115 lines
4 KiB
Python
115 lines
4 KiB
Python
|
# 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)
|