_weight * losses
+
+        # Apply reduction function to the individual weighted losses.
+        loss = reduce_weighted_loss(weighted_losses, reduction)
+        # Convert the result back to the input type.
+        loss = K.cast(loss, input_dtype)
+        return loss
+
diff --git a/keras/utils/metrics_utils.py b/keras/utils/metrics_utils.py
new file mode 100644
index 0000000..e6a5bb0
--- /dev/null
+++ b/keras/utils/metrics_utils.py
@@ -0,0 +1,278 @@
+"""Utilities related to metrics."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from enum import Enum
+
+from .. import backend as K
+from . import losses_utils
+
+NEG_INF = -1e10
+
+class Reduction(object):
+    """Types of metrics reduction.
+    Contains the following values:
+    * `SUM`: Scalar sum of weighted values.
+    * `SUM_OVER_BATCH_SIZE`: Scalar `SUM` of weighted values divided by
+        number of elements in values.
+    * `WEIGHTED_MEAN`: Scalar sum of weighted values divided by sum of weights.
+    """
+
+    SUM = 'sum'
+    SUM_OVER_BATCH_SIZE = 'sum_over_batch_size'
+    WEIGHTED_MEAN = 'weighted_mean'
+
+
+def update_state_wrapper(update_state_fn):
+    """Decorator to wrap metric `update_state()` with `add_update()`.
+    # Arguments
+        update_state_fn: function that accumulates metric statistics.
+    # Returns
+        Decorated function that wraps `update_state_fn()` with `add_update()`.
+    """
+    def decorated(metric_obj, *args, **kwargs):
+        """Decorated function with `add_update()`."""
+
+        update_op = update_state_fn(*args, **kwargs)
+        metric_obj.add_update(update_op)
+        return update_op
+
+    return decorated
+
+def result_wrapper(result_fn):
+    """Decorator to wrap metric `result()` with identity op.
+    Wrapping result in identity so that control dependency between
+    update_op from `update_state` and result works in case result returns
+    a tensor.
+    # Arguments
+        result_fn: function that computes the metric result.
+    # Returns
+        Decorated function that wraps `result()` with identity op.
+    """
+    def decorated(metric_obj, *args, **kwargs):
+        result_t = K.identity(result_fn(*args, **kwargs))
+        metric_obj._call_result = result_t
+        result_t._is_metric = True
+        return result_t
+    return decorated
+
+
+def to_list(x):
+    if isinstance(x, list):
+        return x
+    return [x]
+
+
+def assert_thresholds_range(thresholds):
+    if thresholds is not None:
+        invalid_thresholds = [t for t in thresholds if t is None or t < 0 or t > 1]
+    if invalid_thresholds:
+        raise ValueError(
+            'Threshold values must be in [0, 1]. Invalid values: {}'.format(
+            invalid_thresholds))
+
+
+def parse_init_thresholds(thresholds, default_threshold=0.5):
+    if thresholds is not None:
+        assert_thresholds_range(to_list(thresholds))
+    thresholds = to_list(default_threshold if thresholds is None else thresholds)
+    return thresholds
+
+class ConfusionMatrix(Enum):
+    TRUE_POSITIVES = 'tp'
+    FALSE_POSITIVES = 'fp'
+    TRUE_NEGATIVES = 'tn'
+    FALSE_NEGATIVES = 'fn'
+
+class AUCCurve(Enum):
+    """Type of AUC Curve (ROC or PR)."""
+    ROC = 'ROC'
+    PR = 'PR'
+
+    @staticmethod
+    def from_str(key):
+        if key in ('pr', 'PR'):
+            return AUCCurve.PR
+        elif key in ('roc', 'ROC'):
+            return AUCCurve.ROC
+        else:
+            raise ValueError('Invalid AUC curve value "%s".' % key)
+
+
+class AUCSummationMethod(Enum):
+    """Type of AUC summation method.
+
+    https://en.wikipedia.org/wiki/Riemann_sum)
+
+    Contains the following values:
+    * 'interpolation': Applies mid-point summation scheme for `ROC` curve. For
+    `PR` curve, interpolates (true/false) positives but not the ratio that is
+    precision (see Davis & Goadrich 2006 for details).
+    * 'minoring': Applies left summation for increasing intervals and right
+    summation for decreasing intervals.
+    * 'majoring': Applies right summation for increasing intervals and left
+    summation for decreasing intervals.
+    """
+    INTERPOLATION = 'interpolation'
+    MAJORING = 'majoring'
+    MINORING = 'minoring'
+
+    @staticmethod
+    def from_str(key):
+        if key in ('interpolation', 'Interpolation'):
+            return AUCSummationMethod.INTERPOLATION
+        elif key in ('majoring', 'Majoring'):
+            return AUCSummationMethod.MAJORING
+        elif key in ('minoring', 'Minoring'):
+            return AUCSummationMethod.MINORING
+        else:
+            raise ValueError('Invalid AUC summation method value "%s".' % key)
+
+def weighted_assign_add(label, pred, weights, var):
+    # Logical and
+    label = K.expand_dims(label, 0)
+    pred = K.expand_dims(pred, 0)
+    are_different = K.concatenate([label, pred], axis=0)
+    label_and_pred = K.all(are_different, axis=0)
+    label_and_pred = K.cast(label_and_pred, dtype=K.floatx())
+    if weights is not None:
+        label_and_pred *= weights
+    return var.assign_add(K.sum(label_and_pred, 1))
+
+def update_confusion_matrix_variables(variables_to_update,
+                                      y_true,
+                                      y_pred,
+                                      thresholds,
+                                      top_k=None,
+                                      class_id=None,
+                                      sample_weight=None):
+    """Returns op to update the given confusion matrix variables.
+    For every pair of values in y_true and y_pred:
+    true_positive: y_true == True and y_pred > thresholds
+    false_negatives: y_true == True and y_pred <= thresholds
+    true_negatives: y_true == False and y_pred <= thresholds
+    false_positive: y_true == False and y_pred > thresholds
+    The results will be weighted and added together. When multiple thresholds are
+    provided, we will repeat the same for every threshold.
+    For estimation of these metrics over a stream of data, the function creates an
+    `update_op` operation that updates the given variables.
+    If `sample_weight` is `None`, weights default to 1.
+    Use weights of 0 to mask values.
+    # Arguments
+    variables_to_update: Dictionary with 'tp', 'fn', 'tn', 'fp' as valid keys
+      and corresponding variables to update as values.
+    y_true: A `Tensor` whose shape matches `y_pred`. Will be cast to `bool`.
+    y_pred: A floating point `Tensor` of arbitrary shape and whose values are in
+      the range `[0, 1]`.
+    thresholds: A float value or a python list or tuple of float thresholds in
+      `[0, 1]`, or NEG_INF (used when top_k is set).
+    top_k: Optional int, indicates that the positive labels should be limited to
+      the top k predictions.
+    class_id: Optional int, limits the prediction and labels to the class
+      specified by this argument.
+    sample_weight: Optional `Tensor` whose rank is either 0, or the same rank as
+      `y_true`, and must be broadcastable to `y_true` (i.e., all dimensions must
+      be either `1`, or the same as the corresponding `y_true` dimension).
+    # Returns
+        Update ops.
+    # Raises
+        ValueError: If `y_pred` and `y_true` have mismatched shapes, or if
+            `sample_weight` is not `None` and its shape doesn't match `y_pred`, or if
+            `variables_to_update` contains invalid keys.
+    """
+    if variables_to_update is None:
+        return
+    y_true = K.cast(y_true, dtype=K.floatx())
+    y_pred = K.cast(y_pred, dtype=K.floatx())
+    if sample_weight is not None:
+        sample_weight = K.cast(sample_weight, dtype=K.floatx())
+
+    if not any(key
+               for key in variables_to_update
+               if key in list(ConfusionMatrix)):
+        raise ValueError(
+            'Please provide at least one valid confusion matrix '
+            'variable to update. Valid variable key options are: "{}". '
+            'Received: "{}"'.format(
+                list(ConfusionMatrix), variables_to_update.keys()))
+
+    invalid_keys = [
+        key for key in variables_to_update if key not in list(ConfusionMatrix)
+    ]
+    if invalid_keys:
+        raise ValueError(
+            'Invalid keys: {}. Valid variable key options are: "{}"'.format(
+                invalid_keys, list(ConfusionMatrix)))
+
+    if sample_weight is None:
+        y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
+            y_pred, y_true=y_true)
+    else:
+        y_pred, y_true, sample_weight = (
+            losses_utils.squeeze_or_expand_dimensions(
+                y_pred, y_true=y_true, sample_weight=sample_weight))
+
+    if top_k is not None:
+        y_pred = _filter_top_k(y_pred, top_k)
+    if class_id is not None:
+        y_true = y_true[..., class_id]
+        y_pred = y_pred[..., class_id]
+
+    thresholds = to_list(thresholds)
+    num_thresholds = len(thresholds)
+    num_predictions = K.size(y_pred)
+
+    # Reshape predictions and labels.
+    predictions_2d = K.reshape(y_pred, [1, -1])
+    labels_2d = K.reshape(
+        K.cast(y_true, dtype='bool'), [1, -1])
+
+    # Tile the thresholds for every prediction.
+    thresh_tiled = K.tile(
+        K.expand_dims(K.constant(thresholds), 1),
+        K.stack([1, num_predictions]))
+
+    # Tile the predictions for every threshold.
+    preds_tiled = K.tile(predictions_2d, [num_thresholds, 1])
+
+    # Compare predictions and threshold.
+    pred_is_pos = K.greater(preds_tiled, thresh_tiled)
+    pred_is_neg = K.greater(thresh_tiled, preds_tiled)
+
+    # Tile labels by number of thresholds
+    label_is_pos = K.tile(labels_2d, [num_thresholds, 1])
+
+    if sample_weight is not None:
+        weights = losses_utils.broadcast_weights(
+            y_pred, K.cast(sample_weight, dtype=K.floatx()))
+        weights_tiled = K.tile(
+            K.reshape(weights, [1, -1]), [num_thresholds, 1])
+    else:
+        weights_tiled = None
+
+    update_ops = []
+    loop_vars = {
+        ConfusionMatrix.TRUE_POSITIVES: (label_is_pos, pred_is_pos),
+    }
+    update_tn = ConfusionMatrix.TRUE_NEGATIVES in variables_to_update
+    update_fp = ConfusionMatrix.FALSE_POSITIVES in variables_to_update
+    update_fn = ConfusionMatrix.FALSE_NEGATIVES in variables_to_update
+
+    if update_fn or update_tn:
+        loop_vars[ConfusionMatrix.FALSE_NEGATIVES] = (label_is_pos, pred_is_neg)
+
+    if update_fp or update_tn:
+        label_is_neg = K.equal(
+            label_is_pos, K.zeros_like(label_is_pos, dtype=label_is_pos.dtype))
+        loop_vars[ConfusionMatrix.FALSE_POSITIVES] = (label_is_neg, pred_is_pos)
+    if update_tn:
+        loop_vars[ConfusionMatrix.TRUE_NEGATIVES] = (label_is_neg, pred_is_neg)
+
+    for matrix_cond, (label, pred) in loop_vars.items():
+        if matrix_cond in variables_to_update:
+            update_ops.append(
+                weighted_assign_add(label, pred, weights_tiled,
+                                    variables_to_update[matrix_cond]))
+    return update_ops
+
-- 
2.26.2