200 lines
6.6 KiB
Python
200 lines
6.6 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=unused-import
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"""Built-in loss functions.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import six
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from tensorflow.python.keras import backend as K
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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.ops import math_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('keras.metrics.mean_squared_error',
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'keras.metrics.mse',
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'keras.metrics.MSE',
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'keras.losses.mean_squared_error',
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'keras.losses.mse',
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'keras.losses.MSE')
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def mean_squared_error(y_true, y_pred):
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return K.mean(math_ops.square(y_pred - y_true), axis=-1)
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@tf_export('keras.metrics.mean_absolute_error',
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'keras.metrics.mae',
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'keras.metrics.MAE',
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'keras.losses.mean_absolute_error',
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'keras.losses.mae',
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'keras.losses.MAE')
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def mean_absolute_error(y_true, y_pred):
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return K.mean(math_ops.abs(y_pred - y_true), axis=-1)
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@tf_export('keras.metrics.mean_absolute_percentage_error',
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'keras.metrics.mape',
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'keras.metrics.MAPE',
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'keras.losses.mean_absolute_percentage_error',
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'keras.losses.mape',
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'keras.losses.MAPE')
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def mean_absolute_percentage_error(y_true, y_pred):
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diff = math_ops.abs(
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(y_true - y_pred) / K.clip(math_ops.abs(y_true), K.epsilon(), None))
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return 100. * K.mean(diff, axis=-1)
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@tf_export('keras.metrics.mean_squared_logarithmic_error',
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'keras.metrics.msle',
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'keras.metrics.MSLE',
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'keras.losses.mean_squared_logarithmic_error',
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'keras.losses.msle',
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'keras.losses.MSLE')
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def mean_squared_logarithmic_error(y_true, y_pred):
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first_log = math_ops.log(K.clip(y_pred, K.epsilon(), None) + 1.)
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second_log = math_ops.log(K.clip(y_true, K.epsilon(), None) + 1.)
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return K.mean(math_ops.square(first_log - second_log), axis=-1)
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@tf_export('keras.metrics.squared_hinge', 'keras.losses.squared_hinge')
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def squared_hinge(y_true, y_pred):
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return K.mean(
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math_ops.square(math_ops.maximum(1. - y_true * y_pred, 0.)), axis=-1)
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@tf_export('keras.metrics.hinge', 'keras.losses.hinge')
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def hinge(y_true, y_pred):
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return K.mean(math_ops.maximum(1. - y_true * y_pred, 0.), axis=-1)
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@tf_export('keras.losses.categorical_hinge')
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def categorical_hinge(y_true, y_pred):
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pos = math_ops.reduce_sum(y_true * y_pred, axis=-1)
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neg = math_ops.reduce_max((1. - y_true) * y_pred, axis=-1)
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return math_ops.maximum(0., neg - pos + 1.)
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@tf_export('keras.losses.logcosh')
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def logcosh(y_true, y_pred):
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"""Logarithm of the hyperbolic cosine of the prediction error.
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`log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and
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to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly
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like the mean squared error, but will not be so strongly affected by the
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occasional wildly incorrect prediction.
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Arguments:
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y_true: tensor of true targets.
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y_pred: tensor of predicted targets.
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Returns:
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Tensor with one scalar loss entry per sample.
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"""
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def _logcosh(x):
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return x + nn.softplus(-2. * x) - math_ops.log(2.)
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return K.mean(_logcosh(y_pred - y_true), axis=-1)
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@tf_export('keras.metrics.categorical_crossentropy',
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'keras.losses.categorical_crossentropy')
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def categorical_crossentropy(y_true, y_pred):
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return K.categorical_crossentropy(y_true, y_pred)
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@tf_export('keras.metrics.sparse_categorical_crossentropy',
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'keras.losses.sparse_categorical_crossentropy')
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def sparse_categorical_crossentropy(y_true, y_pred):
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return K.sparse_categorical_crossentropy(y_true, y_pred)
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@tf_export('keras.metrics.binary_crossentropy',
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'keras.losses.binary_crossentropy')
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def binary_crossentropy(y_true, y_pred):
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return K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1)
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@tf_export('keras.metrics.kullback_leibler_divergence',
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'keras.metrics.kld',
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'keras.metrics.KLD',
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'keras.losses.kullback_leibler_divergence',
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'keras.losses.kld',
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'keras.losses.KLD')
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def kullback_leibler_divergence(y_true, y_pred):
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y_true = K.clip(y_true, K.epsilon(), 1)
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y_pred = K.clip(y_pred, K.epsilon(), 1)
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return math_ops.reduce_sum(y_true * math_ops.log(y_true / y_pred), axis=-1)
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@tf_export('keras.metrics.poisson', 'keras.losses.poisson')
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def poisson(y_true, y_pred):
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return K.mean(y_pred - y_true * math_ops.log(y_pred + K.epsilon()), axis=-1)
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@tf_export('keras.metrics.cosine_proximity',
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'keras.metrics.cosine',
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'keras.losses.cosine_proximity',
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'keras.losses.cosine')
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def cosine_proximity(y_true, y_pred):
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y_true = nn.l2_normalize(y_true, axis=-1)
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y_pred = nn.l2_normalize(y_pred, axis=-1)
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return -math_ops.reduce_sum(y_true * y_pred, axis=-1)
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# Aliases.
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mse = MSE = mean_squared_error
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mae = MAE = mean_absolute_error
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mape = MAPE = mean_absolute_percentage_error
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msle = MSLE = mean_squared_logarithmic_error
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kld = KLD = kullback_leibler_divergence
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cosine = cosine_proximity
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@tf_export('keras.losses.serialize')
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def serialize(loss):
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return serialize_keras_object(loss)
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@tf_export('keras.losses.deserialize')
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def deserialize(name, custom_objects=None):
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return deserialize_keras_object(
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name,
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module_objects=globals(),
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custom_objects=custom_objects,
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printable_module_name='loss function')
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@tf_export('keras.losses.get')
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def get(identifier):
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if identifier is None:
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return None
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if isinstance(identifier, six.string_types):
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identifier = str(identifier)
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return deserialize(identifier)
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if isinstance(identifier, dict):
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return deserialize(identifier)
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elif callable(identifier):
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return identifier
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else:
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raise ValueError('Could not interpret '
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'loss function identifier:', identifier)
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