3704 lines
128 KiB
Python
3704 lines
128 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=g-classes-have-attributes
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# pylint: disable=g-doc-return-or-yield
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"""Built-in metrics."""
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import abc
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import types
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import warnings
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import numpy as np
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from tensorflow.python.autograph.core import ag_ctx
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from tensorflow.python.autograph.impl import api as autograph
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from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx
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from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.keras import activations
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from tensorflow.python.keras import backend
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from tensorflow.python.keras.engine import base_layer
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from tensorflow.python.keras.engine import base_layer_utils
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from tensorflow.python.keras.engine import keras_tensor
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from tensorflow.python.keras.losses import binary_crossentropy
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from tensorflow.python.keras.losses import categorical_crossentropy
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from tensorflow.python.keras.losses import categorical_hinge
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from tensorflow.python.keras.losses import hinge
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from tensorflow.python.keras.losses import kullback_leibler_divergence
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from tensorflow.python.keras.losses import logcosh
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from tensorflow.python.keras.losses import mean_absolute_error
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from tensorflow.python.keras.losses import mean_absolute_percentage_error
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from tensorflow.python.keras.losses import mean_squared_error
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from tensorflow.python.keras.losses import mean_squared_logarithmic_error
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from tensorflow.python.keras.losses import poisson
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from tensorflow.python.keras.losses import sparse_categorical_crossentropy
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from tensorflow.python.keras.losses import squared_hinge
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from tensorflow.python.keras.saving.saved_model import metric_serialization
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from tensorflow.python.keras.utils import generic_utils
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from tensorflow.python.keras.utils import losses_utils
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from tensorflow.python.keras.utils import metrics_utils
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from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
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from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
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from tensorflow.python.keras.utils.generic_utils import to_list
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from tensorflow.python.keras.utils.tf_utils import is_tensor_or_variable
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import confusion_matrix
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import variables as variables_module
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from tensorflow.python.ops import weights_broadcast_ops
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from tensorflow.python.util import dispatch
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from tensorflow.python.util import nest
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from tensorflow.python.util.tf_export import keras_export
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from tensorflow.tools.docs import doc_controls
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@keras_export('keras.metrics.Metric')
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class Metric(base_layer.Layer, metaclass=abc.ABCMeta):
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"""Encapsulates metric logic and state.
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Args:
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name: (Optional) string name of the metric instance.
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dtype: (Optional) data type of the metric result.
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**kwargs: Additional layer keywords arguments.
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Standalone usage:
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```python
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m = SomeMetric(...)
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for input in ...:
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m.update_state(input)
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print('Final result: ', m.result().numpy())
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```
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Usage with `compile()` API:
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```python
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model = tf.keras.Sequential()
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model.add(tf.keras.layers.Dense(64, activation='relu'))
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model.add(tf.keras.layers.Dense(64, activation='relu'))
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model.add(tf.keras.layers.Dense(10, activation='softmax'))
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model.compile(optimizer=tf.keras.optimizers.RMSprop(0.01),
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loss=tf.keras.losses.CategoricalCrossentropy(),
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metrics=[tf.keras.metrics.CategoricalAccuracy()])
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data = np.random.random((1000, 32))
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labels = np.random.random((1000, 10))
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dataset = tf.data.Dataset.from_tensor_slices((data, labels))
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dataset = dataset.batch(32)
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model.fit(dataset, epochs=10)
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```
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To be implemented by subclasses:
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* `__init__()`: All state variables should be created in this method by
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calling `self.add_weight()` like: `self.var = self.add_weight(...)`
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* `update_state()`: Has all updates to the state variables like:
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self.var.assign_add(...).
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* `result()`: Computes and returns a value for the metric
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from the state variables.
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Example subclass implementation:
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```python
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class BinaryTruePositives(tf.keras.metrics.Metric):
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def __init__(self, name='binary_true_positives', **kwargs):
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super(BinaryTruePositives, self).__init__(name=name, **kwargs)
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self.true_positives = self.add_weight(name='tp', initializer='zeros')
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def update_state(self, y_true, y_pred, sample_weight=None):
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y_true = tf.cast(y_true, tf.bool)
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y_pred = tf.cast(y_pred, tf.bool)
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values = tf.logical_and(tf.equal(y_true, True), tf.equal(y_pred, True))
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values = tf.cast(values, self.dtype)
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if sample_weight is not None:
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sample_weight = tf.cast(sample_weight, self.dtype)
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sample_weight = tf.broadcast_to(sample_weight, values.shape)
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values = tf.multiply(values, sample_weight)
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self.true_positives.assign_add(tf.reduce_sum(values))
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def result(self):
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return self.true_positives
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```
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"""
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def __init__(self, name=None, dtype=None, **kwargs):
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super(Metric, self).__init__(name=name, dtype=dtype, **kwargs)
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self.stateful = True # All metric layers are stateful.
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self.built = True
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if not base_layer_utils.v2_dtype_behavior_enabled():
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# We only do this when the V2 behavior is not enabled, as when it is
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# enabled, the dtype already defaults to floatx.
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self._dtype = (backend.floatx() if dtype is None
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else dtypes.as_dtype(dtype).name)
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def __new__(cls, *args, **kwargs):
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obj = super(Metric, cls).__new__(cls)
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# If `update_state` is not in eager/tf.function and it is not from a
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# built-in metric, wrap it in `tf.function`. This is so that users writing
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# custom metrics in v1 need not worry about control dependencies and
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# return ops.
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if (base_layer_utils.is_in_eager_or_tf_function() or
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is_built_in(cls)):
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obj_update_state = obj.update_state
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def update_state_fn(*args, **kwargs):
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control_status = ag_ctx.control_status_ctx()
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ag_update_state = autograph.tf_convert(obj_update_state, control_status)
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return ag_update_state(*args, **kwargs)
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else:
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if isinstance(obj.update_state, def_function.Function):
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update_state_fn = obj.update_state
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else:
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update_state_fn = def_function.function(obj.update_state)
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obj.update_state = types.MethodType(
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metrics_utils.update_state_wrapper(update_state_fn), obj)
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obj_result = obj.result
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def result_fn(*args, **kwargs):
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control_status = ag_ctx.control_status_ctx()
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ag_result = autograph.tf_convert(obj_result, control_status)
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return ag_result(*args, **kwargs)
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obj.result = types.MethodType(metrics_utils.result_wrapper(result_fn), obj)
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return obj
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def __call__(self, *args, **kwargs):
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"""Accumulates statistics and then computes metric result value.
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Args:
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*args:
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**kwargs: A mini-batch of inputs to the Metric,
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passed on to `update_state()`.
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Returns:
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The metric value tensor.
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"""
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def replica_local_fn(*args, **kwargs):
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"""Updates the state of the metric in a replica-local context."""
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if any(
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isinstance(arg, keras_tensor.KerasTensor)
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for arg in nest.flatten((args, kwargs))):
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update_op = None
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else:
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update_op = self.update_state(*args, **kwargs) # pylint: disable=not-callable
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update_ops = []
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if update_op is not None:
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update_ops.append(update_op)
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with ops.control_dependencies(update_ops):
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result_t = self.result() # pylint: disable=not-callable
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# We are adding the metric object as metadata on the result tensor.
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# This is required when we want to use a metric with `add_metric` API on
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# a Model/Layer in graph mode. This metric instance will later be used
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# to reset variable state after each epoch of training.
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# Example:
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# model = Model()
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# mean = Mean()
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# model.add_metric(mean(values), name='mean')
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result_t._metric_obj = self # pylint: disable=protected-access
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return result_t
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from tensorflow.python.keras.distribute import distributed_training_utils # pylint:disable=g-import-not-at-top
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return distributed_training_utils.call_replica_local_fn(
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replica_local_fn, *args, **kwargs)
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@property
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def dtype(self):
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return self._dtype
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def get_config(self):
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"""Returns the serializable config of the metric."""
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return {'name': self.name, 'dtype': self.dtype}
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def reset_state(self):
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"""Resets all of the metric state variables.
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This function is called between epochs/steps,
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when a metric is evaluated during training.
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"""
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if not generic_utils.is_default(self.reset_states):
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warnings.warn('Metric %s implements a `reset_states()` method; rename it '
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'to `reset_state()` (without the final "s"). The name '
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'`reset_states()` has been deprecated to improve API '
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'consistency.' % (self.__class__.__name__,))
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return self.reset_states()
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else:
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backend.batch_set_value([(v, 0) for v in self.variables])
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@abc.abstractmethod
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def update_state(self, *args, **kwargs):
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"""Accumulates statistics for the metric.
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Note: This function is executed as a graph function in graph mode.
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This means:
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a) Operations on the same resource are executed in textual order.
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This should make it easier to do things like add the updated
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value of a variable to another, for example.
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b) You don't need to worry about collecting the update ops to execute.
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All update ops added to the graph by this function will be executed.
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As a result, code should generally work the same way with graph or
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eager execution.
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Args:
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*args:
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**kwargs: A mini-batch of inputs to the Metric.
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"""
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raise NotImplementedError('Must be implemented in subclasses.')
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@abc.abstractmethod
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def result(self):
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"""Computes and returns the metric value tensor.
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Result computation is an idempotent operation that simply calculates the
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metric value using the state variables.
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"""
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raise NotImplementedError('Must be implemented in subclasses.')
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### For use by subclasses ###
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@doc_controls.for_subclass_implementers
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def add_weight(
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self,
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name,
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shape=(),
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aggregation=variables_module.VariableAggregation.SUM,
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synchronization=variables_module.VariableSynchronization.ON_READ,
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initializer=None,
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dtype=None):
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"""Adds state variable. Only for use by subclasses."""
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if distribute_ctx.has_strategy():
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strategy = distribute_ctx.get_strategy()
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else:
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strategy = None
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# TODO(b/120571621): Make `ON_READ` work with Keras metrics on TPU.
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if backend.is_tpu_strategy(strategy):
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synchronization = variables_module.VariableSynchronization.ON_WRITE
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with ops.init_scope():
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return super(Metric, self).add_weight(
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name=name,
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shape=shape,
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dtype=self._dtype if dtype is None else dtype,
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trainable=False,
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initializer=initializer,
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collections=[],
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synchronization=synchronization,
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aggregation=aggregation)
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### End: For use by subclasses ###
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@property
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def trainable_weights(self):
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# Overridden from Layer class to track submetric weights.
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if self.trainable:
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trainable_weights = self._trainable_weights
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for m in self._metrics:
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trainable_weights += m.trainable_weights
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return self._dedup_weights(trainable_weights)
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else:
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return []
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@property
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def non_trainable_weights(self):
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# Overridden from Layer class to track submetric weights.
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if self.trainable:
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non_trainable_weights = self._non_trainable_weights
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for m in self._metrics:
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non_trainable_weights += m.non_trainable_weights
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else:
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non_trainable_weights = (
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self._non_trainable_weights + self._trainable_weights)
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for m in self._metrics:
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non_trainable_weights += m.weights
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return self._dedup_weights(non_trainable_weights)
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@property
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def _trackable_saved_model_saver(self):
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return metric_serialization.MetricSavedModelSaver(self)
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@generic_utils.default
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@doc_controls.do_not_generate_docs
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def reset_states(self):
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# Backwards compatibility alias of `reset_state`. New classes should
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# only implement `reset_state`.
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return self.reset_state()
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class Reduce(Metric):
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"""Encapsulates metrics that perform a reduce operation on the values.
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Args:
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reduction: a `tf.keras.metrics.Reduction` enum value.
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name: string name of the metric instance.
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dtype: (Optional) data type of the metric result.
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"""
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def __init__(self, reduction, name, dtype=None):
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super(Reduce, self).__init__(name=name, dtype=dtype)
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self.reduction = reduction
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self.total = self.add_weight(
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'total', initializer=init_ops.zeros_initializer)
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if reduction in [metrics_utils.Reduction.SUM_OVER_BATCH_SIZE,
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metrics_utils.Reduction.WEIGHTED_MEAN]:
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self.count = self.add_weight(
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'count', initializer=init_ops.zeros_initializer)
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def update_state(self, values, sample_weight=None):
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"""Accumulates statistics for computing the metric.
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Args:
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values: Per-example value.
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sample_weight: Optional weighting of each example. Defaults to 1.
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Returns:
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Update op.
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"""
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[values], sample_weight = \
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metrics_utils.ragged_assert_compatible_and_get_flat_values(
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[values], sample_weight)
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try:
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values = math_ops.cast(values, self._dtype)
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except (ValueError, TypeError):
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msg = ('The output of a metric function can only be a single Tensor. '
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'Got: %s' % (values,))
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if isinstance(values, dict):
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msg += ('. To return a dict of values, implement a custom Metric '
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'subclass.')
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raise RuntimeError(msg)
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if sample_weight is not None:
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sample_weight = math_ops.cast(sample_weight, self._dtype)
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# Update dimensions of weights to match with values if possible.
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values, _, sample_weight = losses_utils.squeeze_or_expand_dimensions(
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values, sample_weight=sample_weight)
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try:
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# Broadcast weights if possible.
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sample_weight = weights_broadcast_ops.broadcast_weights(
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sample_weight, values)
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except ValueError:
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# Reduce values to same ndim as weight array
|
||
|
ndim = backend.ndim(values)
|
||
|
weight_ndim = backend.ndim(sample_weight)
|
||
|
if self.reduction == metrics_utils.Reduction.SUM:
|
||
|
values = math_ops.reduce_sum(
|
||
|
values, axis=list(range(weight_ndim, ndim)))
|
||
|
else:
|
||
|
values = math_ops.reduce_mean(
|
||
|
values, axis=list(range(weight_ndim, ndim)))
|
||
|
values = math_ops.multiply(values, sample_weight)
|
||
|
|
||
|
value_sum = math_ops.reduce_sum(values)
|
||
|
with ops.control_dependencies([value_sum]):
|
||
|
update_total_op = self.total.assign_add(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 = math_ops.cast(array_ops.size(values), self._dtype)
|
||
|
elif self.reduction == metrics_utils.Reduction.WEIGHTED_MEAN:
|
||
|
if sample_weight is None:
|
||
|
num_values = math_ops.cast(array_ops.size(values), self._dtype)
|
||
|
else:
|
||
|
num_values = math_ops.reduce_sum(sample_weight)
|
||
|
else:
|
||
|
raise NotImplementedError(
|
||
|
'reduction [%s] not implemented' % self.reduction)
|
||
|
|
||
|
with ops.control_dependencies([update_total_op]):
|
||
|
return self.count.assign_add(num_values)
|
||
|
|
||
|
def result(self):
|
||
|
if self.reduction == metrics_utils.Reduction.SUM:
|
||
|
return array_ops.identity(self.total)
|
||
|
elif self.reduction in [
|
||
|
metrics_utils.Reduction.WEIGHTED_MEAN,
|
||
|
metrics_utils.Reduction.SUM_OVER_BATCH_SIZE
|
||
|
]:
|
||
|
return math_ops.div_no_nan(self.total, self.count)
|
||
|
else:
|
||
|
raise NotImplementedError(
|
||
|
'reduction [%s] not implemented' % self.reduction)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.Sum')
|
||
|
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.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.Sum()
|
||
|
>>> m.update_state([1, 3, 5, 7])
|
||
|
>>> m.result().numpy()
|
||
|
16.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.add_metric(tf.keras.metrics.Sum(name='sum_1')(outputs))
|
||
|
model.compile(optimizer='sgd', loss='mse')
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='sum', dtype=None):
|
||
|
super(Sum, self).__init__(reduction=metrics_utils.Reduction.SUM,
|
||
|
name=name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.Mean')
|
||
|
class Mean(Reduce):
|
||
|
"""Computes the (weighted) mean of the given values.
|
||
|
|
||
|
For example, if values is [1, 3, 5, 7] then the mean is 4.
|
||
|
If the weights were specified as [1, 1, 0, 0] then the mean would be 2.
|
||
|
|
||
|
This metric creates two variables, `total` and `count` that are used to
|
||
|
compute the average of `values`. This average is ultimately returned as `mean`
|
||
|
which is an idempotent operation that simply divides `total` by `count`.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.Mean()
|
||
|
>>> m.update_state([1, 3, 5, 7])
|
||
|
>>> m.result().numpy()
|
||
|
4.0
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
|
||
|
>>> m.result().numpy()
|
||
|
2.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.add_metric(tf.keras.metrics.Mean(name='mean_1')(outputs))
|
||
|
model.compile(optimizer='sgd', loss='mse')
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='mean', dtype=None):
|
||
|
super(Mean, self).__init__(
|
||
|
reduction=metrics_utils.Reduction.WEIGHTED_MEAN, name=name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.MeanRelativeError')
|
||
|
class MeanRelativeError(Mean):
|
||
|
"""Computes the mean relative error by normalizing with the given values.
|
||
|
|
||
|
This metric creates two local variables, `total` and `count` that are used to
|
||
|
compute the mean relative error. This is weighted by `sample_weight`, and
|
||
|
it is ultimately returned as `mean_relative_error`:
|
||
|
an idempotent operation that simply divides `total` by `count`.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
normalizer: The normalizer values with same shape as predictions.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.MeanRelativeError(normalizer=[1, 3, 2, 3])
|
||
|
>>> m.update_state([1, 3, 2, 3], [2, 4, 6, 8])
|
||
|
|
||
|
>>> # metric = mean(|y_pred - y_true| / normalizer)
|
||
|
>>> # = mean([1, 1, 4, 5] / [1, 3, 2, 3]) = mean([1, 1/3, 2, 5/3])
|
||
|
>>> # = 5/4 = 1.25
|
||
|
>>> m.result().numpy()
|
||
|
1.25
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.MeanRelativeError(normalizer=[1, 3])])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, normalizer, name=None, dtype=None):
|
||
|
super(MeanRelativeError, self).__init__(name=name, dtype=dtype)
|
||
|
normalizer = math_ops.cast(normalizer, self._dtype)
|
||
|
self.normalizer = normalizer
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates metric statistics.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values.
|
||
|
y_pred: The predicted values.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
|
||
|
`Tensor` whose rank is either 0, or the same rank as `y_true`, and must
|
||
|
be broadcastable to `y_true`.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
y_true = math_ops.cast(y_true, self._dtype)
|
||
|
y_pred = math_ops.cast(y_pred, self._dtype)
|
||
|
[y_pred, y_true], sample_weight = \
|
||
|
metrics_utils.ragged_assert_compatible_and_get_flat_values(
|
||
|
[y_pred, y_true], sample_weight)
|
||
|
y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
|
||
|
y_pred, y_true)
|
||
|
|
||
|
y_pred, self.normalizer = losses_utils.remove_squeezable_dimensions(
|
||
|
y_pred, self.normalizer)
|
||
|
y_pred.shape.assert_is_compatible_with(y_true.shape)
|
||
|
relative_errors = math_ops.div_no_nan(
|
||
|
math_ops.abs(y_true - y_pred), self.normalizer)
|
||
|
|
||
|
return super(MeanRelativeError, self).update_state(
|
||
|
relative_errors, sample_weight=sample_weight)
|
||
|
|
||
|
def get_config(self):
|
||
|
n = self.normalizer
|
||
|
config = {'normalizer': backend.eval(n) if is_tensor_or_variable(n) else n}
|
||
|
base_config = super(MeanRelativeError, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.MeanMetricWrapper')
|
||
|
class MeanMetricWrapper(Mean):
|
||
|
"""Wraps a stateless metric function with the Mean metric.
|
||
|
|
||
|
You could use this class to quickly build a mean metric from a function. The
|
||
|
function needs to have the signature `fn(y_true, y_pred)` and return a
|
||
|
per-sample loss array. `MeanMetricWrapper.result()` will return
|
||
|
the average metric value across all samples seen so far.
|
||
|
|
||
|
For example:
|
||
|
|
||
|
```python
|
||
|
def accuracy(y_true, y_pred):
|
||
|
return tf.cast(tf.math.equal(y_true, y_pred), tf.float32)
|
||
|
|
||
|
accuracy_metric = tf.keras.metrics.MeanMetricWrapper(fn=accuracy)
|
||
|
|
||
|
keras_model.compile(..., metrics=accuracy_metric)
|
||
|
```
|
||
|
|
||
|
Args:
|
||
|
fn: The metric function to wrap, with signature `fn(y_true, y_pred,
|
||
|
**kwargs)`.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
**kwargs: Keyword arguments to pass on to `fn`.
|
||
|
"""
|
||
|
|
||
|
def __init__(self, fn, name=None, dtype=None, **kwargs):
|
||
|
super(MeanMetricWrapper, self).__init__(name=name, dtype=dtype)
|
||
|
self._fn = fn
|
||
|
self._fn_kwargs = kwargs
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates metric statistics.
|
||
|
|
||
|
`y_true` and `y_pred` should have the same shape.
|
||
|
|
||
|
Args:
|
||
|
y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
|
||
|
y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
|
||
|
sample_weight: Optional `sample_weight` acts as a
|
||
|
coefficient for the metric. If a scalar is provided, then the metric is
|
||
|
simply scaled by the given value. If `sample_weight` is a tensor of size
|
||
|
`[batch_size]`, then the metric for each sample of the batch is rescaled
|
||
|
by the corresponding element in the `sample_weight` vector. If the shape
|
||
|
of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted
|
||
|
to this shape), then each metric element of `y_pred` is scaled by the
|
||
|
corresponding value of `sample_weight`. (Note on `dN-1`: all metric
|
||
|
functions reduce by 1 dimension, usually the last axis (-1)).
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
y_true = math_ops.cast(y_true, self._dtype)
|
||
|
y_pred = math_ops.cast(y_pred, self._dtype)
|
||
|
[y_true, y_pred], sample_weight = (
|
||
|
metrics_utils.ragged_assert_compatible_and_get_flat_values(
|
||
|
[y_true, y_pred], sample_weight))
|
||
|
y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
|
||
|
y_pred, y_true)
|
||
|
|
||
|
ag_fn = autograph.tf_convert(self._fn, ag_ctx.control_status_ctx())
|
||
|
matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
|
||
|
return super(MeanMetricWrapper, self).update_state(
|
||
|
matches, sample_weight=sample_weight)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {}
|
||
|
|
||
|
if type(self) is MeanMetricWrapper: # pylint: disable=unidiomatic-typecheck
|
||
|
# Only include function argument when the object is a MeanMetricWrapper
|
||
|
# and not a subclass.
|
||
|
config['fn'] = self._fn
|
||
|
|
||
|
for k, v in self._fn_kwargs.items():
|
||
|
config[k] = backend.eval(v) if is_tensor_or_variable(v) else v
|
||
|
base_config = super(MeanMetricWrapper, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
@classmethod
|
||
|
def from_config(cls, config):
|
||
|
# Note that while MeanMetricWrapper itself isn't public, objects of this
|
||
|
# class may be created and added to the model by calling model.compile.
|
||
|
fn = config.pop('fn', None)
|
||
|
if cls is MeanMetricWrapper:
|
||
|
return cls(get(fn), **config)
|
||
|
return super(MeanMetricWrapper, cls).from_config(config)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.Accuracy')
|
||
|
class Accuracy(MeanMetricWrapper):
|
||
|
"""Calculates how often predictions equal labels.
|
||
|
|
||
|
This metric creates two local variables, `total` and `count` that are used to
|
||
|
compute the frequency with which `y_pred` matches `y_true`. This frequency is
|
||
|
ultimately returned as `binary accuracy`: an idempotent operation that simply
|
||
|
divides `total` by `count`.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.Accuracy()
|
||
|
>>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]])
|
||
|
>>> m.result().numpy()
|
||
|
0.75
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]],
|
||
|
... sample_weight=[1, 1, 0, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.Accuracy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='accuracy', dtype=None):
|
||
|
super(Accuracy, self).__init__(accuracy, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.BinaryAccuracy')
|
||
|
class BinaryAccuracy(MeanMetricWrapper):
|
||
|
"""Calculates how often predictions match binary labels.
|
||
|
|
||
|
This metric creates two local variables, `total` and `count` that are used to
|
||
|
compute the frequency with which `y_pred` matches `y_true`. This frequency is
|
||
|
ultimately returned as `binary accuracy`: an idempotent operation that simply
|
||
|
divides `total` by `count`.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
threshold: (Optional) Float representing the threshold for deciding
|
||
|
whether prediction values are 1 or 0.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.BinaryAccuracy()
|
||
|
>>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])
|
||
|
>>> m.result().numpy()
|
||
|
0.75
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],
|
||
|
... sample_weight=[1, 0, 0, 1])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.BinaryAccuracy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='binary_accuracy', dtype=None, threshold=0.5):
|
||
|
super(BinaryAccuracy, self).__init__(
|
||
|
binary_accuracy, name, dtype=dtype, threshold=threshold)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.CategoricalAccuracy')
|
||
|
class CategoricalAccuracy(MeanMetricWrapper):
|
||
|
"""Calculates how often predictions match one-hot labels.
|
||
|
|
||
|
You can provide logits of classes as `y_pred`, since argmax of
|
||
|
logits and probabilities are same.
|
||
|
|
||
|
This metric creates two local variables, `total` and `count` that are used to
|
||
|
compute the frequency with which `y_pred` matches `y_true`. This frequency is
|
||
|
ultimately returned as `categorical accuracy`: an idempotent operation that
|
||
|
simply divides `total` by `count`.
|
||
|
|
||
|
`y_pred` and `y_true` should be passed in as vectors of probabilities, rather
|
||
|
than as labels. If necessary, use `tf.one_hot` to expand `y_true` as a vector.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.CategoricalAccuracy()
|
||
|
>>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
|
||
|
... [0.05, 0.95, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
|
||
|
... [0.05, 0.95, 0]],
|
||
|
... sample_weight=[0.7, 0.3])
|
||
|
>>> m.result().numpy()
|
||
|
0.3
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.CategoricalAccuracy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='categorical_accuracy', dtype=None):
|
||
|
super(CategoricalAccuracy, self).__init__(
|
||
|
categorical_accuracy, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.SparseCategoricalAccuracy')
|
||
|
class SparseCategoricalAccuracy(MeanMetricWrapper):
|
||
|
"""Calculates how often predictions match integer labels.
|
||
|
|
||
|
```python
|
||
|
acc = np.dot(sample_weight, np.equal(y_true, np.argmax(y_pred, axis=1))
|
||
|
```
|
||
|
|
||
|
You can provide logits of classes as `y_pred`, since argmax of
|
||
|
logits and probabilities are same.
|
||
|
|
||
|
This metric creates two local variables, `total` and `count` that are used to
|
||
|
compute the frequency with which `y_pred` matches `y_true`. This frequency is
|
||
|
ultimately returned as `sparse categorical accuracy`: an idempotent operation
|
||
|
that simply divides `total` by `count`.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.SparseCategoricalAccuracy()
|
||
|
>>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]],
|
||
|
... sample_weight=[0.7, 0.3])
|
||
|
>>> m.result().numpy()
|
||
|
0.3
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='sparse_categorical_accuracy', dtype=None):
|
||
|
super(SparseCategoricalAccuracy, self).__init__(
|
||
|
sparse_categorical_accuracy, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.TopKCategoricalAccuracy')
|
||
|
class TopKCategoricalAccuracy(MeanMetricWrapper):
|
||
|
"""Computes how often targets are in the top `K` predictions.
|
||
|
|
||
|
Args:
|
||
|
k: (Optional) Number of top elements to look at for computing accuracy.
|
||
|
Defaults to 5.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.TopKCategoricalAccuracy(k=1)
|
||
|
>>> m.update_state([[0, 0, 1], [0, 1, 0]],
|
||
|
... [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 0, 1], [0, 1, 0]],
|
||
|
... [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
|
||
|
... sample_weight=[0.7, 0.3])
|
||
|
>>> m.result().numpy()
|
||
|
0.3
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.TopKCategoricalAccuracy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, k=5, name='top_k_categorical_accuracy', dtype=None):
|
||
|
super(TopKCategoricalAccuracy, self).__init__(
|
||
|
top_k_categorical_accuracy, name, dtype=dtype, k=k)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.SparseTopKCategoricalAccuracy')
|
||
|
class SparseTopKCategoricalAccuracy(MeanMetricWrapper):
|
||
|
"""Computes how often integer targets are in the top `K` predictions.
|
||
|
|
||
|
Args:
|
||
|
k: (Optional) Number of top elements to look at for computing accuracy.
|
||
|
Defaults to 5.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.SparseTopKCategoricalAccuracy(k=1)
|
||
|
>>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
|
||
|
... sample_weight=[0.7, 0.3])
|
||
|
>>> m.result().numpy()
|
||
|
0.3
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.SparseTopKCategoricalAccuracy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, k=5, name='sparse_top_k_categorical_accuracy', dtype=None):
|
||
|
super(SparseTopKCategoricalAccuracy, self).__init__(
|
||
|
sparse_top_k_categorical_accuracy, name, dtype=dtype, k=k)
|
||
|
|
||
|
|
||
|
class _ConfusionMatrixConditionCount(Metric):
|
||
|
"""Calculates the number of the given confusion matrix condition.
|
||
|
|
||
|
Args:
|
||
|
confusion_matrix_cond: One of `metrics_utils.ConfusionMatrix` conditions.
|
||
|
thresholds: (Optional) Defaults to 0.5. A float value or a python list/tuple
|
||
|
of float threshold values in [0, 1]. A threshold is compared with
|
||
|
prediction values to determine the truth value of predictions (i.e., above
|
||
|
the threshold is `true`, below is `false`). One metric value is generated
|
||
|
for each threshold value.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
confusion_matrix_cond,
|
||
|
thresholds=None,
|
||
|
name=None,
|
||
|
dtype=None):
|
||
|
super(_ConfusionMatrixConditionCount, self).__init__(name=name, dtype=dtype)
|
||
|
self._confusion_matrix_cond = confusion_matrix_cond
|
||
|
self.init_thresholds = thresholds
|
||
|
self.thresholds = metrics_utils.parse_init_thresholds(
|
||
|
thresholds, default_threshold=0.5)
|
||
|
self._thresholds_distributed_evenly = (
|
||
|
metrics_utils.is_evenly_distributed_thresholds(self.thresholds))
|
||
|
self.accumulator = self.add_weight(
|
||
|
'accumulator',
|
||
|
shape=(len(self.thresholds),),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates the metric statistics.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values.
|
||
|
y_pred: The predicted values.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
|
||
|
`Tensor` whose rank is either 0, or the same rank as `y_true`, and must
|
||
|
be broadcastable to `y_true`.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
return metrics_utils.update_confusion_matrix_variables(
|
||
|
{self._confusion_matrix_cond: self.accumulator},
|
||
|
y_true,
|
||
|
y_pred,
|
||
|
thresholds=self.thresholds,
|
||
|
thresholds_distributed_evenly=self._thresholds_distributed_evenly,
|
||
|
sample_weight=sample_weight)
|
||
|
|
||
|
def result(self):
|
||
|
if len(self.thresholds) == 1:
|
||
|
result = self.accumulator[0]
|
||
|
else:
|
||
|
result = self.accumulator
|
||
|
return ops.convert_to_tensor_v2_with_dispatch(result)
|
||
|
|
||
|
def reset_state(self):
|
||
|
num_thresholds = len(to_list(self.thresholds))
|
||
|
backend.batch_set_value(
|
||
|
[(v, np.zeros((num_thresholds,))) for v in self.variables])
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'thresholds': self.init_thresholds}
|
||
|
base_config = super(_ConfusionMatrixConditionCount, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.FalsePositives')
|
||
|
class FalsePositives(_ConfusionMatrixConditionCount):
|
||
|
"""Calculates the number of false positives.
|
||
|
|
||
|
If `sample_weight` is given, calculates the sum of the weights of
|
||
|
false positives. This metric creates one local variable, `accumulator`
|
||
|
that is used to keep track of the number of false positives.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
thresholds: (Optional) Defaults to 0.5. A float value or a python
|
||
|
list/tuple of float threshold values in [0, 1]. A threshold is compared
|
||
|
with prediction values to determine the truth value of predictions
|
||
|
(i.e., above the threshold is `true`, below is `false`). One metric
|
||
|
value is generated for each threshold value.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.FalsePositives()
|
||
|
>>> m.update_state([0, 1, 0, 0], [0, 0, 1, 1])
|
||
|
>>> m.result().numpy()
|
||
|
2.0
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 1, 0, 0], [0, 0, 1, 1], sample_weight=[0, 0, 1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.FalsePositives()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, thresholds=None, name=None, dtype=None):
|
||
|
super(FalsePositives, self).__init__(
|
||
|
confusion_matrix_cond=metrics_utils.ConfusionMatrix.FALSE_POSITIVES,
|
||
|
thresholds=thresholds,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.FalseNegatives')
|
||
|
class FalseNegatives(_ConfusionMatrixConditionCount):
|
||
|
"""Calculates the number of false negatives.
|
||
|
|
||
|
If `sample_weight` is given, calculates the sum of the weights of
|
||
|
false negatives. This metric creates one local variable, `accumulator`
|
||
|
that is used to keep track of the number of false negatives.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
thresholds: (Optional) Defaults to 0.5. A float value or a python
|
||
|
list/tuple of float threshold values in [0, 1]. A threshold is compared
|
||
|
with prediction values to determine the truth value of predictions
|
||
|
(i.e., above the threshold is `true`, below is `false`). One metric
|
||
|
value is generated for each threshold value.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.FalseNegatives()
|
||
|
>>> m.update_state([0, 1, 1, 1], [0, 1, 0, 0])
|
||
|
>>> m.result().numpy()
|
||
|
2.0
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 1, 1, 1], [0, 1, 0, 0], sample_weight=[0, 0, 1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.FalseNegatives()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, thresholds=None, name=None, dtype=None):
|
||
|
super(FalseNegatives, self).__init__(
|
||
|
confusion_matrix_cond=metrics_utils.ConfusionMatrix.FALSE_NEGATIVES,
|
||
|
thresholds=thresholds,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.TrueNegatives')
|
||
|
class TrueNegatives(_ConfusionMatrixConditionCount):
|
||
|
"""Calculates the number of true negatives.
|
||
|
|
||
|
If `sample_weight` is given, calculates the sum of the weights of
|
||
|
true negatives. This metric creates one local variable, `accumulator`
|
||
|
that is used to keep track of the number of true negatives.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
thresholds: (Optional) Defaults to 0.5. A float value or a python
|
||
|
list/tuple of float threshold values in [0, 1]. A threshold is compared
|
||
|
with prediction values to determine the truth value of predictions
|
||
|
(i.e., above the threshold is `true`, below is `false`). One metric
|
||
|
value is generated for each threshold value.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.TrueNegatives()
|
||
|
>>> m.update_state([0, 1, 0, 0], [1, 1, 0, 0])
|
||
|
>>> m.result().numpy()
|
||
|
2.0
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 1, 0, 0], [1, 1, 0, 0], sample_weight=[0, 0, 1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.TrueNegatives()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, thresholds=None, name=None, dtype=None):
|
||
|
super(TrueNegatives, self).__init__(
|
||
|
confusion_matrix_cond=metrics_utils.ConfusionMatrix.TRUE_NEGATIVES,
|
||
|
thresholds=thresholds,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.TruePositives')
|
||
|
class TruePositives(_ConfusionMatrixConditionCount):
|
||
|
"""Calculates the number of true positives.
|
||
|
|
||
|
If `sample_weight` is given, calculates the sum of the weights of
|
||
|
true positives. This metric creates one local variable, `true_positives`
|
||
|
that is used to keep track of the number of true positives.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
thresholds: (Optional) Defaults to 0.5. A float value or a python
|
||
|
list/tuple of float threshold values in [0, 1]. A threshold is compared
|
||
|
with prediction values to determine the truth value of predictions
|
||
|
(i.e., above the threshold is `true`, below is `false`). One metric
|
||
|
value is generated for each threshold value.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.TruePositives()
|
||
|
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
|
||
|
>>> m.result().numpy()
|
||
|
2.0
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.TruePositives()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, thresholds=None, name=None, dtype=None):
|
||
|
super(TruePositives, self).__init__(
|
||
|
confusion_matrix_cond=metrics_utils.ConfusionMatrix.TRUE_POSITIVES,
|
||
|
thresholds=thresholds,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.Precision')
|
||
|
class Precision(Metric):
|
||
|
"""Computes the precision of the predictions with respect to the labels.
|
||
|
|
||
|
The metric creates two local variables, `true_positives` and `false_positives`
|
||
|
that are used to compute the precision. This value is ultimately returned as
|
||
|
`precision`, an idempotent operation that simply divides `true_positives`
|
||
|
by the sum of `true_positives` and `false_positives`.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
If `top_k` is set, we'll calculate precision as how often on average a class
|
||
|
among the top-k classes with the highest predicted values of a batch entry is
|
||
|
correct and can be found in the label for that entry.
|
||
|
|
||
|
If `class_id` is specified, we calculate precision by considering only the
|
||
|
entries in the batch for which `class_id` is above the threshold and/or in the
|
||
|
top-k highest predictions, and computing the fraction of them for which
|
||
|
`class_id` is indeed a correct label.
|
||
|
|
||
|
Args:
|
||
|
thresholds: (Optional) A float value or a python list/tuple of float
|
||
|
threshold values in [0, 1]. A threshold is compared with prediction
|
||
|
values to determine the truth value of predictions (i.e., above the
|
||
|
threshold is `true`, below is `false`). One metric value is generated
|
||
|
for each threshold value. If neither thresholds nor top_k are set, the
|
||
|
default is to calculate precision with `thresholds=0.5`.
|
||
|
top_k: (Optional) Unset by default. An int value specifying the top-k
|
||
|
predictions to consider when calculating precision.
|
||
|
class_id: (Optional) Integer class ID for which we want binary metrics.
|
||
|
This must be in the half-open interval `[0, num_classes)`, where
|
||
|
`num_classes` is the last dimension of predictions.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.Precision()
|
||
|
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
|
||
|
>>> m.result().numpy()
|
||
|
0.6666667
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.0
|
||
|
|
||
|
>>> # With top_k=2, it will calculate precision over y_true[:2] and y_pred[:2]
|
||
|
>>> m = tf.keras.metrics.Precision(top_k=2)
|
||
|
>>> m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
|
||
|
>>> m.result().numpy()
|
||
|
0.0
|
||
|
|
||
|
>>> # With top_k=4, it will calculate precision over y_true[:4] and y_pred[:4]
|
||
|
>>> m = tf.keras.metrics.Precision(top_k=4)
|
||
|
>>> m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.Precision()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
thresholds=None,
|
||
|
top_k=None,
|
||
|
class_id=None,
|
||
|
name=None,
|
||
|
dtype=None):
|
||
|
super(Precision, self).__init__(name=name, dtype=dtype)
|
||
|
self.init_thresholds = thresholds
|
||
|
self.top_k = top_k
|
||
|
self.class_id = class_id
|
||
|
|
||
|
default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
|
||
|
self.thresholds = metrics_utils.parse_init_thresholds(
|
||
|
thresholds, default_threshold=default_threshold)
|
||
|
self._thresholds_distributed_evenly = (
|
||
|
metrics_utils.is_evenly_distributed_thresholds(self.thresholds))
|
||
|
self.true_positives = self.add_weight(
|
||
|
'true_positives',
|
||
|
shape=(len(self.thresholds),),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
self.false_positives = self.add_weight(
|
||
|
'false_positives',
|
||
|
shape=(len(self.thresholds),),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates true positive and false positive statistics.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values, with the same dimensions as `y_pred`.
|
||
|
Will be cast to `bool`.
|
||
|
y_pred: The predicted values. Each element must be in the range `[0, 1]`.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
|
||
|
`Tensor` whose rank is either 0, or the same rank as `y_true`, and must
|
||
|
be broadcastable to `y_true`.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
return metrics_utils.update_confusion_matrix_variables(
|
||
|
{
|
||
|
metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
|
||
|
metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives
|
||
|
},
|
||
|
y_true,
|
||
|
y_pred,
|
||
|
thresholds=self.thresholds,
|
||
|
thresholds_distributed_evenly=self._thresholds_distributed_evenly,
|
||
|
top_k=self.top_k,
|
||
|
class_id=self.class_id,
|
||
|
sample_weight=sample_weight)
|
||
|
|
||
|
def result(self):
|
||
|
result = math_ops.div_no_nan(self.true_positives,
|
||
|
self.true_positives + self.false_positives)
|
||
|
return result[0] if len(self.thresholds) == 1 else result
|
||
|
|
||
|
def reset_state(self):
|
||
|
num_thresholds = len(to_list(self.thresholds))
|
||
|
backend.batch_set_value([(v, np.zeros((num_thresholds,)))
|
||
|
for v in (self.true_positives,
|
||
|
self.false_positives)])
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {
|
||
|
'thresholds': self.init_thresholds,
|
||
|
'top_k': self.top_k,
|
||
|
'class_id': self.class_id
|
||
|
}
|
||
|
base_config = super(Precision, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.Recall')
|
||
|
class Recall(Metric):
|
||
|
"""Computes the recall of the predictions with respect to the labels.
|
||
|
|
||
|
This metric creates two local variables, `true_positives` and
|
||
|
`false_negatives`, that are used to compute the recall. This value is
|
||
|
ultimately returned as `recall`, an idempotent operation that simply divides
|
||
|
`true_positives` by the sum of `true_positives` and `false_negatives`.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
If `top_k` is set, recall will be computed as how often on average a class
|
||
|
among the labels of a batch entry is in the top-k predictions.
|
||
|
|
||
|
If `class_id` is specified, we calculate recall by considering only the
|
||
|
entries in the batch for which `class_id` is in the label, and computing the
|
||
|
fraction of them for which `class_id` is above the threshold and/or in the
|
||
|
top-k predictions.
|
||
|
|
||
|
Args:
|
||
|
thresholds: (Optional) A float value or a python list/tuple of float
|
||
|
threshold values in [0, 1]. A threshold is compared with prediction
|
||
|
values to determine the truth value of predictions (i.e., above the
|
||
|
threshold is `true`, below is `false`). One metric value is generated
|
||
|
for each threshold value. If neither thresholds nor top_k are set, the
|
||
|
default is to calculate recall with `thresholds=0.5`.
|
||
|
top_k: (Optional) Unset by default. An int value specifying the top-k
|
||
|
predictions to consider when calculating recall.
|
||
|
class_id: (Optional) Integer class ID for which we want binary metrics.
|
||
|
This must be in the half-open interval `[0, num_classes)`, where
|
||
|
`num_classes` is the last dimension of predictions.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.Recall()
|
||
|
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
|
||
|
>>> m.result().numpy()
|
||
|
0.6666667
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.Recall()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
thresholds=None,
|
||
|
top_k=None,
|
||
|
class_id=None,
|
||
|
name=None,
|
||
|
dtype=None):
|
||
|
super(Recall, self).__init__(name=name, dtype=dtype)
|
||
|
self.init_thresholds = thresholds
|
||
|
self.top_k = top_k
|
||
|
self.class_id = class_id
|
||
|
|
||
|
default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
|
||
|
self.thresholds = metrics_utils.parse_init_thresholds(
|
||
|
thresholds, default_threshold=default_threshold)
|
||
|
self._thresholds_distributed_evenly = (
|
||
|
metrics_utils.is_evenly_distributed_thresholds(self.thresholds))
|
||
|
self.true_positives = self.add_weight(
|
||
|
'true_positives',
|
||
|
shape=(len(self.thresholds),),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
self.false_negatives = self.add_weight(
|
||
|
'false_negatives',
|
||
|
shape=(len(self.thresholds),),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates true positive and false negative statistics.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values, with the same dimensions as `y_pred`.
|
||
|
Will be cast to `bool`.
|
||
|
y_pred: The predicted values. Each element must be in the range `[0, 1]`.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
|
||
|
`Tensor` whose rank is either 0, or the same rank as `y_true`, and must
|
||
|
be broadcastable to `y_true`.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
return metrics_utils.update_confusion_matrix_variables(
|
||
|
{
|
||
|
metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
|
||
|
metrics_utils.ConfusionMatrix.FALSE_NEGATIVES: self.false_negatives
|
||
|
},
|
||
|
y_true,
|
||
|
y_pred,
|
||
|
thresholds=self.thresholds,
|
||
|
thresholds_distributed_evenly=self._thresholds_distributed_evenly,
|
||
|
top_k=self.top_k,
|
||
|
class_id=self.class_id,
|
||
|
sample_weight=sample_weight)
|
||
|
|
||
|
def result(self):
|
||
|
result = math_ops.div_no_nan(self.true_positives,
|
||
|
self.true_positives + self.false_negatives)
|
||
|
return result[0] if len(self.thresholds) == 1 else result
|
||
|
|
||
|
def reset_state(self):
|
||
|
num_thresholds = len(to_list(self.thresholds))
|
||
|
backend.batch_set_value([(v, np.zeros((num_thresholds,)))
|
||
|
for v in (self.true_positives,
|
||
|
self.false_negatives)])
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {
|
||
|
'thresholds': self.init_thresholds,
|
||
|
'top_k': self.top_k,
|
||
|
'class_id': self.class_id
|
||
|
}
|
||
|
base_config = super(Recall, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
class SensitivitySpecificityBase(Metric, metaclass=abc.ABCMeta):
|
||
|
"""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,
|
||
|
class_id=None,
|
||
|
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.class_id = class_id
|
||
|
self.true_positives = self.add_weight(
|
||
|
'true_positives',
|
||
|
shape=(num_thresholds,),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
self.true_negatives = self.add_weight(
|
||
|
'true_negatives',
|
||
|
shape=(num_thresholds,),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
self.false_positives = self.add_weight(
|
||
|
'false_positives',
|
||
|
shape=(num_thresholds,),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
self.false_negatives = self.add_weight(
|
||
|
'false_negatives',
|
||
|
shape=(num_thresholds,),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
|
||
|
# Compute `num_thresholds` thresholds in [0, 1]
|
||
|
if num_thresholds == 1:
|
||
|
self.thresholds = [0.5]
|
||
|
self._thresholds_distributed_evenly = False
|
||
|
else:
|
||
|
thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
|
||
|
for i in range(num_thresholds - 2)]
|
||
|
self.thresholds = [0.0] + thresholds + [1.0]
|
||
|
self._thresholds_distributed_evenly = True
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates confusion matrix statistics.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values.
|
||
|
y_pred: The predicted values.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
|
||
|
`Tensor` whose rank is either 0, or the same rank as `y_true`, and must
|
||
|
be broadcastable to `y_true`.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
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,
|
||
|
thresholds_distributed_evenly=self._thresholds_distributed_evenly,
|
||
|
class_id=self.class_id,
|
||
|
sample_weight=sample_weight)
|
||
|
|
||
|
def reset_state(self):
|
||
|
num_thresholds = len(self.thresholds)
|
||
|
confusion_matrix_variables = (self.true_positives, self.true_negatives,
|
||
|
self.false_positives, self.false_negatives)
|
||
|
backend.batch_set_value([
|
||
|
(v, np.zeros((num_thresholds,))) for v in confusion_matrix_variables
|
||
|
])
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'class_id': self.class_id}
|
||
|
base_config = super(SensitivitySpecificityBase, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
def _find_max_under_constraint(self, constrained, dependent, predicate):
|
||
|
"""Returns the maximum of dependent_statistic that satisfies the constraint.
|
||
|
|
||
|
Args:
|
||
|
constrained: Over these values the constraint
|
||
|
is specified. A rank-1 tensor.
|
||
|
dependent: From these values the maximum that satiesfies the
|
||
|
constraint is selected. Values in this tensor and in
|
||
|
`constrained` are linked by having the same threshold at each
|
||
|
position, hence this tensor must have the same shape.
|
||
|
predicate: A binary boolean functor to be applied to arguments
|
||
|
`constrained` and `self.value`, e.g. `tf.greater`.
|
||
|
|
||
|
Returns maximal dependent value, if no value satiesfies the constraint 0.0.
|
||
|
"""
|
||
|
feasible = array_ops.where_v2(predicate(constrained, self.value))
|
||
|
feasible_exists = math_ops.greater(array_ops.size(feasible), 0)
|
||
|
max_dependent = math_ops.reduce_max(array_ops.gather(dependent, feasible))
|
||
|
|
||
|
return array_ops.where_v2(feasible_exists, max_dependent, 0.0)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.SensitivityAtSpecificity')
|
||
|
class SensitivityAtSpecificity(SensitivitySpecificityBase):
|
||
|
"""Computes best sensitivity where specificity is >= specified value.
|
||
|
|
||
|
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.
|
||
|
|
||
|
If `class_id` is specified, we calculate precision by considering only the
|
||
|
entries in the batch for which `class_id` is above the threshold predictions,
|
||
|
and computing the fraction of them for which `class_id` is indeed a correct
|
||
|
label.
|
||
|
|
||
|
For additional information about specificity and sensitivity, see
|
||
|
[the following](https://en.wikipedia.org/wiki/Sensitivity_and_specificity).
|
||
|
|
||
|
Args:
|
||
|
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.
|
||
|
class_id: (Optional) Integer class ID for which we want binary metrics.
|
||
|
This must be in the half-open interval `[0, num_classes)`, where
|
||
|
`num_classes` is the last dimension of predictions.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.SensitivityAtSpecificity(0.5)
|
||
|
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
|
||
|
... sample_weight=[1, 1, 2, 2, 1])
|
||
|
>>> m.result().numpy()
|
||
|
0.333333
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.SensitivityAtSpecificity()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
specificity,
|
||
|
num_thresholds=200,
|
||
|
class_id=None,
|
||
|
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,
|
||
|
class_id=class_id,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
def result(self):
|
||
|
specificities = math_ops.div_no_nan(
|
||
|
self.true_negatives, self.true_negatives + self.false_positives)
|
||
|
sensitivities = math_ops.div_no_nan(
|
||
|
self.true_positives, self.true_positives + self.false_negatives)
|
||
|
return self._find_max_under_constraint(
|
||
|
specificities, sensitivities, math_ops.greater_equal)
|
||
|
|
||
|
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()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.SpecificityAtSensitivity')
|
||
|
class SpecificityAtSensitivity(SensitivitySpecificityBase):
|
||
|
"""Computes best specificity where sensitivity is >= specified value.
|
||
|
|
||
|
`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
|
||
|
specificity at the given sensitivity. The threshold for the given sensitivity
|
||
|
value is computed and used to evaluate the corresponding specificity.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
If `class_id` is specified, we calculate precision by considering only the
|
||
|
entries in the batch for which `class_id` is above the threshold predictions,
|
||
|
and computing the fraction of them for which `class_id` is indeed a correct
|
||
|
label.
|
||
|
|
||
|
For additional information about specificity and sensitivity, see
|
||
|
[the following](https://en.wikipedia.org/wiki/Sensitivity_and_specificity).
|
||
|
|
||
|
Args:
|
||
|
sensitivity: A scalar value in range `[0, 1]`.
|
||
|
num_thresholds: (Optional) Defaults to 200. The number of thresholds to
|
||
|
use for matching the given sensitivity.
|
||
|
class_id: (Optional) Integer class ID for which we want binary metrics.
|
||
|
This must be in the half-open interval `[0, num_classes)`, where
|
||
|
`num_classes` is the last dimension of predictions.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.SpecificityAtSensitivity(0.5)
|
||
|
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
|
||
|
>>> m.result().numpy()
|
||
|
0.66666667
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
|
||
|
... sample_weight=[1, 1, 2, 2, 2])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.SpecificityAtSensitivity()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
sensitivity,
|
||
|
num_thresholds=200,
|
||
|
class_id=None,
|
||
|
name=None,
|
||
|
dtype=None):
|
||
|
if sensitivity < 0 or sensitivity > 1:
|
||
|
raise ValueError('`sensitivity` must be in the range [0, 1].')
|
||
|
self.sensitivity = sensitivity
|
||
|
self.num_thresholds = num_thresholds
|
||
|
super(SpecificityAtSensitivity, self).__init__(
|
||
|
sensitivity,
|
||
|
num_thresholds=num_thresholds,
|
||
|
class_id=class_id,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
def result(self):
|
||
|
sensitivities = math_ops.div_no_nan(
|
||
|
self.true_positives, self.true_positives + self.false_negatives)
|
||
|
specificities = math_ops.div_no_nan(
|
||
|
self.true_negatives, self.true_negatives + self.false_positives)
|
||
|
return self._find_max_under_constraint(
|
||
|
sensitivities, specificities, math_ops.greater_equal)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {
|
||
|
'num_thresholds': self.num_thresholds,
|
||
|
'sensitivity': self.sensitivity
|
||
|
}
|
||
|
base_config = super(SpecificityAtSensitivity, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.PrecisionAtRecall')
|
||
|
class PrecisionAtRecall(SensitivitySpecificityBase):
|
||
|
"""Computes best precision where recall is >= specified value.
|
||
|
|
||
|
This metric creates four local variables, `true_positives`, `true_negatives`,
|
||
|
`false_positives` and `false_negatives` that are used to compute the
|
||
|
precision at the given recall. The threshold for the given recall
|
||
|
value is computed and used to evaluate the corresponding precision.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
If `class_id` is specified, we calculate precision by considering only the
|
||
|
entries in the batch for which `class_id` is above the threshold predictions,
|
||
|
and computing the fraction of them for which `class_id` is indeed a correct
|
||
|
label.
|
||
|
|
||
|
Args:
|
||
|
recall: A scalar value in range `[0, 1]`.
|
||
|
num_thresholds: (Optional) Defaults to 200. The number of thresholds to
|
||
|
use for matching the given recall.
|
||
|
class_id: (Optional) Integer class ID for which we want binary metrics.
|
||
|
This must be in the half-open interval `[0, num_classes)`, where
|
||
|
`num_classes` is the last dimension of predictions.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.PrecisionAtRecall(0.5)
|
||
|
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
|
||
|
... sample_weight=[2, 2, 2, 1, 1])
|
||
|
>>> m.result().numpy()
|
||
|
0.33333333
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.PrecisionAtRecall(recall=0.8)])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
recall,
|
||
|
num_thresholds=200,
|
||
|
class_id=None,
|
||
|
name=None,
|
||
|
dtype=None):
|
||
|
if recall < 0 or recall > 1:
|
||
|
raise ValueError('`recall` must be in the range [0, 1].')
|
||
|
self.recall = recall
|
||
|
self.num_thresholds = num_thresholds
|
||
|
super(PrecisionAtRecall, self).__init__(
|
||
|
value=recall,
|
||
|
num_thresholds=num_thresholds,
|
||
|
class_id=class_id,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
def result(self):
|
||
|
recalls = math_ops.div_no_nan(
|
||
|
self.true_positives, self.true_positives + self.false_negatives)
|
||
|
precisions = math_ops.div_no_nan(
|
||
|
self.true_positives, self.true_positives + self.false_positives)
|
||
|
return self._find_max_under_constraint(
|
||
|
recalls, precisions, math_ops.greater_equal)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'num_thresholds': self.num_thresholds, 'recall': self.recall}
|
||
|
base_config = super(PrecisionAtRecall, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.RecallAtPrecision')
|
||
|
class RecallAtPrecision(SensitivitySpecificityBase):
|
||
|
"""Computes best recall where precision is >= specified value.
|
||
|
|
||
|
For a given score-label-distribution the required precision might not
|
||
|
be achievable, in this case 0.0 is returned as recall.
|
||
|
|
||
|
This metric creates four local variables, `true_positives`, `true_negatives`,
|
||
|
`false_positives` and `false_negatives` that are used to compute the
|
||
|
recall at the given precision. The threshold for the given precision
|
||
|
value is computed and used to evaluate the corresponding recall.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
If `class_id` is specified, we calculate precision by considering only the
|
||
|
entries in the batch for which `class_id` is above the threshold predictions,
|
||
|
and computing the fraction of them for which `class_id` is indeed a correct
|
||
|
label.
|
||
|
|
||
|
Args:
|
||
|
precision: A scalar value in range `[0, 1]`.
|
||
|
num_thresholds: (Optional) Defaults to 200. The number of thresholds to
|
||
|
use for matching the given precision.
|
||
|
class_id: (Optional) Integer class ID for which we want binary metrics.
|
||
|
This must be in the half-open interval `[0, num_classes)`, where
|
||
|
`num_classes` is the last dimension of predictions.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.RecallAtPrecision(0.8)
|
||
|
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
|
||
|
... sample_weight=[1, 0, 0, 1])
|
||
|
>>> m.result().numpy()
|
||
|
1.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.RecallAtPrecision(precision=0.8)])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
precision,
|
||
|
num_thresholds=200,
|
||
|
class_id=None,
|
||
|
name=None,
|
||
|
dtype=None):
|
||
|
if precision < 0 or precision > 1:
|
||
|
raise ValueError('`precision` must be in the range [0, 1].')
|
||
|
self.precision = precision
|
||
|
self.num_thresholds = num_thresholds
|
||
|
super(RecallAtPrecision, self).__init__(
|
||
|
value=precision,
|
||
|
num_thresholds=num_thresholds,
|
||
|
class_id=class_id,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
def result(self):
|
||
|
precisions = math_ops.div_no_nan(
|
||
|
self.true_positives, self.true_positives + self.false_positives)
|
||
|
recalls = math_ops.div_no_nan(
|
||
|
self.true_positives, self.true_positives + self.false_negatives)
|
||
|
return self._find_max_under_constraint(
|
||
|
precisions, recalls, math_ops.greater_equal)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'num_thresholds': self.num_thresholds,
|
||
|
'precision': self.precision}
|
||
|
base_config = super(RecallAtPrecision, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.AUC')
|
||
|
class AUC(Metric):
|
||
|
"""Approximates the AUC (Area under the curve) of the ROC or PR curves.
|
||
|
|
||
|
The AUC (Area under the curve) of the ROC (Receiver operating
|
||
|
characteristic; default) or PR (Precision Recall) curves are quality measures
|
||
|
of binary classifiers. Unlike the accuracy, and like cross-entropy
|
||
|
losses, ROC-AUC and PR-AUC evaluate all the operational points of a model.
|
||
|
|
||
|
This class approximates AUCs using a Riemann sum. During the metric
|
||
|
accumulation phrase, predictions are accumulated within predefined buckets
|
||
|
by value. The AUC is then computed by interpolating per-bucket averages. These
|
||
|
buckets define the evaluated operational points.
|
||
|
|
||
|
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 a best approximation of the real AUC, `predictions` should be distributed
|
||
|
approximately uniformly in the range [0, 1] (if `from_logits=False`). 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.
|
||
|
|
||
|
Args:
|
||
|
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](
|
||
|
https://en.wikipedia.org/wiki/Riemann_sum) used.
|
||
|
'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' applies left summation
|
||
|
for increasing intervals and right summation for decreasing intervals;
|
||
|
'majoring' 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.
|
||
|
multi_label: boolean indicating whether multilabel data should be
|
||
|
treated as such, wherein AUC is computed separately for each label and
|
||
|
then averaged across labels, or (when False) if the data should be
|
||
|
flattened into a single label before AUC computation. In the latter
|
||
|
case, when multilabel data is passed to AUC, each label-prediction pair
|
||
|
is treated as an individual data point. Should be set to False for
|
||
|
multi-class data.
|
||
|
num_labels: (Optional) The number of labels, used when `multi_label` is
|
||
|
True. If `num_labels` is not specified, then state variables get created
|
||
|
on the first call to `update_state`.
|
||
|
label_weights: (Optional) list, array, or tensor of non-negative weights
|
||
|
used to compute AUCs for multilabel data. When `multi_label` is True,
|
||
|
the weights are applied to the individual label AUCs when they are
|
||
|
averaged to produce the multi-label AUC. When it's False, they are used
|
||
|
to weight the individual label predictions in computing the confusion
|
||
|
matrix on the flattened data. Note that this is unlike class_weights in
|
||
|
that class_weights weights the example depending on the value of its
|
||
|
label, whereas label_weights depends only on the index of that label
|
||
|
before flattening; therefore `label_weights` should not be used for
|
||
|
multi-class data.
|
||
|
from_logits: boolean indicating whether the predictions (`y_pred` in
|
||
|
`update_state`) are probabilities or sigmoid logits. As a rule of thumb,
|
||
|
when using a keras loss, the `from_logits` constructor argument of the
|
||
|
loss should match the AUC `from_logits` constructor argument.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.AUC(num_thresholds=3)
|
||
|
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
|
||
|
>>> # threshold values are [0 - 1e-7, 0.5, 1 + 1e-7]
|
||
|
>>> # tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2]
|
||
|
>>> # tp_rate = recall = [1, 0.5, 0], fp_rate = [1, 0, 0]
|
||
|
>>> # auc = ((((1+0.5)/2)*(1-0)) + (((0.5+0)/2)*(0-0))) = 0.75
|
||
|
>>> m.result().numpy()
|
||
|
0.75
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
|
||
|
... sample_weight=[1, 0, 0, 1])
|
||
|
>>> m.result().numpy()
|
||
|
1.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
# Reports the AUC of a model outputing a probability.
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss=tf.keras.losses.BinaryCrossentropy(),
|
||
|
metrics=[tf.keras.metrics.AUC()])
|
||
|
|
||
|
# Reports the AUC of a model outputing a logit.
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
|
||
|
metrics=[tf.keras.metrics.AUC(from_logits=True)])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
num_thresholds=200,
|
||
|
curve='ROC',
|
||
|
summation_method='interpolation',
|
||
|
name=None,
|
||
|
dtype=None,
|
||
|
thresholds=None,
|
||
|
multi_label=False,
|
||
|
num_labels=None,
|
||
|
label_weights=None,
|
||
|
from_logits=False):
|
||
|
# 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)
|
||
|
self._thresholds_distributed_evenly = (
|
||
|
metrics_utils.is_evenly_distributed_thresholds(
|
||
|
np.array([0.0] + thresholds + [1.0])))
|
||
|
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)]
|
||
|
self._thresholds_distributed_evenly = True
|
||
|
|
||
|
# Add an endpoint "threshold" below zero and above one for either
|
||
|
# threshold method to account for floating point imprecisions.
|
||
|
self._thresholds = np.array([0.0 - backend.epsilon()] + thresholds +
|
||
|
[1.0 + backend.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)
|
||
|
|
||
|
# Handle multilabel arguments.
|
||
|
self.multi_label = multi_label
|
||
|
if label_weights is not None:
|
||
|
label_weights = constant_op.constant(label_weights, dtype=self.dtype)
|
||
|
checks = [
|
||
|
check_ops.assert_non_negative(
|
||
|
label_weights,
|
||
|
message='All values of `label_weights` must be non-negative.')
|
||
|
]
|
||
|
with ops.control_dependencies(checks):
|
||
|
self.label_weights = label_weights
|
||
|
|
||
|
else:
|
||
|
self.label_weights = None
|
||
|
|
||
|
self._from_logits = from_logits
|
||
|
|
||
|
self._built = False
|
||
|
if self.multi_label:
|
||
|
if num_labels:
|
||
|
shape = tensor_shape.TensorShape([None, num_labels])
|
||
|
self._build(shape)
|
||
|
else:
|
||
|
if num_labels:
|
||
|
raise ValueError(
|
||
|
'`num_labels` is needed only when `multi_label` is True.')
|
||
|
self._build(None)
|
||
|
|
||
|
@property
|
||
|
def thresholds(self):
|
||
|
"""The thresholds used for evaluating AUC."""
|
||
|
return list(self._thresholds)
|
||
|
|
||
|
def _build(self, shape):
|
||
|
"""Initialize TP, FP, TN, and FN tensors, given the shape of the data."""
|
||
|
if self.multi_label:
|
||
|
if shape.ndims != 2:
|
||
|
raise ValueError('`y_true` must have rank=2 when `multi_label` is '
|
||
|
'True. Found rank %s.' % shape.ndims)
|
||
|
self._num_labels = shape[1]
|
||
|
variable_shape = tensor_shape.TensorShape(
|
||
|
[tensor_shape.Dimension(self.num_thresholds), self._num_labels])
|
||
|
|
||
|
else:
|
||
|
variable_shape = tensor_shape.TensorShape(
|
||
|
[tensor_shape.Dimension(self.num_thresholds)])
|
||
|
self._build_input_shape = shape
|
||
|
# Create metric variables
|
||
|
self.true_positives = self.add_weight(
|
||
|
'true_positives',
|
||
|
shape=variable_shape,
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
self.true_negatives = self.add_weight(
|
||
|
'true_negatives',
|
||
|
shape=variable_shape,
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
self.false_positives = self.add_weight(
|
||
|
'false_positives',
|
||
|
shape=variable_shape,
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
self.false_negatives = self.add_weight(
|
||
|
'false_negatives',
|
||
|
shape=variable_shape,
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
|
||
|
if self.multi_label:
|
||
|
with ops.init_scope():
|
||
|
# This should only be necessary for handling v1 behavior. In v2, AUC
|
||
|
# should be initialized outside of any tf.functions, and therefore in
|
||
|
# eager mode.
|
||
|
if not context.executing_eagerly():
|
||
|
backend._initialize_variables(backend._get_session()) # pylint: disable=protected-access
|
||
|
|
||
|
self._built = True
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates confusion matrix statistics.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values.
|
||
|
y_pred: The predicted values.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
|
||
|
`Tensor` whose rank is either 0, or the same rank as `y_true`, and must
|
||
|
be broadcastable to `y_true`.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
deps = []
|
||
|
if not self._built:
|
||
|
self._build(tensor_shape.TensorShape(y_pred.shape))
|
||
|
|
||
|
if self.multi_label or (self.label_weights is not None):
|
||
|
# y_true should have shape (number of examples, number of labels).
|
||
|
shapes = [
|
||
|
(y_true, ('N', 'L'))
|
||
|
]
|
||
|
if self.multi_label:
|
||
|
# TP, TN, FP, and FN should all have shape
|
||
|
# (number of thresholds, number of labels).
|
||
|
shapes.extend([(self.true_positives, ('T', 'L')),
|
||
|
(self.true_negatives, ('T', 'L')),
|
||
|
(self.false_positives, ('T', 'L')),
|
||
|
(self.false_negatives, ('T', 'L'))])
|
||
|
if self.label_weights is not None:
|
||
|
# label_weights should be of length equal to the number of labels.
|
||
|
shapes.append((self.label_weights, ('L',)))
|
||
|
deps = [
|
||
|
check_ops.assert_shapes(
|
||
|
shapes, message='Number of labels is not consistent.')
|
||
|
]
|
||
|
|
||
|
# Only forward label_weights to update_confusion_matrix_variables when
|
||
|
# multi_label is False. Otherwise the averaging of individual label AUCs is
|
||
|
# handled in AUC.result
|
||
|
label_weights = None if self.multi_label else self.label_weights
|
||
|
|
||
|
if self._from_logits:
|
||
|
y_pred = activations.sigmoid(y_pred)
|
||
|
|
||
|
with ops.control_dependencies(deps):
|
||
|
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,
|
||
|
thresholds_distributed_evenly=self._thresholds_distributed_evenly,
|
||
|
sample_weight=sample_weight,
|
||
|
multi_label=self.multi_label,
|
||
|
label_weights=label_weights)
|
||
|
|
||
|
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 = math_ops.div_no_nan(
|
||
|
dtp, math_ops.maximum(dp, 0), name='prec_slope')
|
||
|
intercept = self.true_positives[1:] - math_ops.multiply(prec_slope, p[1:])
|
||
|
|
||
|
safe_p_ratio = array_ops.where(
|
||
|
math_ops.logical_and(p[:self.num_thresholds - 1] > 0, p[1:] > 0),
|
||
|
math_ops.div_no_nan(
|
||
|
p[:self.num_thresholds - 1],
|
||
|
math_ops.maximum(p[1:], 0),
|
||
|
name='recall_relative_ratio'),
|
||
|
array_ops.ones_like(p[1:]))
|
||
|
|
||
|
pr_auc_increment = math_ops.div_no_nan(
|
||
|
prec_slope * (dtp + intercept * math_ops.log(safe_p_ratio)),
|
||
|
math_ops.maximum(self.true_positives[1:] + self.false_negatives[1:], 0),
|
||
|
name='pr_auc_increment')
|
||
|
|
||
|
if self.multi_label:
|
||
|
by_label_auc = math_ops.reduce_sum(
|
||
|
pr_auc_increment, name=self.name + '_by_label', axis=0)
|
||
|
if self.label_weights is None:
|
||
|
# Evenly weighted average of the label AUCs.
|
||
|
return math_ops.reduce_mean(by_label_auc, name=self.name)
|
||
|
else:
|
||
|
# Weighted average of the label AUCs.
|
||
|
return math_ops.div_no_nan(
|
||
|
math_ops.reduce_sum(
|
||
|
math_ops.multiply(by_label_auc, self.label_weights)),
|
||
|
math_ops.reduce_sum(self.label_weights),
|
||
|
name=self.name)
|
||
|
else:
|
||
|
return math_ops.reduce_sum(pr_auc_increment, name='interpolate_pr_auc')
|
||
|
|
||
|
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 = math_ops.div_no_nan(self.true_positives,
|
||
|
self.true_positives + self.false_negatives)
|
||
|
if self.curve == metrics_utils.AUCCurve.ROC:
|
||
|
fp_rate = math_ops.div_no_nan(self.false_positives,
|
||
|
self.false_positives + self.true_negatives)
|
||
|
x = fp_rate
|
||
|
y = recall
|
||
|
else: # curve == 'PR'.
|
||
|
precision = math_ops.div_no_nan(
|
||
|
self.true_positives, self.true_positives + self.false_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 = math_ops.minimum(y[:self.num_thresholds - 1], y[1:])
|
||
|
else: # self.summation_method = metrics_utils.AUCSummationMethod.MAJORING:
|
||
|
heights = math_ops.maximum(y[:self.num_thresholds - 1], y[1:])
|
||
|
|
||
|
# Sum up the areas of all the rectangles.
|
||
|
if self.multi_label:
|
||
|
riemann_terms = math_ops.multiply(x[:self.num_thresholds - 1] - x[1:],
|
||
|
heights)
|
||
|
by_label_auc = math_ops.reduce_sum(
|
||
|
riemann_terms, name=self.name + '_by_label', axis=0)
|
||
|
|
||
|
if self.label_weights is None:
|
||
|
# Unweighted average of the label AUCs.
|
||
|
return math_ops.reduce_mean(by_label_auc, name=self.name)
|
||
|
else:
|
||
|
# Weighted average of the label AUCs.
|
||
|
return math_ops.div_no_nan(
|
||
|
math_ops.reduce_sum(
|
||
|
math_ops.multiply(by_label_auc, self.label_weights)),
|
||
|
math_ops.reduce_sum(self.label_weights),
|
||
|
name=self.name)
|
||
|
else:
|
||
|
return math_ops.reduce_sum(
|
||
|
math_ops.multiply(x[:self.num_thresholds - 1] - x[1:], heights),
|
||
|
name=self.name)
|
||
|
|
||
|
def reset_state(self):
|
||
|
if self._built:
|
||
|
confusion_matrix_variables = (self.true_positives, self.true_negatives,
|
||
|
self.false_positives, self.false_negatives)
|
||
|
if self.multi_label:
|
||
|
backend.batch_set_value(
|
||
|
[(v, np.zeros((self.num_thresholds, self._num_labels)))
|
||
|
for v in confusion_matrix_variables])
|
||
|
else:
|
||
|
backend.batch_set_value([(v, np.zeros((self.num_thresholds,)))
|
||
|
for v in confusion_matrix_variables])
|
||
|
|
||
|
def get_config(self):
|
||
|
if is_tensor_or_variable(self.label_weights):
|
||
|
label_weights = backend.eval(self.label_weights)
|
||
|
else:
|
||
|
label_weights = self.label_weights
|
||
|
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],
|
||
|
'multi_label': self.multi_label,
|
||
|
'label_weights': label_weights
|
||
|
}
|
||
|
base_config = super(AUC, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.CosineSimilarity')
|
||
|
class CosineSimilarity(MeanMetricWrapper):
|
||
|
"""Computes the cosine similarity between the labels and predictions.
|
||
|
|
||
|
`cosine similarity = (a . b) / ||a|| ||b||`
|
||
|
|
||
|
See: [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity).
|
||
|
|
||
|
This metric keeps the average cosine similarity between `predictions` and
|
||
|
`labels` over a stream of data.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
axis: (Optional) Defaults to -1. The dimension along which the cosine
|
||
|
similarity is computed.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> # l2_norm(y_true) = [[0., 1.], [1./1.414, 1./1.414]]
|
||
|
>>> # l2_norm(y_pred) = [[1., 0.], [1./1.414, 1./1.414]]
|
||
|
>>> # l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]
|
||
|
>>> # result = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
|
||
|
>>> # = ((0. + 0.) + (0.5 + 0.5)) / 2
|
||
|
>>> m = tf.keras.metrics.CosineSimilarity(axis=1)
|
||
|
>>> m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
|
||
|
>>> m.result().numpy()
|
||
|
0.49999997
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]],
|
||
|
... sample_weight=[0.3, 0.7])
|
||
|
>>> m.result().numpy()
|
||
|
0.6999999
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.CosineSimilarity(axis=1)])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='cosine_similarity', dtype=None, axis=-1):
|
||
|
super(CosineSimilarity, self).__init__(
|
||
|
cosine_similarity, name, dtype=dtype, axis=axis)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.MeanAbsoluteError')
|
||
|
class MeanAbsoluteError(MeanMetricWrapper):
|
||
|
"""Computes the mean absolute error between the labels and predictions.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.MeanAbsoluteError()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.25
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.MeanAbsoluteError()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='mean_absolute_error', dtype=None):
|
||
|
super(MeanAbsoluteError, self).__init__(
|
||
|
mean_absolute_error, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.MeanAbsolutePercentageError')
|
||
|
class MeanAbsolutePercentageError(MeanMetricWrapper):
|
||
|
"""Computes the mean absolute percentage error between `y_true` and `y_pred`.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.MeanAbsolutePercentageError()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
250000000.0
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
500000000.0
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.MeanAbsolutePercentageError()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='mean_absolute_percentage_error', dtype=None):
|
||
|
super(MeanAbsolutePercentageError, self).__init__(
|
||
|
mean_absolute_percentage_error, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.MeanSquaredError')
|
||
|
class MeanSquaredError(MeanMetricWrapper):
|
||
|
"""Computes the mean squared error between `y_true` and `y_pred`.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.MeanSquaredError()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.25
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.MeanSquaredError()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='mean_squared_error', dtype=None):
|
||
|
super(MeanSquaredError, self).__init__(
|
||
|
mean_squared_error, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.MeanSquaredLogarithmicError')
|
||
|
class MeanSquaredLogarithmicError(MeanMetricWrapper):
|
||
|
"""Computes the mean squared logarithmic error between `y_true` and `y_pred`.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.MeanSquaredLogarithmicError()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.12011322
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.24022643
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.MeanSquaredLogarithmicError()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='mean_squared_logarithmic_error', dtype=None):
|
||
|
super(MeanSquaredLogarithmicError, self).__init__(
|
||
|
mean_squared_logarithmic_error, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.Hinge')
|
||
|
class Hinge(MeanMetricWrapper):
|
||
|
"""Computes the hinge metric between `y_true` and `y_pred`.
|
||
|
|
||
|
`y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are
|
||
|
provided we will convert them to -1 or 1.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.Hinge()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
|
||
|
>>> m.result().numpy()
|
||
|
1.3
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.1
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.Hinge()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='hinge', dtype=None):
|
||
|
super(Hinge, self).__init__(hinge, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.SquaredHinge')
|
||
|
class SquaredHinge(MeanMetricWrapper):
|
||
|
"""Computes the squared hinge metric between `y_true` and `y_pred`.
|
||
|
|
||
|
`y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are
|
||
|
provided we will convert them to -1 or 1.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.SquaredHinge()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
|
||
|
>>> m.result().numpy()
|
||
|
1.86
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.46
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.SquaredHinge()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='squared_hinge', dtype=None):
|
||
|
super(SquaredHinge, self).__init__(squared_hinge, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.CategoricalHinge')
|
||
|
class CategoricalHinge(MeanMetricWrapper):
|
||
|
"""Computes the categorical hinge metric between `y_true` and `y_pred`.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.CategoricalHinge()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
|
||
|
>>> m.result().numpy()
|
||
|
1.4000001
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
1.2
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.CategoricalHinge()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='categorical_hinge', dtype=None):
|
||
|
super(CategoricalHinge, self).__init__(categorical_hinge, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.RootMeanSquaredError')
|
||
|
class RootMeanSquaredError(Mean):
|
||
|
"""Computes root mean squared error metric between `y_true` and `y_pred`.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.RootMeanSquaredError()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.5
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.70710677
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.RootMeanSquaredError()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='root_mean_squared_error', dtype=None):
|
||
|
super(RootMeanSquaredError, self).__init__(name, dtype=dtype)
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates root mean squared error statistics.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values.
|
||
|
y_pred: The predicted values.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
|
||
|
`Tensor` whose rank is either 0, or the same rank as `y_true`, and must
|
||
|
be broadcastable to `y_true`.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
y_true = math_ops.cast(y_true, self._dtype)
|
||
|
y_pred = math_ops.cast(y_pred, self._dtype)
|
||
|
y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
|
||
|
y_pred, y_true)
|
||
|
error_sq = math_ops.squared_difference(y_pred, y_true)
|
||
|
return super(RootMeanSquaredError, self).update_state(
|
||
|
error_sq, sample_weight=sample_weight)
|
||
|
|
||
|
def result(self):
|
||
|
return math_ops.sqrt(math_ops.div_no_nan(self.total, self.count))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.LogCoshError')
|
||
|
class LogCoshError(MeanMetricWrapper):
|
||
|
"""Computes the logarithm of the hyperbolic cosine of the prediction error.
|
||
|
|
||
|
`logcosh = log((exp(x) + exp(-x))/2)`, where x is the error (y_pred - y_true)
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.LogCoshError()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.10844523
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.21689045
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.LogCoshError()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='logcosh', dtype=None):
|
||
|
super(LogCoshError, self).__init__(logcosh, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.Poisson')
|
||
|
class Poisson(MeanMetricWrapper):
|
||
|
"""Computes the Poisson metric between `y_true` and `y_pred`.
|
||
|
|
||
|
`metric = y_pred - y_true * log(y_pred)`
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.Poisson()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
|
||
|
>>> m.result().numpy()
|
||
|
0.49999997
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.99999994
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.Poisson()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='poisson', dtype=None):
|
||
|
super(Poisson, self).__init__(poisson, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.KLDivergence')
|
||
|
class KLDivergence(MeanMetricWrapper):
|
||
|
"""Computes Kullback-Leibler divergence metric between `y_true` and `y_pred`.
|
||
|
|
||
|
`metric = y_true * log(y_true / y_pred)`
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.KLDivergence()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
|
||
|
>>> m.result().numpy()
|
||
|
0.45814306
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.9162892
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.KLDivergence()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='kullback_leibler_divergence', dtype=None):
|
||
|
super(KLDivergence, self).__init__(
|
||
|
kullback_leibler_divergence, name, dtype=dtype)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.MeanIoU')
|
||
|
class MeanIoU(Metric):
|
||
|
"""Computes the mean Intersection-Over-Union metric.
|
||
|
|
||
|
Mean Intersection-Over-Union is a common evaluation metric for semantic image
|
||
|
segmentation, which first computes the IOU for each semantic class and then
|
||
|
computes the average over classes. IOU is defined as follows:
|
||
|
IOU = true_positive / (true_positive + false_positive + false_negative).
|
||
|
The predictions are accumulated in a confusion matrix, weighted by
|
||
|
`sample_weight` and the metric is then calculated from it.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
||
|
Args:
|
||
|
num_classes: The possible number of labels the prediction task can have.
|
||
|
This value must be provided, since a confusion matrix of dimension =
|
||
|
[num_classes, num_classes] will be allocated.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> # cm = [[1, 1],
|
||
|
>>> # [1, 1]]
|
||
|
>>> # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]
|
||
|
>>> # iou = true_positives / (sum_row + sum_col - true_positives))
|
||
|
>>> # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33
|
||
|
>>> m = tf.keras.metrics.MeanIoU(num_classes=2)
|
||
|
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
|
||
|
>>> m.result().numpy()
|
||
|
0.33333334
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1],
|
||
|
... sample_weight=[0.3, 0.3, 0.3, 0.1])
|
||
|
>>> m.result().numpy()
|
||
|
0.23809525
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.MeanIoU(num_classes=2)])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, num_classes, name=None, dtype=None):
|
||
|
super(MeanIoU, self).__init__(name=name, dtype=dtype)
|
||
|
self.num_classes = num_classes
|
||
|
|
||
|
# Variable to accumulate the predictions in the confusion matrix.
|
||
|
self.total_cm = self.add_weight(
|
||
|
'total_confusion_matrix',
|
||
|
shape=(num_classes, num_classes),
|
||
|
initializer=init_ops.zeros_initializer)
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
"""Accumulates the confusion matrix statistics.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values.
|
||
|
y_pred: The predicted values.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
|
||
|
`Tensor` whose rank is either 0, or the same rank as `y_true`, and must
|
||
|
be broadcastable to `y_true`.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
|
||
|
y_true = math_ops.cast(y_true, self._dtype)
|
||
|
y_pred = math_ops.cast(y_pred, self._dtype)
|
||
|
|
||
|
# Flatten the input if its rank > 1.
|
||
|
if y_pred.shape.ndims > 1:
|
||
|
y_pred = array_ops.reshape(y_pred, [-1])
|
||
|
|
||
|
if y_true.shape.ndims > 1:
|
||
|
y_true = array_ops.reshape(y_true, [-1])
|
||
|
|
||
|
if sample_weight is not None:
|
||
|
sample_weight = math_ops.cast(sample_weight, self._dtype)
|
||
|
if sample_weight.shape.ndims > 1:
|
||
|
sample_weight = array_ops.reshape(sample_weight, [-1])
|
||
|
|
||
|
# Accumulate the prediction to current confusion matrix.
|
||
|
current_cm = confusion_matrix.confusion_matrix(
|
||
|
y_true,
|
||
|
y_pred,
|
||
|
self.num_classes,
|
||
|
weights=sample_weight,
|
||
|
dtype=self._dtype)
|
||
|
return self.total_cm.assign_add(current_cm)
|
||
|
|
||
|
def result(self):
|
||
|
"""Compute the mean intersection-over-union via the confusion matrix."""
|
||
|
sum_over_row = math_ops.cast(
|
||
|
math_ops.reduce_sum(self.total_cm, axis=0), dtype=self._dtype)
|
||
|
sum_over_col = math_ops.cast(
|
||
|
math_ops.reduce_sum(self.total_cm, axis=1), dtype=self._dtype)
|
||
|
true_positives = math_ops.cast(
|
||
|
array_ops.tensor_diag_part(self.total_cm), dtype=self._dtype)
|
||
|
|
||
|
# sum_over_row + sum_over_col =
|
||
|
# 2 * true_positives + false_positives + false_negatives.
|
||
|
denominator = sum_over_row + sum_over_col - true_positives
|
||
|
|
||
|
# The mean is only computed over classes that appear in the
|
||
|
# label or prediction tensor. If the denominator is 0, we need to
|
||
|
# ignore the class.
|
||
|
num_valid_entries = math_ops.reduce_sum(
|
||
|
math_ops.cast(math_ops.not_equal(denominator, 0), dtype=self._dtype))
|
||
|
|
||
|
iou = math_ops.div_no_nan(true_positives, denominator)
|
||
|
|
||
|
return math_ops.div_no_nan(
|
||
|
math_ops.reduce_sum(iou, name='mean_iou'), num_valid_entries)
|
||
|
|
||
|
def reset_state(self):
|
||
|
backend.set_value(
|
||
|
self.total_cm, np.zeros((self.num_classes, self.num_classes)))
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'num_classes': self.num_classes}
|
||
|
base_config = super(MeanIoU, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.MeanTensor')
|
||
|
class MeanTensor(Metric):
|
||
|
"""Computes the element-wise (weighted) mean of the given tensors.
|
||
|
|
||
|
`MeanTensor` returns a tensor with the same shape of the input tensors. The
|
||
|
mean value is updated by keeping local variables `total` and `count`. The
|
||
|
`total` tracks the sum of the weighted values, and `count` stores the sum of
|
||
|
the weighted counts.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
shape: (Optional) A list of integers, a tuple of integers, or a 1-D Tensor
|
||
|
of type int32. If not specified, the shape is inferred from the values at
|
||
|
the first call of update_state.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.MeanTensor()
|
||
|
>>> m.update_state([0, 1, 2, 3])
|
||
|
>>> m.update_state([4, 5, 6, 7])
|
||
|
>>> m.result().numpy()
|
||
|
array([2., 3., 4., 5.], dtype=float32)
|
||
|
|
||
|
>>> m.update_state([12, 10, 8, 6], sample_weight= [0, 0.2, 0.5, 1])
|
||
|
>>> m.result().numpy()
|
||
|
array([2. , 3.6363635, 4.8 , 5.3333335], dtype=float32)
|
||
|
|
||
|
>>> m = tf.keras.metrics.MeanTensor(dtype=tf.float64, shape=(1, 4))
|
||
|
>>> m.result().numpy()
|
||
|
array([[0., 0., 0., 0.]])
|
||
|
>>> m.update_state([[0, 1, 2, 3]])
|
||
|
>>> m.update_state([[4, 5, 6, 7]])
|
||
|
>>> m.result().numpy()
|
||
|
array([[2., 3., 4., 5.]])
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='mean_tensor', dtype=None, shape=None):
|
||
|
super(MeanTensor, self).__init__(name=name, dtype=dtype)
|
||
|
self._shape = None
|
||
|
self._total = None
|
||
|
self._count = None
|
||
|
self._built = False
|
||
|
if shape is not None:
|
||
|
self._build(shape)
|
||
|
|
||
|
def _build(self, shape):
|
||
|
self._shape = tensor_shape.TensorShape(shape)
|
||
|
self._build_input_shape = self._shape
|
||
|
# Create new state variables
|
||
|
self._total = self.add_weight(
|
||
|
'total', shape=shape, initializer=init_ops.zeros_initializer)
|
||
|
self._count = self.add_weight(
|
||
|
'count', shape=shape, initializer=init_ops.zeros_initializer)
|
||
|
with ops.init_scope():
|
||
|
if not context.executing_eagerly():
|
||
|
backend._initialize_variables(backend._get_session()) # pylint: disable=protected-access
|
||
|
self._built = True
|
||
|
|
||
|
@property
|
||
|
def total(self):
|
||
|
return self._total if self._built else None
|
||
|
|
||
|
@property
|
||
|
def count(self):
|
||
|
return self._count if self._built else None
|
||
|
|
||
|
def update_state(self, values, sample_weight=None):
|
||
|
"""Accumulates statistics for computing the element-wise mean.
|
||
|
|
||
|
Args:
|
||
|
values: Per-example value.
|
||
|
sample_weight: Optional weighting of each example. Defaults to 1.
|
||
|
|
||
|
Returns:
|
||
|
Update op.
|
||
|
"""
|
||
|
values = math_ops.cast(values, self._dtype)
|
||
|
if not self._built:
|
||
|
self._build(values.shape)
|
||
|
elif values.shape != self._shape:
|
||
|
raise ValueError('MeanTensor input values must always have the same '
|
||
|
'shape. Expected shape (set during the first call): {}. '
|
||
|
'Got: {}'.format(self._shape, values.shape))
|
||
|
|
||
|
num_values = array_ops.ones_like(values)
|
||
|
if sample_weight is not None:
|
||
|
sample_weight = math_ops.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)
|
||
|
try:
|
||
|
# Broadcast weights if possible.
|
||
|
sample_weight = weights_broadcast_ops.broadcast_weights(
|
||
|
sample_weight, values)
|
||
|
except ValueError:
|
||
|
# Reduce values to same ndim as weight array
|
||
|
ndim = backend.ndim(values)
|
||
|
weight_ndim = backend.ndim(sample_weight)
|
||
|
values = math_ops.reduce_mean(
|
||
|
values, axis=list(range(weight_ndim, ndim)))
|
||
|
|
||
|
num_values = math_ops.multiply(num_values, sample_weight)
|
||
|
values = math_ops.multiply(values, sample_weight)
|
||
|
|
||
|
update_total_op = self._total.assign_add(values)
|
||
|
with ops.control_dependencies([update_total_op]):
|
||
|
return self._count.assign_add(num_values)
|
||
|
|
||
|
def result(self):
|
||
|
if not self._built:
|
||
|
raise ValueError(
|
||
|
'MeanTensor does not have any result yet. Please call the MeanTensor '
|
||
|
'instance or use `.update_state(value)` before retrieving the result.'
|
||
|
)
|
||
|
return math_ops.div_no_nan(self.total, self.count)
|
||
|
|
||
|
def reset_state(self):
|
||
|
if self._built:
|
||
|
backend.batch_set_value(
|
||
|
[(v, np.zeros(self._shape.as_list())) for v in self.variables])
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.BinaryCrossentropy')
|
||
|
class BinaryCrossentropy(MeanMetricWrapper):
|
||
|
"""Computes the crossentropy metric between the labels and predictions.
|
||
|
|
||
|
This is the crossentropy metric class to be used when there are only two
|
||
|
label classes (0 and 1).
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
from_logits: (Optional )Whether output is expected to be a logits tensor.
|
||
|
By default, we consider that output encodes a probability distribution.
|
||
|
label_smoothing: (Optional) Float in [0, 1]. When > 0, label values are
|
||
|
smoothed, meaning the confidence on label values are relaxed.
|
||
|
e.g. `label_smoothing=0.2` means that we will use a value of `0.1` for
|
||
|
label `0` and `0.9` for label `1`".
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> m = tf.keras.metrics.BinaryCrossentropy()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
|
||
|
>>> m.result().numpy()
|
||
|
0.81492424
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
|
||
|
... sample_weight=[1, 0])
|
||
|
>>> m.result().numpy()
|
||
|
0.9162905
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.BinaryCrossentropy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
name='binary_crossentropy',
|
||
|
dtype=None,
|
||
|
from_logits=False,
|
||
|
label_smoothing=0):
|
||
|
super(BinaryCrossentropy, self).__init__(
|
||
|
binary_crossentropy,
|
||
|
name,
|
||
|
dtype=dtype,
|
||
|
from_logits=from_logits,
|
||
|
label_smoothing=label_smoothing)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.CategoricalCrossentropy')
|
||
|
class CategoricalCrossentropy(MeanMetricWrapper):
|
||
|
"""Computes the crossentropy metric between the labels and predictions.
|
||
|
|
||
|
This is the crossentropy metric class to be used when there are multiple
|
||
|
label classes (2 or more). Here we assume that labels are given as a `one_hot`
|
||
|
representation. eg., When labels values are [2, 0, 1],
|
||
|
`y_true` = [[0, 0, 1], [1, 0, 0], [0, 1, 0]].
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
from_logits: (Optional) Whether output is expected to be a logits tensor.
|
||
|
By default, we consider that output encodes a probability distribution.
|
||
|
label_smoothing: (Optional) Float in [0, 1]. When > 0, label values are
|
||
|
smoothed, meaning the confidence on label values are relaxed. e.g.
|
||
|
`label_smoothing=0.2` means that we will use a value of `0.1` for label
|
||
|
`0` and `0.9` for label `1`"
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> # EPSILON = 1e-7, y = y_true, y` = y_pred
|
||
|
>>> # y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
|
||
|
>>> # y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
|
||
|
>>> # xent = -sum(y * log(y'), axis = -1)
|
||
|
>>> # = -((log 0.95), (log 0.1))
|
||
|
>>> # = [0.051, 2.302]
|
||
|
>>> # Reduced xent = (0.051 + 2.302) / 2
|
||
|
>>> m = tf.keras.metrics.CategoricalCrossentropy()
|
||
|
>>> m.update_state([[0, 1, 0], [0, 0, 1]],
|
||
|
... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
|
||
|
>>> m.result().numpy()
|
||
|
1.1769392
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([[0, 1, 0], [0, 0, 1]],
|
||
|
... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
|
||
|
... sample_weight=tf.constant([0.3, 0.7]))
|
||
|
>>> m.result().numpy()
|
||
|
1.6271976
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.CategoricalCrossentropy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
name='categorical_crossentropy',
|
||
|
dtype=None,
|
||
|
from_logits=False,
|
||
|
label_smoothing=0):
|
||
|
super(CategoricalCrossentropy, self).__init__(
|
||
|
categorical_crossentropy,
|
||
|
name,
|
||
|
dtype=dtype,
|
||
|
from_logits=from_logits,
|
||
|
label_smoothing=label_smoothing)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.SparseCategoricalCrossentropy')
|
||
|
class SparseCategoricalCrossentropy(MeanMetricWrapper):
|
||
|
"""Computes the crossentropy metric between the labels and predictions.
|
||
|
|
||
|
Use this crossentropy metric when there are two or more label classes.
|
||
|
We expect labels to be provided as integers. If you want to provide labels
|
||
|
using `one-hot` representation, please use `CategoricalCrossentropy` metric.
|
||
|
There should be `# classes` floating point values per feature for `y_pred`
|
||
|
and a single floating point value per feature for `y_true`.
|
||
|
|
||
|
In the snippet below, there is a single floating point value per example for
|
||
|
`y_true` and `# classes` floating pointing values per example for `y_pred`.
|
||
|
The shape of `y_true` is `[batch_size]` and the shape of `y_pred` is
|
||
|
`[batch_size, num_classes]`.
|
||
|
|
||
|
Args:
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
from_logits: (Optional) Whether output is expected to be a logits tensor.
|
||
|
By default, we consider that output encodes a probability distribution.
|
||
|
axis: (Optional) Defaults to -1. The dimension along which the metric is
|
||
|
computed.
|
||
|
|
||
|
Standalone usage:
|
||
|
|
||
|
>>> # y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
|
||
|
>>> # logits = log(y_pred)
|
||
|
>>> # softmax = exp(logits) / sum(exp(logits), axis=-1)
|
||
|
>>> # softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
|
||
|
>>> # xent = -sum(y * log(softmax), 1)
|
||
|
>>> # log(softmax) = [[-2.9957, -0.0513, -16.1181],
|
||
|
>>> # [-2.3026, -0.2231, -2.3026]]
|
||
|
>>> # y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]
|
||
|
>>> # xent = [0.0513, 2.3026]
|
||
|
>>> # Reduced xent = (0.0513 + 2.3026) / 2
|
||
|
>>> m = tf.keras.metrics.SparseCategoricalCrossentropy()
|
||
|
>>> m.update_state([1, 2],
|
||
|
... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
|
||
|
>>> m.result().numpy()
|
||
|
1.1769392
|
||
|
|
||
|
>>> m.reset_state()
|
||
|
>>> m.update_state([1, 2],
|
||
|
... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
|
||
|
... sample_weight=tf.constant([0.3, 0.7]))
|
||
|
>>> m.result().numpy()
|
||
|
1.6271976
|
||
|
|
||
|
Usage with `compile()` API:
|
||
|
|
||
|
```python
|
||
|
model.compile(
|
||
|
optimizer='sgd',
|
||
|
loss='mse',
|
||
|
metrics=[tf.keras.metrics.SparseCategoricalCrossentropy()])
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
name='sparse_categorical_crossentropy',
|
||
|
dtype=None,
|
||
|
from_logits=False,
|
||
|
axis=-1):
|
||
|
super(SparseCategoricalCrossentropy, self).__init__(
|
||
|
sparse_categorical_crossentropy,
|
||
|
name,
|
||
|
dtype=dtype,
|
||
|
from_logits=from_logits,
|
||
|
axis=axis)
|
||
|
|
||
|
|
||
|
class SumOverBatchSize(Reduce):
|
||
|
"""Computes the weighted sum over batch size of the given values.
|
||
|
|
||
|
For example, if values is [1, 3, 5, 7] then the metric value is 4.
|
||
|
If the weights were specified as [1, 1, 0, 0] then the value would be 1.
|
||
|
|
||
|
This metric creates two variables, `total` and `count` that are used to
|
||
|
compute the average of `values`. This average is ultimately returned as sum
|
||
|
over batch size which is an idempotent operation that simply divides `total`
|
||
|
by `count`.
|
||
|
|
||
|
If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0
|
||
|
to mask values.
|
||
|
"""
|
||
|
|
||
|
def __init__(self, name='sum_over_batch_size', dtype=None):
|
||
|
super(SumOverBatchSize, self).__init__(
|
||
|
reduction=metrics_utils.Reduction.SUM_OVER_BATCH_SIZE,
|
||
|
name=name,
|
||
|
dtype=dtype)
|
||
|
|
||
|
|
||
|
class SumOverBatchSizeMetricWrapper(SumOverBatchSize):
|
||
|
"""Wraps a function with the `SumOverBatchSizeMetricWrapper` metric."""
|
||
|
|
||
|
def __init__(self, fn, name=None, dtype=None, **kwargs):
|
||
|
"""Creates a `SumOverBatchSizeMetricWrapper` instance.
|
||
|
|
||
|
Args:
|
||
|
fn: The metric function to wrap, with signature `fn(y_true, y_pred,
|
||
|
**kwargs)`.
|
||
|
name: (Optional) string name of the metric instance.
|
||
|
dtype: (Optional) data type of the metric result.
|
||
|
**kwargs: The keyword arguments that are passed on to `fn`.
|
||
|
"""
|
||
|
super(SumOverBatchSizeMetricWrapper, self).__init__(name=name, dtype=dtype)
|
||
|
self._fn = fn
|
||
|
self._fn_kwargs = kwargs
|
||
|
|
||
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
y_true = math_ops.cast(y_true, self._dtype)
|
||
|
y_pred = math_ops.cast(y_pred, self._dtype)
|
||
|
y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
|
||
|
y_pred, y_true)
|
||
|
|
||
|
ag_fn = autograph.tf_convert(self._fn, ag_ctx.control_status_ctx())
|
||
|
matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
|
||
|
return super(SumOverBatchSizeMetricWrapper, self).update_state(
|
||
|
matches, sample_weight=sample_weight)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {}
|
||
|
for k, v in self._fn_kwargs.items():
|
||
|
config[k] = backend.eval(v) if is_tensor_or_variable(v) else v
|
||
|
base_config = super(SumOverBatchSizeMetricWrapper, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
def accuracy(y_true, y_pred):
|
||
|
[y_pred, y_true], _ = \
|
||
|
metrics_utils.ragged_assert_compatible_and_get_flat_values(
|
||
|
[y_pred, y_true])
|
||
|
y_true.shape.assert_is_compatible_with(y_pred.shape)
|
||
|
if y_true.dtype != y_pred.dtype:
|
||
|
y_pred = math_ops.cast(y_pred, y_true.dtype)
|
||
|
return math_ops.cast(math_ops.equal(y_true, y_pred), backend.floatx())
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.binary_accuracy')
|
||
|
@dispatch.add_dispatch_support
|
||
|
def binary_accuracy(y_true, y_pred, threshold=0.5):
|
||
|
"""Calculates how often predictions match binary labels.
|
||
|
|
||
|
Standalone usage:
|
||
|
>>> y_true = [[1], [1], [0], [0]]
|
||
|
>>> y_pred = [[1], [1], [0], [0]]
|
||
|
>>> m = tf.keras.metrics.binary_accuracy(y_true, y_pred)
|
||
|
>>> assert m.shape == (4,)
|
||
|
>>> m.numpy()
|
||
|
array([1., 1., 1., 1.], dtype=float32)
|
||
|
|
||
|
Args:
|
||
|
y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
|
||
|
y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
|
||
|
threshold: (Optional) Float representing the threshold for deciding whether
|
||
|
prediction values are 1 or 0.
|
||
|
|
||
|
Returns:
|
||
|
Binary accuracy values. shape = `[batch_size, d0, .. dN-1]`
|
||
|
"""
|
||
|
y_pred = ops.convert_to_tensor_v2_with_dispatch(y_pred)
|
||
|
threshold = math_ops.cast(threshold, y_pred.dtype)
|
||
|
y_pred = math_ops.cast(y_pred > threshold, y_pred.dtype)
|
||
|
return backend.mean(math_ops.equal(y_true, y_pred), axis=-1)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.categorical_accuracy')
|
||
|
@dispatch.add_dispatch_support
|
||
|
def categorical_accuracy(y_true, y_pred):
|
||
|
"""Calculates how often predictions match one-hot labels.
|
||
|
|
||
|
Standalone usage:
|
||
|
>>> y_true = [[0, 0, 1], [0, 1, 0]]
|
||
|
>>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
|
||
|
>>> m = tf.keras.metrics.categorical_accuracy(y_true, y_pred)
|
||
|
>>> assert m.shape == (2,)
|
||
|
>>> m.numpy()
|
||
|
array([0., 1.], dtype=float32)
|
||
|
|
||
|
You can provide logits of classes as `y_pred`, since argmax of
|
||
|
logits and probabilities are same.
|
||
|
|
||
|
Args:
|
||
|
y_true: One-hot ground truth values.
|
||
|
y_pred: The prediction values.
|
||
|
|
||
|
Returns:
|
||
|
Categorical accuracy values.
|
||
|
"""
|
||
|
return math_ops.cast(
|
||
|
math_ops.equal(
|
||
|
math_ops.argmax(y_true, axis=-1), math_ops.argmax(y_pred, axis=-1)),
|
||
|
backend.floatx())
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.sparse_categorical_accuracy')
|
||
|
@dispatch.add_dispatch_support
|
||
|
def sparse_categorical_accuracy(y_true, y_pred):
|
||
|
"""Calculates how often predictions match integer labels.
|
||
|
|
||
|
Standalone usage:
|
||
|
>>> y_true = [2, 1]
|
||
|
>>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
|
||
|
>>> m = tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
|
||
|
>>> assert m.shape == (2,)
|
||
|
>>> m.numpy()
|
||
|
array([0., 1.], dtype=float32)
|
||
|
|
||
|
You can provide logits of classes as `y_pred`, since argmax of
|
||
|
logits and probabilities are same.
|
||
|
|
||
|
Args:
|
||
|
y_true: Integer ground truth values.
|
||
|
y_pred: The prediction values.
|
||
|
|
||
|
Returns:
|
||
|
Sparse categorical accuracy values.
|
||
|
"""
|
||
|
y_pred = ops.convert_to_tensor_v2_with_dispatch(y_pred)
|
||
|
y_true = ops.convert_to_tensor_v2_with_dispatch(y_true)
|
||
|
y_pred_rank = y_pred.shape.ndims
|
||
|
y_true_rank = y_true.shape.ndims
|
||
|
# If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
|
||
|
if (y_true_rank is not None) and (y_pred_rank is not None) and (len(
|
||
|
backend.int_shape(y_true)) == len(backend.int_shape(y_pred))):
|
||
|
y_true = array_ops.squeeze(y_true, [-1])
|
||
|
y_pred = math_ops.argmax(y_pred, axis=-1)
|
||
|
|
||
|
# If the predicted output and actual output types don't match, force cast them
|
||
|
# to match.
|
||
|
if backend.dtype(y_pred) != backend.dtype(y_true):
|
||
|
y_pred = math_ops.cast(y_pred, backend.dtype(y_true))
|
||
|
|
||
|
return math_ops.cast(math_ops.equal(y_true, y_pred), backend.floatx())
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.top_k_categorical_accuracy')
|
||
|
@dispatch.add_dispatch_support
|
||
|
def top_k_categorical_accuracy(y_true, y_pred, k=5):
|
||
|
"""Computes how often targets are in the top `K` predictions.
|
||
|
|
||
|
Standalone usage:
|
||
|
>>> y_true = [[0, 0, 1], [0, 1, 0]]
|
||
|
>>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
|
||
|
>>> m = tf.keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=3)
|
||
|
>>> assert m.shape == (2,)
|
||
|
>>> m.numpy()
|
||
|
array([1., 1.], dtype=float32)
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values.
|
||
|
y_pred: The prediction values.
|
||
|
k: (Optional) Number of top elements to look at for computing accuracy.
|
||
|
Defaults to 5.
|
||
|
|
||
|
Returns:
|
||
|
Top K categorical accuracy value.
|
||
|
"""
|
||
|
return math_ops.cast(
|
||
|
nn.in_top_k(
|
||
|
y_pred, math_ops.argmax(y_true, axis=-1), k), backend.floatx())
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.sparse_top_k_categorical_accuracy')
|
||
|
@dispatch.add_dispatch_support
|
||
|
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
|
||
|
"""Computes how often integer targets are in the top `K` predictions.
|
||
|
|
||
|
Standalone usage:
|
||
|
>>> y_true = [2, 1]
|
||
|
>>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
|
||
|
>>> m = tf.keras.metrics.sparse_top_k_categorical_accuracy(
|
||
|
... y_true, y_pred, k=3)
|
||
|
>>> assert m.shape == (2,)
|
||
|
>>> m.numpy()
|
||
|
array([1., 1.], dtype=float32)
|
||
|
|
||
|
Args:
|
||
|
y_true: tensor of true targets.
|
||
|
y_pred: tensor of predicted targets.
|
||
|
k: (Optional) Number of top elements to look at for computing accuracy.
|
||
|
Defaults to 5.
|
||
|
|
||
|
Returns:
|
||
|
Sparse top K categorical accuracy value.
|
||
|
"""
|
||
|
y_pred_rank = ops.convert_to_tensor_v2_with_dispatch(y_pred).shape.ndims
|
||
|
y_true_rank = ops.convert_to_tensor_v2_with_dispatch(y_true).shape.ndims
|
||
|
# Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
|
||
|
if (y_true_rank is not None) and (y_pred_rank is not None):
|
||
|
if y_pred_rank > 2:
|
||
|
y_pred = array_ops.reshape(y_pred, [-1, y_pred.shape[-1]])
|
||
|
if y_true_rank > 1:
|
||
|
y_true = array_ops.reshape(y_true, [-1])
|
||
|
|
||
|
return math_ops.cast(
|
||
|
nn.in_top_k(y_pred, math_ops.cast(y_true, 'int32'), k), backend.floatx())
|
||
|
|
||
|
|
||
|
def cosine_proximity(y_true, y_pred, axis=-1):
|
||
|
"""Computes the cosine similarity between labels and predictions.
|
||
|
|
||
|
Args:
|
||
|
y_true: The ground truth values.
|
||
|
y_pred: The prediction values.
|
||
|
axis: (Optional) Defaults to -1. The dimension along which the cosine
|
||
|
similarity is computed.
|
||
|
|
||
|
Returns:
|
||
|
Cosine similarity value.
|
||
|
"""
|
||
|
y_true = nn.l2_normalize(y_true, axis=axis)
|
||
|
y_pred = nn.l2_normalize(y_pred, axis=axis)
|
||
|
return math_ops.reduce_sum(y_true * y_pred, axis=axis)
|
||
|
|
||
|
# Aliases
|
||
|
|
||
|
acc = ACC = accuracy
|
||
|
bce = BCE = binary_crossentropy
|
||
|
mse = MSE = mean_squared_error
|
||
|
mae = MAE = mean_absolute_error
|
||
|
mape = MAPE = mean_absolute_percentage_error
|
||
|
msle = MSLE = mean_squared_logarithmic_error
|
||
|
cosine_similarity = cosine_proximity
|
||
|
log_cosh = logcosh
|
||
|
|
||
|
|
||
|
def clone_metric(metric):
|
||
|
"""Returns a clone of the metric if stateful, otherwise returns it as is."""
|
||
|
if isinstance(metric, Metric):
|
||
|
with ops.init_scope():
|
||
|
return metric.__class__.from_config(metric.get_config())
|
||
|
return metric
|
||
|
|
||
|
|
||
|
def clone_metrics(metrics):
|
||
|
"""Clones the given metric list/dict."""
|
||
|
return nest.map_structure(clone_metric, metrics)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.serialize')
|
||
|
def serialize(metric):
|
||
|
"""Serializes metric function or `Metric` instance.
|
||
|
|
||
|
Args:
|
||
|
metric: A Keras `Metric` instance or a metric function.
|
||
|
|
||
|
Returns:
|
||
|
Metric configuration dictionary.
|
||
|
"""
|
||
|
return serialize_keras_object(metric)
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.deserialize')
|
||
|
def deserialize(config, custom_objects=None):
|
||
|
"""Deserializes a serialized metric class/function instance.
|
||
|
|
||
|
Args:
|
||
|
config: Metric configuration.
|
||
|
custom_objects: Optional dictionary mapping names (strings) to custom
|
||
|
objects (classes and functions) to be considered during deserialization.
|
||
|
|
||
|
Returns:
|
||
|
A Keras `Metric` instance or a metric function.
|
||
|
"""
|
||
|
return deserialize_keras_object(
|
||
|
config,
|
||
|
module_objects=globals(),
|
||
|
custom_objects=custom_objects,
|
||
|
printable_module_name='metric function')
|
||
|
|
||
|
|
||
|
@keras_export('keras.metrics.get')
|
||
|
def get(identifier):
|
||
|
"""Retrieves a Keras metric as a `function`/`Metric` class instance.
|
||
|
|
||
|
The `identifier` may be the string name of a metric function or class.
|
||
|
|
||
|
>>> metric = tf.keras.metrics.get("categorical_crossentropy")
|
||
|
>>> type(metric)
|
||
|
<class 'function'>
|
||
|
>>> metric = tf.keras.metrics.get("CategoricalCrossentropy")
|
||
|
>>> type(metric)
|
||
|
<class '...keras.metrics.CategoricalCrossentropy'>
|
||
|
|
||
|
You can also specify `config` of the metric to this function by passing dict
|
||
|
containing `class_name` and `config` as an identifier. Also note that the
|
||
|
`class_name` must map to a `Metric` class
|
||
|
|
||
|
>>> identifier = {"class_name": "CategoricalCrossentropy",
|
||
|
... "config": {"from_logits": True}}
|
||
|
>>> metric = tf.keras.metrics.get(identifier)
|
||
|
>>> type(metric)
|
||
|
<class '...keras.metrics.CategoricalCrossentropy'>
|
||
|
|
||
|
Args:
|
||
|
identifier: A metric identifier. One of None or string name of a metric
|
||
|
function/class or metric configuration dictionary or a metric function or
|
||
|
a metric class instance
|
||
|
|
||
|
Returns:
|
||
|
A Keras metric as a `function`/ `Metric` class instance.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: If `identifier` cannot be interpreted.
|
||
|
"""
|
||
|
if isinstance(identifier, dict):
|
||
|
return deserialize(identifier)
|
||
|
elif isinstance(identifier, str):
|
||
|
return deserialize(str(identifier))
|
||
|
elif callable(identifier):
|
||
|
return identifier
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
'Could not interpret metric function identifier: {}'.format(identifier))
|
||
|
|
||
|
|
||
|
def is_built_in(cls):
|
||
|
return cls.__module__ == Metric.__module__
|