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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=unused-import
"""Built-in metrics.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.losses import binary_crossentropy
from tensorflow.python.keras.losses import categorical_crossentropy
from tensorflow.python.keras.losses import cosine_proximity
from tensorflow.python.keras.losses import hinge
from tensorflow.python.keras.losses import kullback_leibler_divergence
from tensorflow.python.keras.losses import logcosh
from tensorflow.python.keras.losses import mean_absolute_error
from tensorflow.python.keras.losses import mean_absolute_percentage_error
from tensorflow.python.keras.losses import mean_squared_error
from tensorflow.python.keras.losses import mean_squared_logarithmic_error
from tensorflow.python.keras.losses import poisson
from tensorflow.python.keras.losses import sparse_categorical_crossentropy
from tensorflow.python.keras.losses import squared_hinge
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.util.tf_export import tf_export
@tf_export('keras.metrics.binary_accuracy')
def binary_accuracy(y_true, y_pred):
return K.mean(math_ops.equal(y_true, math_ops.round(y_pred)), axis=-1)
@tf_export('keras.metrics.categorical_accuracy')
def categorical_accuracy(y_true, y_pred):
return math_ops.cast(
math_ops.equal(
math_ops.argmax(y_true, axis=-1), math_ops.argmax(y_pred, axis=-1)),
K.floatx())
def sparse_categorical_accuracy(y_true, y_pred):
return math_ops.cast(
math_ops.equal(
math_ops.reduce_max(y_true, axis=-1),
math_ops.cast(math_ops.argmax(y_pred, axis=-1), K.floatx())),
K.floatx())
@tf_export('keras.metrics.top_k_categorical_accuracy')
def top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(
nn.in_top_k(y_pred, math_ops.argmax(y_true, axis=-1), k), axis=-1)
@tf_export('keras.metrics.sparse_top_k_categorical_accuracy')
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(
nn.in_top_k(y_pred,
math_ops.cast(math_ops.reduce_max(y_true, axis=-1), 'int32'),
k),
axis=-1)
# Aliases
mse = MSE = mean_squared_error
mae = MAE = mean_absolute_error
mape = MAPE = mean_absolute_percentage_error
msle = MSLE = mean_squared_logarithmic_error
cosine = cosine_proximity
@tf_export('keras.metrics.serialize')
def serialize(metric):
return serialize_keras_object(metric)
@tf_export('keras.metrics.deserialize')
def deserialize(config, custom_objects=None):
return deserialize_keras_object(
config,
module_objects=globals(),
custom_objects=custom_objects,
printable_module_name='metric function')
@tf_export('keras.metrics.get')
def get(identifier):
if isinstance(identifier, dict):
config = {'class_name': str(identifier), 'config': {}}
return deserialize(config)
elif isinstance(identifier, six.string_types):
return deserialize(str(identifier))
elif callable(identifier):
return identifier
else:
raise ValueError('Could not interpret '
'metric function identifier: %s' % identifier)
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